Multi-modal diagnostic ingestion, Python-powered clinical intelligence engines, and vector databases designed for the semantic complexity of medical knowledge.
The Ayur-Pod architecture unifies classical Ayurvedic diagnostics with modern clinical data streams into a single, queryable patient intelligence layer.
Ayur-Pod is a data integration layer that accepts inputs from 8 classical Ayurvedic diagnostic modalities (Ashtavidha Pariksha) alongside modern biometric streams — wearable data, lab results, imaging metadata, and genomic markers — and fuses them into a unified patient intelligence vector.
Each data modality is encoded separately, then merged via a learned clinical fusion model trained on annotated BAMS practitioner assessments.
Piezoelectric sensor arrays capture Nadi characteristics at three positions (Vata, Pitta, Kapha sites). Signal processing extracts 47 pulse features mapped to classical Ayurvedic parameters.
A lightweight transformer architecture (4M parameters, runs on CPU) learns to weight and combine inputs from all Ayur-Pod modalities into a coherent patient state vector updated at each clinical encounter.
All Ayur-Pod outputs are mapped to FHIR R4 Observation resources with custom extensions for Ayurvedic terminologies. Interoperability with existing hospital systems without compromising data fidelity.
Open-source, auditable clinical decision support built in Python. Every algorithm is inspectable, every output is explainable.
A Python library for multi-dimensional clinical risk scoring. Integrates standard risk models (CHADS-VASc, FRAX, Framingham) with custom Ayurvedic vitality indices into a unified patient risk profile.
Symptom-to-diagnosis mapping using a Bayesian network trained on 2.3M clinical encounters. Outputs ranked differentials with confidence intervals and evidence citations — all running locally with no cloud dependency.
Time-series modelling of patient health trajectories. Detects decompensation patterns, treatment response curves, and seasonal constitutional shifts in Ayurvedic patients using Prophet + custom clinical seasonality priors.
Semantic search across your entire clinical knowledge base — from patient records to research literature and classical Ayurvedic texts.
Qdrant deployed on bare-metal or local Docker. Index patient records, discharge summaries, protocol documents, and the complete Charak Samhita. Sub-50ms semantic retrieval across millions of clinical documents.
Not all embeddings are equal for clinical text. BGE-M3 outperforms general-purpose models on medical retrieval benchmarks. Custom fine-tuning on Ayurvedic terminology bridges the domain gap between classical and modern medical language.
Questions about pipeline architecture, data ingestion, or vector database configuration? Share them here.
The Ayur-Pod modality fusion architecture is the missing piece we needed. Our institution has been collecting Nadi data for 3 years with no way to integrate it into the main EHR pipeline. This approach could finally bridge that gap.
BGE-M3 is genuinely the right choice for clinical text. I've benchmarked it against six embedding models on our discharge summary corpus and the gap is significant — especially for multi-language medical records.
The patient trajectory modeller with seasonal constitutional priors is elegant. Ritucharya (seasonal regimen) in classical Ayurveda maps surprisingly well to modern chronobiology. Would love to see the validation dataset details.