Online ISSN: 2515-8260

Keywords : Electronic Health Records (EHR)


PROGNOSTICATING CLINICAL INCIDENTS VIA RECURRENT NEURAL NETWORKS BY USING CLINICAL DOCTOR AI

S. Kausalya; S. Kalaiselvi; D. Thamarai Selvi; Dr.V. Gomathi

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 2374-2386

Doctor AI imitates human doctor’s forecasting potential and gives diagnostic results that are clinically significant. Prognosticating Clinical Incidents is a timeseries based RNN model. It is implemented and employed to longitudinal time stamped electronic health record data from a twenty thousand patients over a decade. Encounter medical logs of patients data such as diagnosis codes, medication codes and procedure codes are input data to RNN to predict the diagnosis and medication types for a future visit of patients in a hospital. Doctor AI evaluates the history of patient’s to prepare one label for each diagnosis predictions and medication types i.e.,multi-label forecasting/prediction. Leveraging huge historical patient details in electronic health records (EHR), a collective generic and comprehensive predictive model that covers perceived health state and medication uses for EHR, is new approach in disease progress identification.