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Project Proposals From Bengal MSME's
 

PROJECT PROPOSALS FROM BENGAL MSME'S
 
 
Workflow & Dashboards for AI based Disease Management Kanchan x Thakur District: Kolkata 2019-06-01 Credit Assessment score: Not Applicable KR Consultancy 1. Creating a complete Diagnosis Workflow and Decision Tree that will be input to AI Engine 2. Creating graphical Dashboard called (Intelligent Diabetes Dashboard) that will be shown to doctors initially by Data collected and subsequently by AI and Knowledge Graph 3. A Neural Network Based (ANN) Classifier that will intake the Decision Tree (or trees) , The data, The classification algorithms and produce output for both patients and doctors Management of complex chronic diseases such as diabetes, requires the assimilation and interpretation of multiple laboratory test results, and lot of collected historical data. Traditional electronic health records (EHRs) tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to chronic disease care challenging. We will solve this part first by integrating all data into a single place and build a knowledge Graph or simply a Graph Database. Our Goal then would be, to develop a disease-specific Artificial Intelligence supported, clinical decision support system ( IDD - Intelligent Diabetes Dashboard ) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines by local doctors. The clinical decision support system will then include a dashboard feature that graphically summarizes all relevant laboratory results, diagnosis and displays them in a color-coded system that allows quick interpretation of the metabolic control of the patients. An AI based alert module would inform the user (patient) of tests that are due for repeat testing. An interactive graph module would be developed for better visual appreciation of the trends of the laboratory results of the patient. In conclusion, we will develop and show that the use of the Intelligent Diabetes Dashboard, which incorporates several decision support features, can improve the management of disease for both doctors and patients. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.