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PyHealth: Healthcare AI Toolkit · FreedomIntelligence | DecimalAI

AI/MLby FreedomIntelligence

PyHealth: Healthcare AI Toolkit

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

Never made a case worse

SkillScore

SkillScore43/100

Test results 43

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

9%43%of test cases passed +35 pts

Without skill2 of 23

With skill10 of 23

8 fixed2 passed either way13 still failing0 broke

Measured on gemini-3.6-flash7/26/2026v1

Track record

Agent runs: 48; All time
Forks: 2

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add pyhealth
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install pyhealth

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/pyhealth/SKILL.md https://app.decimal.ai/s/pyhealth/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/pyhealth/SKILL.md

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add pyhealth

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add pyhealth

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add pyhealth

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add pyhealth

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add pyhealth

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add pyhealth

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add pyhealth

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add pyhealth

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add pyhealth

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add pyhealth

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add pyhealth

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add pyhealth

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("pyhealth")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/pyhealth/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add pyhealth

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/pyhealth/SKILL.md https://app.decimal.ai/s/pyhealth/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/pyhealth/SKILL.md

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/pyhealth/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/pyhealth.json

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 7/26/2026
Source: FreedomIntelligence/OpenClaw-Medical-Skills ↗
License: Not recorded

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

PyHealth: Healthcare AI Toolkit

Overview

PyHealth is a comprehensive Python library for healthcare AI that provides specialized tools, models, and datasets for clinical machine learning. Use this skill when developing healthcare prediction models, processing clinical data, working with medical coding systems, or deploying AI solutions in healthcare settings.

When to Use This Skill

Invoke this skill when:

Working with healthcare datasets: MIMIC-III, MIMIC-IV, eICU, OMOP, sleep EEG data, medical images
Clinical prediction tasks: Mortality prediction, hospital readmission, length of stay, drug recommendation
Medical coding: Translating between ICD-9/10, NDC, RxNorm, ATC coding systems
Processing clinical data: Sequential events, physiological signals, clinical text, medical images
Implementing healthcare models: RETAIN, SafeDrug, GAMENet, StageNet, Transformer for EHR
Evaluating clinical models: Fairness metrics, calibration, interpretability, uncertainty quantification

Core Capabilities

PyHealth operates through a modular 5-stage pipeline optimized for healthcare AI:

Data Loading: Access 10+ healthcare datasets with standardized interfaces
Task Definition: Apply 20+ predefined clinical prediction tasks or create custom tasks
Model Selection: Choose from 33+ models (baselines, deep learning, healthcare-specific)
Training: Train with automatic checkpointing, monitoring, and evaluation
Deployment: Calibrate, interpret, and validate for clinical use

Performance: 3x faster than pandas for healthcare data processing

Quick Start Workflow

from pyhealth.datasets import MIMIC4Dataset
from pyhealth.tasks import mortality_prediction_mimic4_fn
from pyhealth.datasets import split_by_patient, get_dataloader
from pyhealth.models import Transformer
from pyhealth.trainer import Trainer

# 1. Load dataset and set task
dataset = MIMIC4Dataset(root="/path/to/data")
sample_dataset = dataset.set_task(mortality_prediction_mimic4_fn)

# 2. Split data
train, val, test = split_by_patient(sample_dataset, [0.7, 0.1, 0.2])

# 3. Create data loaders
train_loader = get_dataloader(train, batch_size=64, shuffle=True)
val_loader = get_dataloader(val, batch_size=64, shuffle=False)
test_loader = get_dataloader(test, batch_size=64, shuffle=False)

# 4. Initialize and train model
model = Transformer(
    dataset=sample_dataset,
    feature_keys=["diagnoses", "medications"],
    mode="binary",
    embedding_dim=128
)

trainer = Trainer(model=model, device="cuda")
trainer.train(
    train_dataloader=train_loader,
    val_dataloader=val_loader,
    epochs=50,
    monitor="pr_auc_score"
)

# 5. Evaluate
results = trainer.evaluate(test_loader)

Detailed Documentation

This skill includes comprehensive reference documentation organized by functionality. Read specific reference files as needed:

1. Datasets and Data Structures

File: references/datasets.md

Read when:

Loading healthcare datasets (MIMIC, eICU, OMOP, sleep EEG, etc.)
Understanding Event, Patient, Visit data structures
Processing different data types (EHR, signals, images, text)
Splitting data for training/validation/testing
Working with SampleDataset for task-specific formatting

Key Topics:

