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 VIRtual DOCtor, using statistical and machine intelligence tools (SMI), to analyse Dengue 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. [ABSTRACT FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
The devastating trail of Covid-19 is characterized by one of the highest mortality-to-infected ratio for a pandemic. Restricted therapeutic and early-stage vaccination still renders social exclusion through lockdown as the key containment mode.To understand the dynamics, we propose PHIRVD, a mechanistic infection propagation model that Machine Learns (Bayesian Markov Chain Monte Carlo) the evolution of six infection stages, namely healthy susceptible (H), predisposed comorbid susceptible (P), infected (I), recovered (R), herd immunized (V) and mortality (D), providing a highly reliable mortality prediction profile for 18 countries at varying stages of lockdown. Training data between 10 February to 29 June 2020, PHIRVD can accurately predict mortality profile up to November 2020, including the second wave kinetics. The model also suggests mortality-to-infection ratio as a more dynamic pandemic descriptor, substituting reproduction number. PHIRVD establishes the importance of early and prolonged but strategic lockdown to contain future relapse, complementing futuristic vaccine impact.