I built MedKit because medical data is notoriously difficult to work with. If you want to correlate a drug's FDA label with its latest clinical trial phases and related research papers, you usually have to juggle three different APIs, handle idiosyncratic JSON schemas, and deal with inconsistent identifier types.
MedKit is a unified Python SDK that transforms these fragmented sources (OpenFDA, PubMed, and ClinicalTrials.gov) into a single, programmable platform.
Key Features:
Unified Client: One MedKit() client to rule them all. No more multiple API keys or manual correlation.
Clinical Synthesis (med.conclude()): Aggregates data to give a "snapshot" verdict on a drug or condition, including an evidence strength score (0.0–1.0).
Interaction Engine: catch drug-drug contraindications using cross-label mentions (brand vs generic).
Medical Relationship Graph: Visualize connections between drugs, trials, and research papers as a knowledge graph.
Intelligence Layer: Natural language routing (med.ask()) to query data in plain English.
Why Use It? Most healthcare developers spend 80% of their time just cleaning and joining data. MedKit handles the plumbing (caching, schema normalization, relationship mapping) so you can focus on the analysis or the application logic.
Tech Stack: Python (Sync/Async), Disk/Memory caching, and a provider-based architecture for easy extensibility.
I'd love to get your thoughts on the med.conclude() synthesis logic and what other providers (e.g., pharmacogenomics) you'd find useful.
Hi HN,
I built MedKit because medical data is notoriously difficult to work with. If you want to correlate a drug's FDA label with its latest clinical trial phases and related research papers, you usually have to juggle three different APIs, handle idiosyncratic JSON schemas, and deal with inconsistent identifier types.
MedKit is a unified Python SDK that transforms these fragmented sources (OpenFDA, PubMed, and ClinicalTrials.gov) into a single, programmable platform.
Key Features:
Unified Client: One MedKit() client to rule them all. No more multiple API keys or manual correlation. Clinical Synthesis (med.conclude()): Aggregates data to give a "snapshot" verdict on a drug or condition, including an evidence strength score (0.0–1.0). Interaction Engine: catch drug-drug contraindications using cross-label mentions (brand vs generic). Medical Relationship Graph: Visualize connections between drugs, trials, and research papers as a knowledge graph. Intelligence Layer: Natural language routing (med.ask()) to query data in plain English. Why Use It? Most healthcare developers spend 80% of their time just cleaning and joining data. MedKit handles the plumbing (caching, schema normalization, relationship mapping) so you can focus on the analysis or the application logic.
Tech Stack: Python (Sync/Async), Disk/Memory caching, and a provider-based architecture for easy extensibility.
I'd love to get your thoughts on the med.conclude() synthesis logic and what other providers (e.g., pharmacogenomics) you'd find useful.
Repo: https://github.com/interestng/medkit PyPI: pip install medkit-sdk
Looking forward to your feedback and support! All constructive criticism, stars and follows (on github) and votes greatly help me out as well.