MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085863 A) filed by Vallurupalli Nageswara Rao Vignana Jyothi Institute Of Engineering Technology on July 13, 2026, for Intelligent Clinical Decision Support System For Adr And Ddi Prediction Using Healthcare Data Analytics.
Inventors include Dr. A. Madhavi; Mrs. Sarika Nyaramneni; and Dr. Anjusha Pimpalshende.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: Adverse drug reactions (ADRs) and drug-drug interactions (DDIs) pose significant risks to patient safety, often leading to severe complications and even death. The early prediction and detection of these risks are critical to improving patient outcomes and optimizing treatment plans. This project aims to develop a system for predicting ADRs and DDIs using advanced machine learning and data mining techniques. The system leverages a comprehensive dataset containing drug interactions, side effects, and other relevant clinical information to provide actionable insights for healthcare professionals. The proposed solution integrates various methodologies, including statistical analysis, machine learning algorithms (such as Random Forest, Support Vector Machines, and Neural Networks), and natural language processing (NLP) to extract relevant information from unstructured clinical texts. By analyzing large-scale datasets from sources like DrugBank and clinical reports, the system can predict potential ADRs and DDIs with high accuracy, allowing healthcare providers to make more informed decisions when prescribing medications. The system architecture consists of data collection, preprocessing, feature extraction, model training, and evaluation stages, culminating in a user-friendly interface that displays predictions and recommendations for doctors, pharmacists, and patients. The system also includes robust evaluation mechanisms, such as confusion matrices and performance metrics like accuracy, precision, recall, and F1-score, to ensure its effectiveness. This tool aims to enhance patient safety by reducing the risks of harmful drug interactions and side effects. The integration of ADR and DDI prediction into healthcare practices can significantly reduce adverse events, improve the quality of care, and facilitate personalized medicine. Furthermore, the proposed system can be extended to incorporate new drug data and continually improve its predictive capabilities.
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