Research-stage · Open for collaboration
A cardiac AI pipeline built on real patient data across 6 hospitals in 4 countries. No synthetic data. No corporate funding.
LangGraph · Gemini · XGBoost · SHAP · EchoNet · MONAI · FastAPI
Research-stage MVP · Not a cleared medical device
01 — The clinical gap
of stent patients are readmitted within 30 days of the procedure.
Most readmissions happen because warning signs in biomarker trends go unnoticed between discharge and the first follow-up.
JAMA Cardiologyof post-PCI patients return to hospital within 12 months.
There is no continuous monitoring layer between cardiology appointments. Each visit sees only the latest report — not the trend.
Lancet Digital Healthearly mortality within 30 days post-procedure — rising to 10% at 5 years.
Delayed intervention on recurring blockages or early heart failure signs is the leading cause of this gap.
European Heart Journallost per patient when cardiologists manually correlate ECG, DICOM, and blood panel data.
Cognitive overload from unstructured multi-modal data leads to missed early warning patterns.
Journal of the American College of CardiologyKeep scrolling — each stat is a published outcome, not a projection
02 — The pipeline
Deterministic ML handles the risk scoring. Gemini only extracts and polishes language — never interprets numbers.
01 / 06
PDF reports, ECG printouts, blood panel results, or CT Angiogram reports. The system accepts messy, real-world hospital documents — not clean structured data.
02 / 06
A multi-node LangGraph pipeline detects the report type, then runs a cardiac-specific Gemini prompt to extract the exact biomarkers cardiologists track. Unstructured text becomes structured JSON.
03 / 06
Three specialized XGBoost sub-models run in parallel — one for Acute MI risk, one for Structural Failure, one for MACE (Major Adverse Cardiac Events). Their outputs are weighted into a single risk trajectory.
04 / 06
A pre-trained deep learning model (Stanford open-source) processes echocardiogram video frames to predict Ejection Fraction — the most critical post-stent cardiac metric.
05 / 06
MONAI framework processes CT Angiogram images to detect and quantify coronary artery stenosis percentage and calcium score — directly from the imaging data, not just the radiology text report.
06 / 06
Every risk signal is backed by SHAP (SHapley Additive Explanations). The system shows which biomarkers drove the output — so cardiologists see the reasoning, not a black-box score.
03 — Models we use
Each data type needs a different model. We are honest about what is built today and what is coming next.
Tabular Data
Three specialized gradient-boosted tree models trained exclusively on real patient records. No LLM involvement in risk scoring — purely deterministic ML.
ECG & Echo
Stanford open-source deep learning model trained on 10,000+ echocardiogram videos to predict Ejection Fraction directly from the video — more accurate than text extraction alone.
CT Angiogram
MONAI (Medical Open Network for AI) is the standard framework for medical imaging AI. Used here to process CT Angiogram DICOM files and quantify coronary stenosis and calcium score.
04 — Data foundation
Healthcare AI trained on synthetic data fails in real clinical environments. Every record here came from a real patient in a real hospital.
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Real patient records
0
Hospitals
0
Countries
Coronary CTA Risk Reclassification Cohort
Structural imaging · Calcium score · LAD stenosis · MACE risk
27,126
Netherlands / USA
Zheen Hospital, Erbil
CK-MB · Troponin · Acute MI · Real clinical pipeline
1,319
Iraq
Zia Heart Foundation Hospital
South Asian cardiac phenotypes · Troponin-I · LDL · eGFR
1,048
Bangladesh
UCI Multi-Hospital Consortium
Cleveland Clinic · Gold-standard benchmark · 4-center validation
920
USA / Hungary / Switzerland
University Medical Center — HF Clinical Records
EF% · Serum creatinine · Heart failure outcomes · Real patients
299
Netherlands
NT-proBNP Longitudinal HFrEF Cohort
2-timepoint NT-proBNP · Velocity features · Mortality outcomes
157
Netherlands
Live demo
Upload any cardiac PDF — an Echo report, CT Angiogram, blood panel, or prescription. The pipeline extracts cardiac biomarkers and returns structured values in seconds.
No account required. No data stored. Your file is processed and discarded immediately.
No cardiac report? The demo page includes sample test data to try.
05 — Who I am
I build applied AI systems — retrieval-augmented generation, agentic pipelines with LangChain and LangGraph, and machine learning models that go from raw data to a production API. OjasLabs is where I applied those skills to a real clinical problem.
The LangGraph extraction pipeline, the XGBoost risk models, the FastAPI backend, the Next.js frontend — I built all of it end to end, on 30,000+ real patient records, to prove I can take an applied AI system from idea to something a hospital could use.
I want to go deeper into this kind of work — data science, applied AI engineering, RAG and LLM systems — at a company building in health-tech, clinical AI, or applied ML. If that's you, I'd like to talk.
Data Scientist · Applied AI Engineer · RAG / LLM / LangChain
Hands-on with retrieval-augmented generation, agentic pipelines built on LangChain and LangGraph, and machine learning from raw data to production APIs. Looking for a role that goes deeper into applied AI — ideally in health-tech or clinical AI.
View what I built →Clinical · Regulatory · Business
The technical foundation is here. To take this to hospitals requires clinical validation, regulatory navigation (MDR, FDA), and business development. If that is your background, let's talk.
Get in touch →Cardiologists · Hospitals · Academia
I am looking for a cardiology department willing to validate this pipeline on retrospective patient data. In return: early access to the platform, custom integration, and if it leads somewhere worth publishing, I'd welcome working on that together.
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