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disease-trajectories

Extract disease trajectory edges from DisTraj JSON into dismech YAML signals.

50|9|Updated Dec 4, 2025
Search Tags:#trajectory#data-extraction#healthcare-analytics#comorbidity#disease-trajectories#yaml-mapping
One-click install
npx skills add https://github.com/monarch-initiative/dismech --skill disease-trajectories
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: disease-trajectories
Source: https://github.com/monarch-initiative/dismech/tree/main/.claude/skills/disease-trajectories
Command: npx skills add https://github.com/monarch-initiative/dismech --skill disease-trajectories

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Mine disease trajectories (DT/DisTraj) outputs for comorbidity/trajectory candidates, including parsing DT JSON/TSV, extracting directed pairs, filtering by sex or significance, and mapping signals into dismech comorbidity YAML.

Core Features & Use Cases

  • Parses DT artifacts (JSON/phase_dict or edge lists) and normalizes to disease_a_id, disease_b_id, directionality, and statistical fields.
  • Supports sex-based and significance filtering to focus on relevant comorbidity signals.
  • Maps signals into dismech comorbidity YAML for downstream validation and knowledge-base integration.
  • Use cases include converting DisTraj outputs into YAML inputs for the dismech knowledge base and related pipelines.

Quick Start

Run dt_extract_edges.py on a DT JSON file to produce a normalized edge list, then map selected edges to dismech comorbidity YAML signals.

Frequently Asked Questions about disease-trajectories

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract disease trajectory relationships from DisTraj JSON outputs?▼

Extract disease trajectory relationships from DisTraj JSON by running dt_extract_edges.py to parse phase_dict structures, normalize disease_a_id and disease_b_id pairs, and output a normalized edge list for mapping.

What is the best way to convert DisTraj outputs into comorbidity YAML signals?▼

Convert DisTraj outputs into comorbidity YAML signals by parsing DT JSON/TSV artifacts, extracting directed disease pairs, applying sex or significance filters, and mapping the normalized statistical fields into dismech comorbidity YAML.

Can I filter comorbidity trajectory pairs by sex or statistical significance?▼

Filter comorbidity trajectory pairs by sex or statistical significance during edge extraction to focus on relevant directed disease relationships before mapping them into the dismech knowledge base YAML format.

Do I need any external dependencies to parse DT JSON and map disease trajectory signals?▼

No external dependencies are required to parse DT JSON and map disease trajectory signals; the Skill bundles all necessary Python scripts, including dt_extract_edges.py, to handle normalization, edge parsing, and YAML mapping rules.

What formats does the disease trajectory parser support for edge list extraction?▼

The disease trajectory parser supports DT JSON phase_dict structures and TSV edge lists, normalizing them into disease_a_id, disease_b_id, directionality, and statistical fields for downstream comorbidity mapping.

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