Core data structures (Event, Patient, Visit)
10+ available datasets (EHR, physiological signals, imaging, text)
Data loading and iteration
Train/val/test splitting strategies
Performance optimization for large datasets

2. Medical Coding Translation

File: references/medical_coding.md

Read when:

Translating between medical coding systems
Working with diagnosis codes (ICD-9-CM, ICD-10-CM, CCS)
Processing medication codes (NDC, RxNorm, ATC)
Standardizing procedure codes (ICD-9-PROC, ICD-10-PROC)
Grouping codes into clinical categories
Handling hierarchical drug classifications

Key Topics:

InnerMap for within-system lookups
CrossMap for cross-system translation
Supported coding systems (ICD, NDC, ATC, CCS, RxNorm)
Code standardization and hierarchy traversal
Medication classification by therapeutic class
Integration with datasets

3. Clinical Prediction Tasks

File: references/tasks.md

Read when:

Defining clinical prediction objectives
Using predefined tasks (mortality, readmission, drug recommendation)
Working with EHR, signal, imaging, or text-based tasks
Creating custom prediction tasks
Setting up input/output schemas for models
Applying task-specific filtering logic

Key Topics:

20+ predefined clinical tasks
EHR tasks (mortality, readmission, length of stay, drug recommendation)
Signal tasks (sleep staging, EEG analysis, seizure detection)
Imaging tasks (COVID-19 chest X-ray classification)
Text tasks (medical coding, specialty classification)
Custom task creation patterns

4. Models and Architectures

File: references/models.md

Read when:

Selecting models for clinical prediction
Understanding model architectures and capabilities
Choosing between general-purpose and healthcare-specific models
Implementing interpretable models (RETAIN, AdaCare)
Working with medication recommendation (SafeDrug, GAMENet)
Using graph neural networks for healthcare
Configuring model hyperparameters

Key Topics:

33+ available models
General-purpose: Logistic Regression, MLP, CNN, RNN, Transformer, GNN
Healthcare-specific: RETAIN, SafeDrug, GAMENet, StageNet, AdaCare
Model selection by task type and data type
Interpretability considerations
Computational requirements
Hyperparameter tuning guidelines

5. Data Preprocessing

File: references/preprocessing.md

Read when:

Preprocessing clinical data for models
Handling sequential events and time-series data
Processing physiological signals (EEG, ECG)
Normalizing lab values and vital signs
Preparing labels for different task types
Building feature vocabularies
Managing missing data and outliers

Key Topics:

15+ processor types
Sequence processing (padding, truncation)
Signal processing (filtering, segmentation)
Feature extraction and encoding
Label processors (binary, multi-class, multi-label, regression)
Text and image preprocessing
Common preprocessing workflows

6. Training and Evaluation

File: references/training_evaluation.md

Read when:

Training models with the Trainer class
Evaluating model performance
Computing clinical metrics
Assessing model fairness across demographics
Calibrating predictions for reliability
Quantifying prediction uncertainty
Interpreting model predictions
Preparing models for clinical deployment

Key Topics:

Trainer class (train, evaluate, inference)
Metrics for binary, multi-class, multi-label, regression tasks
Fairness metrics for bias assessment
Calibration methods (Platt scaling, temperature scaling)
Uncertainty quantification (conformal prediction, MC dropout)
Interpretability tools (attention visualization, SHAP, ChEFER)
Complete training pipeline example

Installation

uv pip install pyhealth

Requirements:

Python ≥ 3.7
PyTorch ≥ 1.8
NumPy, pandas, scikit-learn

Common Use Cases

Use Case 1: ICU Mortality Prediction

Objective: Predict patient mortality in intensive care unit

Approach:

Load MIMIC-IV dataset → Read references/datasets.md
Apply mortality prediction task → Read references/tasks.md
Select interpretable model (RETAIN) → Read references/models.md
Train and evaluate → Read references/training_evaluation.md
Interpret predictions for clinical use → Read references/training_evaluation.md

Use Case 2: Safe Medication Recommendation

Objective: Recommend medications while avoiding drug-drug interactions

Approach:

Load EHR dataset (MIMIC-IV or OMOP) → Read references/datasets.md
Apply drug recommendation task → Read references/tasks.md
Use SafeDrug model with DDI constraints → Read references/models.md
Preprocess medication codes → Read references/medical_coding.md
Evaluate with multi-label metrics → Read references/training_evaluation.md

Use Case 3: Hospital Readmission Prediction

Objective: Identify patients at risk of 30-day readmission

Approach:

