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• W3196306014 abstract "Abstract Clinicians make routine diagnosis by scrutinizing patients' medical signs and symptoms, a skill popularly referred to as ‘Clinical Eye’. This skill evolves through trial‐and‐error and improves with time. The success of the therapeutic regime relies largely on the accuracy of interpretation of such sign‐symptoms, analysing which a clinician assesses the severity of the illness. The present study is an attempt to propose a complementary medical front by mathematically modelling the ‘Clinical Eye’ of a VIR tual DOC tor, using statistical and machine intelligence tools (SMI), to analyse D engue epidemic infected patients ( 100 case studies with 11 weighted sign‐symptoms ). The SMI in VIRDOCD reads medical data and translates these into a vector comprising multiple linear regression (MLR) coefficients to predict infection severity grades of dengue patients that clone the clinician's experience‐based assessment. Risk managed through ANOVA, the dengue severity grade prediction accuracy from VIRDOCD is found higher (ca 75%) than conventional clinical practice (ca 71.4%, mean accuracy profile assessed by a team of 10 senior consultants). Free of human errors and capable of deciphering even minute differences from almost identical symptoms (to the Clinical eye), VIRDOCD is uniquely individualized in its decision‐making ability. The algorithm has been validated against Random Forest classification (RF, ca 63%), another regression‐based classifier similar to MLR that can be trained through supervised learning. We find that MLR‐based VIRDOCD is superior to RF in predicting the grade of Dengue morbidity. VIRDOCD can be further extended to analyse other epidemic infections, such as COVID‐19." @default.
• W3196306014 created "2021-09-13" @default.
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• W3196306014 date "2021-09-13" @default.
• W3196306014 modified "2023-09-27" @default.
• W3196306014 title "VIRDOCD : A VIRtual DOCtor to predict dengue fatality" @default.
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• W3196306014 doi "https://doi.org/10.1111/exsy.12796" @default.
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