medical-data-tools

name: medical-data-tools description: Medical data workflow guide for DICOM metadata inspection and basic de-identification, physiological signal analysis with NeuroKit2, and cohort-table profiling for clinical research datasets. Use when the user asks to inspect imaging metadata, summarize ECG/PPG/EDA/RSP/EMG signals, or profile tabular medical datasets without making patient-specific diagnoses or treatment decisions.

Medical Data Tools

Use this skill when the user asks to inspect medical imaging files, biosignal recordings, or cohort tables for research, QA, or data-engineering work.

Typical triggers:

• inspect DICOM files, modality mix, study or series structure, metadata completeness
• write basic de-identified DICOM copies for downstream research workflows
• analyze ECG, PPG, EDA, RSP, or EMG tables with NeuroKit2
• summarize clinical cohort tables exported from EHR, OMOP, FHIR, registry, or claims workflows
• profile labels, codes, visits, time ranges, or subgroup balance before modeling

Environment Check

which python3 || true
python3 - <<'PY'
mods = ["pandas", "numpy", "pydicom", "neurokit2"]
for name in mods:
    try:
        __import__(name)
        print(f"{name}: ok")
    except Exception as exc:
        print(f"{name}: missing ({exc})")
PY

If pydicom or neurokit2 is missing, say so immediately instead of pretending the analysis ran.

Bundled Assets

• templates/dicom_inspect.py
• templates/neuro_signal_analyze.py
• templates/clinical_cohort_profile.py

Preferred Workflow

1. Identify the input surface first: DICOM file tree, CSV or TSV signal table, or cohort table.
2. Run the smallest deterministic template before any broader interpretation.
3. Save both the machine-readable output and the summary JSON.
4. Report missing columns, unreadable files, or module gaps explicitly.
5. Treat all outputs as research or operations artifacts, not clinical decisions.

DICOM

Use templates/dicom_inspect.py for:

• modality or study inventory
• metadata QA
• study/series counts
• basic research-oriented de-identification copies

Quick start:

python3 templates/dicom_inspect.py imaging/ct_series \
  --recursive \
  --output medical/dicom_inventory.csv \
  --summary medical/dicom_inventory.json

Basic de-identification example:

python3 templates/dicom_inspect.py imaging/mri_case \
  --recursive \
  --deidentify-dir medical/dicom_deidentified \
  --output medical/dicom_case.csv \
  --summary medical/dicom_case.json

Deliverables:

• per-file metadata CSV
• summary JSON with modality counts and study/series counts
• optional de-identified DICOM copies

State clearly that the built-in de-identification is basic tag scrubbing, not a validated anonymization pipeline.

Biosignals

Use templates/neuro_signal_analyze.py for:

• ECG feature extraction and quality-aware summaries
• PPG, EDA, RSP, or EMG preprocessing and interval features
• quick physiology feature generation for downstream research models

Example:

python3 templates/neuro_signal_analyze.py \
  --input signals/ecg.csv \
  --signal-column ecg \
  --signal-type ecg \
  --sampling-rate 250 \
  --signals-output medical/ecg_processed.csv \
  --output medical/ecg_features.csv \
  --summary medical/ecg_features.json

Deliverables:

• one-row feature CSV
• optional processed signal CSV
• summary JSON with duration and processing metadata

Do not overstate these outputs. They are research features, signal summaries, and QA signals, not diagnoses.

Cohort Tables

Use templates/clinical_cohort_profile.py for:

• patient and visit counts
• label balance checks
• code distribution checks
• subgroup balance before survival, prediction, or trial-emulation work

Example:

python3 templates/clinical_cohort_profile.py \
  --input cohorts/nsclc_registry.csv \
  --patient-id-column patient_id \
  --visit-id-column visit_id \
  --time-column encounter_time \
  --label-column response \
  --code-column regimen \
  --group-column sex \
  --group-column stage \
  --output medical/nsclc_profile.csv \
  --summary medical/nsclc_profile.json

Deliverables:

• normalized metric table CSV
• summary JSON with patient, visit, label, code, and group distributions

Output Expectations

Good answers should mention:

• the exact input paths and column names used
• which template was run
• what files were written
• the key data-quality findings
• any missing metadata, missing modules, or de-identification limits

Related Skills

For public drug, clinical-trial, regulatory, or literature APIs, activate pharma-db-tools. For study design, evidence synthesis, and reporting-guideline work, activate clinical-research-tools. For biology databases, activate bio-db-tools. For hypothesis tests, regression, or survival analysis on cohort tables, activate stat-modeling-tools or survival-analysis-tools. For review matrices, citation cleanup, or hypothesis and reproducibility planning, activate literature-review-tools or scientific-workflow-tools.