Intellectual Property India Publishes Patent Application for Intelligent Clinical Computing Platform For Predicting Patient Health Outcomes Using Deep- Learning-Based Multimodal Health Data Processing

MUMBAI, India, June 30 -- Intellectual Property India has published a patent application (202641073751 A) filed by Dr. K. Bindu Madhavi; Prof. Vrushali Pankaj Thakur; Dr. Rajesh L.; Dayananda Sagar Academy Of Technology And Management; Ms. Veena V.; Mrs. Swetha Koduri; Dr. Pathan Ahmed Khan; Dr. Rajesh Kumar Vishwakarma; Prof. Vishal Gotarane; and Dr. Amitava Biswas on June 14, 2026, for Intelligent Clinical Computing Platform For Predicting Patient Health Outcomes Using Deep- Learning-Based Multimodal Health Data Processing.

Inventors include Dr. K. Bindu Madhavi; Prof. Vrushali Pankaj Thakur; Dr. Rajesh L.; Ms. Veena V.; Mrs. Swetha Koduri; Dr. Pathan Ahmed Khan; Dr. Rajesh Kumar Vishwakarma; Prof. Vishal Gotarane; and Dr. Amitava Biswas.

The application for the patent was published on June 26, 2026, under issue no. 26/2026.

Abstract: [054] The present invention relates to an intelligent clinical computing platform for predicting patient health outcomes using deep-learning-based multimodal health data processing. The platform comprises a health data ingestion engine, a multimodal data harmonisation engine, a temporal feature construction engine, a deep- learning prediction engine, an uncertainty estimation engine, an explainability engine, a clinical alert generation engine, and an audit and feedback engine. The platform receives heterogeneous patient data from electronic health records, laboratory systems, medication records, clinician notes, vital-sign monitors, hospital event systems, and wearable devices. The received data is validated, normalised, temporally aligned, and converted into patient-specific longitudinal health vectors. The deep-learning prediction engine processes the longitudinal health vectors to predict future health outcomes including deterioration risk, readmission risk, treatment response, complication probability, discharge readiness, and mortality risk. The platform generates risk-stratified outputs with confidence values, uncertainty bands, clinically relevant explanatory factors, prioritised alerts, and auditable records. The invention improves healthcare data processing, prediction reliability, clinical interpretability, and secure traceability in healthcare computing environments. Accompanied Drawing [FIGS. 1-2]

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