ADAPTIVE SYSTEM FOR PREDICTION OF MUCORMYCOSIS SEVERITY WITH EXPLAINABLE AI AND INTEGRATED CLINICAL DECISION SUPPORT

Abstract

A system for predicting mucormycosis severity(100), consists of a modules for acquiring clinical data(101), analysing the data(102), explainability module for generating human-interpretable insights into the machine learning model's predictions(103), A decision support module for providing risk stratification and treatment recommendations based on the model’s output(104).

Specification

Description:The invention provides a novel method for predicting the severity of mucormycosis disease by leveraging machine learning (ML) and explainable artificial intelligence (XAI). An AI-driven system is utilized to analyze clinical data to identify patterns predictive of disease progression. Unlike traditional diagnostic methods, which rely on clinical suspicion or invasive testing, this invention enables early detection, aiding in timely and accurate intervention.
The system consists of a machine learning model designed to process a wide array of clinical data collected from both individual patients and larger populations. The input data includes, but is not limited to previous medical conditions like diabetes, cancer, immunosuppressive treatments,comorbidities,laboratory results,symptoms and Demographic Data like Age, sex, geographic location, and environmental exposure. , Claims:1.A system for predicting mucormycosis severity(100), comprising modules for acquiring clinical data(101), analysing the data(102), explainability module for generating human-interpretable insights into the machine learning model's predictions(103), A decision support module for providing risk stratification and treatment recommendations based on the model's output(104).
2.A system for predicting mucormycosis severity(100)of claim 1, wherein the data collection module(201) integrates patient history, lab results, demographics, and imaging data from electronic health records.
3.A system for predicting mucormycosis severity(100) of claim 1, wherein the explainable AI techniques(301) include SHAP values or LIME to provide a visual explanation of the factors contributing to the prediction of mucormycosis severity.
4.A system for predicting mucormycosis severity(100) as claimed in claim 1 wherein the computer readable medium(401) storing instructions for executing the method, wherein the machine learning model is configured to continuously update its predictive capacity by learning from new patient data.

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