Load multi-site EHR data (eICU or OMOP) → Read references/datasets.md
Apply readmission prediction task → Read references/tasks.md
Handle class imbalance in preprocessing → Read references/preprocessing.md
Train Transformer model → Read references/models.md
Calibrate predictions and assess fairness → Read references/training_evaluation.md

Use Case 4: Sleep Disorder Diagnosis

Objective: Classify sleep stages from EEG signals

Approach:

Load sleep EEG dataset (SleepEDF, SHHS) → Read references/datasets.md
Apply sleep staging task → Read references/tasks.md
Preprocess EEG signals (filtering, segmentation) → Read references/preprocessing.md
Train CNN or RNN model → Read references/models.md
Evaluate per-stage performance → Read references/training_evaluation.md

Use Case 5: Medical Code Translation

Objective: Standardize diagnoses across different coding systems

Approach:

Read references/medical_coding.md for comprehensive guidance
Use CrossMap to translate between ICD-9, ICD-10, CCS
Group codes into clinically meaningful categories
Integrate with dataset processing

Use Case 6: Clinical Text to ICD Coding

Objective: Automatically assign ICD codes from clinical notes

Approach:

Load MIMIC-III with clinical text → Read references/datasets.md
Apply ICD coding task → Read references/tasks.md
Preprocess clinical text → Read references/preprocessing.md
Use TransformersModel (ClinicalBERT) → Read references/models.md
Evaluate with multi-label metrics → Read references/training_evaluation.md

Best Practices

Data Handling

Always split by patient: Prevent data leakage by ensuring no patient appears in multiple splits

from pyhealth.datasets import split_by_patient
train, val, test = split_by_patient(dataset, [0.7, 0.1, 0.2])

Check dataset statistics: Understand your data before modeling

print(dataset.stats())  # Patients, visits, events, code distributions

Use appropriate preprocessing: Match processors to data types (see references/preprocessing.md)

Model Development

Start with baselines: Establish baseline performance with simple models
- Logistic Regression for binary/multi-class tasks
- MLP for initial deep learning baseline
Choose task-appropriate models:
- Interpretability needed → RETAIN, AdaCare
- Drug recommendation → SafeDrug, GAMENet
- Long sequences → Transformer
- Graph relationships → GNN
Monitor validation metrics: Use appropriate metrics for task and handle class imbalance
- Binary classification: AUROC, AUPRC (especially for rare events)
- Multi-class: macro-F1 (for imbalanced), weighted-F1
- Multi-label: Jaccard, example-F1
- Regression: MAE, RMSE

Clinical Deployment

Calibrate predictions: Ensure probabilities are reliable (see references/training_evaluation.md)
Assess fairness: Evaluate across demographic groups to detect bias
Quantify uncertainty: Provide confidence estimates for predictions
Interpret predictions: Use attention weights, SHAP, or ChEFER for clinical trust
Validate thoroughly: Use held-out test sets from different time periods or sites

Limitations and Considerations

Data Requirements

Large datasets: Deep learning models require sufficient data (thousands of patients)
Data quality: Missing data and coding errors impact performance
Temporal consistency: Ensure train/test split respects temporal ordering when needed

Clinical Validation

External validation: Test on data from different hospitals/systems
Prospective evaluation: Validate in real clinical settings before deployment
Clinical review: Have clinicians review predictions and interpretations
Ethical considerations: Address privacy (HIPAA/GDPR), fairness, and safety

Computational Resources

GPU recommended: For training deep learning models efficiently
Memory requirements: Large datasets may require 16GB+ RAM
Storage: Healthcare datasets can be 10s-100s of GB

Troubleshooting

Common Issues

ImportError for dataset:

Ensure dataset files are downloaded and path is correct
Check PyHealth version compatibility

Out of memory:

Reduce batch size
Reduce sequence length (max_seq_length)
Use gradient accumulation
Process data in chunks

Poor performance:

Check class imbalance and use appropriate metrics (AUPRC vs AUROC)
Verify preprocessing (normalization, missing data handling)
Increase model capacity or training epochs
Check for data leakage in train/test split

Slow training:

Use GPU (device="cuda")
Increase batch size (if memory allows)
Reduce sequence length
Use more efficient model (CNN vs Transformer)

Getting Help

Documentation: https://pyhealth.readthedocs.io/
GitHub Issues: https://github.com/sunlabuiuc/PyHealth/issues
Tutorials: 7 core tutorials + 5 practical pipelines available online

