Skills pyhealth

📦

pyhealth

Name: pyhealth
Author: davila7

Content revision r2 Safe ⚡ Contains scripts⚙️ External commands🌐 Network access

Build Clinical Machine Learning Workflows with PyHealth

Clinical machine learning projects require specialized data, task, model, and evaluation choices. This skill provides structured PyHealth guidance from dataset preparation through validation.

Supports: Claude Codex Code(CC)

📊 76 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "pyhealth" from https://skillstore.io/skills/davila7-pyhealth.md and its manifest at https://skillstore.io/api/skills/davila7-pyhealth/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/davila7-pyhealth.md Signed manifest GET /api/skills/davila7-pyhealth/manifest Signed lockfile GET /api/skills/davila7-pyhealth/lockfile

Test it

Using "pyhealth". Plan mortality prediction with MIMIC-IV and RETAIN.

Expected outcome:

Define an in-hospital mortality cohort and split records by patient.
Compare RETAIN against logistic regression and monitor AUPRC.
Calibrate probabilities, assess demographic subgroups, and require clinical review.

Using "pyhealth". Standardize medication codes for a drug recommendation task.

Expected outcome:

Normalize source codes before mapping NDC or RxNorm values to ATC.
Select an explicit ATC hierarchy level and retain unmapped-code reports.
Validate one-to-many mappings before creating medication features.

Using "pyhealth". Evaluate a sleep staging model built from EEG signals.

Expected outcome:

Check filtering, sampling rate, and fixed-length segmentation.
Report macro F1 and per-stage performance for imbalanced labels.
Test patient-level generalization and inspect confusion between adjacent stages.

Security Audit

Safe

v9 • 7/23/2026 Open versioned report

All 60 static findings are false positives caused by Markdown syntax, standard Python imports, clinical terminology, and documentation links. The examples include one fixed installation command, with no automatic execution, command interpolation, reconnaissance, or suspicious network behavior. No prompt injection, social engineering, or data exfiltration intent was found.

Files scanned

3,213

Lines analyzed

Review items

False positives ignored

Risk Factors

⚡ Contains scripts (4)

references/preprocessing.md:427-431 references/preprocessing.md:455-459 references/preprocessing.md:484-488 references/preprocessing.md:511-516

⚙️ External commands (46)

🌐 Network access (2)

SKILL.md:379 SKILL.md:380

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex View Audit History →

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Open versioned report

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Academic citations (APA · BibTeX · CFF)

APA citation

davila7. (2026). pyhealth security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-pyhealth/audits/9

BibTeX citation

@techreport{davila7-davila7-pyhealth-2026,
  author = {davila7},
  title = {pyhealth security audit report (audit version 9)},
  institution = {Skillstore},
  year = {2026},
  number = {9},
  url = {https://skillstore.io/skills/davila7-pyhealth/audits/9},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "pyhealth security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
  - name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-pyhealth/audits/9"
identifiers:
  - type: other
    value: "skillstore:davila7-pyhealth:audit:9"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended Current

davila7-pyhealth

Skillstore Score 76

Evidence Confidence High

Skillstore usage 13

Updated

2026-09-09

K-Dense-AI

k-dense-ai-pyhealth

Skillstore Score 67

Evidence Confidence High

Skillstore usage 11

Updated

2026-09-09

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Design a Clinical Prediction Study

Plan cohorts, patient-level splits, baselines, PyHealth models, metrics, calibration, and subgroup evaluation for a clinical prediction task.

Standardize Healthcare Data

Map diagnosis and medication codes, process longitudinal events, and prepare consistent PyHealth samples across data sources.

Validate a Healthcare Model

Review discrimination, calibration, uncertainty, interpretability, fairness, and external validation requirements before deployment.

Try These Prompts

Plan a PyHealth Project

Plan a beginner PyHealth workflow for [dataset] and [clinical_task]. Explain required data, task setup, model baseline, split strategy, and metrics.

Prepare Clinical Data

Design preprocessing for [data_type] in PyHealth. Include missing values, encoding, patient-level splitting, leakage checks, and validation statistics.

Compare Healthcare Models

Compare [models] for [task] using [dataset]. Recommend hyperparameters, metrics, calibration, fairness checks, and interpretation methods with clear tradeoffs.

Design Multisite Validation

Draft an end-to-end PyHealth experiment for [task] across [sites]. Include external validation, uncertainty, subgroup analysis, deployment gates, and reproducibility controls.

Best Practices

Split records by patient to prevent leakage across training, validation, and test sets.
Start with a simple baseline before comparing specialized healthcare models.
Evaluate calibration, fairness, uncertainty, and external validity alongside predictive performance.

Avoid

Do not place records from one patient in multiple dataset splits.
Do not choose metrics without considering class imbalance and clinical consequences.
Do not deploy predictions without privacy, clinical, fairness, and external validation reviews.