MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202611110943 A) filed by Mr. Laxminarayana Thirupathi on September 16, 2026, for Deep Learning Based Multi Modal Health Care Diagnosis And Risk Prediction Platform.
Inventor includes Mr. Laxminarayana Thirupathi.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: The rapid growth of digital healthcare data has created new opportunities for developing intelligent systems that can support early diagnosis and risk prediction. This project proposes a Deep Learning Based Multi-Modal Healthcare Diagnosis and Risk Prediction Platform that integrates heterogeneous medical information, including clinical records, laboratory test results, medical images, demographic attributes, and patient-reported information, to provide a comprehensive assessment of an individual’s health condition. Unlike conventional healthcare systems that primarily depend on a single data source, the proposed platform combines multiple modalities to capture complementary clinical patterns and improve the reliability of predictions. Deep learning techniques are employed to automatically learn meaningful representations from different types of healthcare data, while a multi-modal fusion mechanism combines these representations to generate unified diagnostic and risk assessments. The platform can be designed to support medical image analysis using convolutional neural networks, structured clinical data analysis using deep neural networks, and sequential patient information processing using suitable temporal learning models. The integrated system aims to identify potential diseases at an early stage, estimate the likelihood of future health complications, and assist healthcare professionals in making informed clinical decisions. In addition, the platform can incorporate explainable artificial intelligence techniques to provide interpretable insights into the factors contributing to a prediction, thereby improving transparency and user confidence. The proposed approach is intended to reduce the limitations associated with isolated medical data analysis, improve diagnostic accuracy, enable personalized risk assessment, and support timely intervention. By bringing diverse healthcare information into a unified deep learning framework, the platform has the potential to enhance clinical decision support, optimize healthcare resource utilization, and contribute to more proactive and personalized patient care. The system is envisioned as an assistive technology for healthcare professionals rather than a replacement for clinical expertise, with its effectiveness depending on the quality, diversity, and representativeness of the underlying medical data.
Disclaimer: Curated by HT Syndication.