Example: Complete Workflow

# Complete mortality prediction pipeline
from pyhealth.datasets import MIMIC4Dataset
from pyhealth.tasks import mortality_prediction_mimic4_fn
from pyhealth.datasets import split_by_patient, get_dataloader
from pyhealth.models import RETAIN
from pyhealth.trainer import Trainer

# 1. Load dataset
print("Loading MIMIC-IV dataset...")
dataset = MIMIC4Dataset(root="/data/mimic4")
print(dataset.stats())

# 2. Define task
print("Setting mortality prediction task...")
sample_dataset = dataset.set_task(mortality_prediction_mimic4_fn)
print(f"Generated {len(sample_dataset)} samples")

# 3. Split data (by patient to prevent leakage)
print("Splitting data...")
train_ds, val_ds, test_ds = split_by_patient(
    sample_dataset, ratios=[0.7, 0.1, 0.2], seed=42
)

# 4. Create data loaders
train_loader = get_dataloader(train_ds, batch_size=64, shuffle=True)
val_loader = get_dataloader(val_ds, batch_size=64)
test_loader = get_dataloader(test_ds, batch_size=64)

# 5. Initialize interpretable model
print("Initializing RETAIN model...")
model = RETAIN(
    dataset=sample_dataset,
    feature_keys=["diagnoses", "procedures", "medications"],
    mode="binary",
    embedding_dim=128,
    hidden_dim=128
)

# 6. Train model
print("Training model...")
trainer = Trainer(model=model, device="cuda")
trainer.train(
    train_dataloader=train_loader,
    val_dataloader=val_loader,
    epochs=50,
    optimizer="Adam",
    learning_rate=1e-3,
    weight_decay=1e-5,
    monitor="pr_auc_score",  # Use AUPRC for imbalanced data
    monitor_criterion="max",
    save_path="./checkpoints/mortality_retain"
)

# 7. Evaluate on test set
print("Evaluating on test set...")
test_results = trainer.evaluate(
    test_loader,
    metrics=["accuracy", "precision", "recall", "f1_score",
             "roc_auc_score", "pr_auc_score"]
)

print("\nTest Results:")
for metric, value in test_results.items():
    print(f"  {metric}: {value:.4f}")

# 8. Get predictions with attention for interpretation
predictions = trainer.inference(
    test_loader,
    additional_outputs=["visit_attention", "feature_attention"],
    return_patient_ids=True
)

# 9. Analyze a high-risk patient
high_risk_idx = predictions["y_pred"].argmax()
patient_id = predictions["patient_ids"][high_risk_idx]
visit_attn = predictions["visit_attention"][high_risk_idx]
feature_attn = predictions["feature_attention"][high_risk_idx]

print(f"\nHigh-risk patient: {patient_id}")
print(f"Risk score: {predictions['y_pred'][high_risk_idx]:.3f}")
print(f"Most influential visit: {visit_attn.argmax()}")
print(f"Most important features: {feature_attn[visit_attn.argmax()].argsort()[-5:]}")

# 10. Save model for deployment
trainer.save("./models/mortality_retain_final.pt")
print("\nModel saved successfully!")

Resources

For detailed information on each component, refer to the comprehensive reference files in the references/ directory:

datasets.md: Data structures, loading, and splitting (4,500 words)
medical_coding.md: Code translation and standardization (3,800 words)
tasks.md: Clinical prediction tasks and custom task creation (4,200 words)
models.md: Model architectures and selection guidelines (5,100 words)
preprocessing.md: Data processors and preprocessing workflows (4,600 words)
training_evaluation.md: Training, metrics, calibration, interpretability (5,900 words)

Total comprehensive documentation: ~28,000 words across modular reference files.

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skills/pyhealth/SKILL.md

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Imported 8/1/2026Version v1Bundled 6 files

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PyHealth: Healthcare AI Toolkit

Overview

When to Use This Skill

Core Capabilities

Quick Start Workflow

Detailed Documentation

1. Datasets and Data Structures

2. Medical Coding Translation

3. Clinical Prediction Tasks

4. Models and Architectures

5. Data Preprocessing

6. Training and Evaluation

Installation

Common Use Cases

Use Case 1: ICU Mortality Prediction

Use Case 2: Safe Medication Recommendation

Use Case 3: Hospital Readmission Prediction

Use Case 4: Sleep Disorder Diagnosis

Use Case 5: Medical Code Translation

Use Case 6: Clinical Text to ICD Coding

Best Practices

Data Handling

Model Development

Clinical Deployment

Limitations and Considerations

Data Requirements

Clinical Validation

Computational Resources

Troubleshooting

Common Issues

Getting Help

Example: Complete Workflow

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