Machine learning based decipherment of Cell Population Data: a promising hospital front-door screening tool for COVID-19. (28th October 2021)

• Record Type: Journal Article
• Authors: Haider, R; Shamsi, T S; Khan, N A
• Is Part Of: American journal of clinical pathology. Volume 156(2021)Supplement 1
• Page Start: S101
• Page End: S102
• Publication Date: 2021-10-28
• Subjects: Diagnosis, Laboratory -- Periodicals; Pathology -- Periodicals; 616.07
• DOI: 10.1093/ajcp/aqab191.216
• Languages: English
• ISSNs: 0002-9173

Abstract

Abstract: Introduction/Objective: Key challenges against early diagnosis of COVID-19 are its symptoms sharing nature and prolong SARS-CoV-2 PCR turnaround time. Hither machine learning (ML) tools experienced by routinely generated clinical data; potentially grant early prediction. Methods/Case Report: Routine and earlier diagnostic data along demographic information were extracted for total of 21, 672 subsequent presentations. Along conventional statistics, multilayer perceptron (MLP) and radial basis function (RBF) were applied to predict COVID-19 from pre-pandemic control. Three feature sets were prepared, and performance evaluated through stratified 10-fold cross validation. With differing predominance of COVID-19, multiple test sets were created and predictive efficiency was evaluated to simulate real-fashion performance against fluctuating course of pandemic. Models validation was also inducted in prospective manner on independent dataset, equating framework forecasting to conclusions from PCR. Results (if a Case Study enter NA): RBF model attained superior cross entropy error 20.761(7.883) and 20.782(3.991) for Q-Flags and Routine Items respectively while MLP outperformed for cell population data (CPD) parameters with value of 6.968(1.259) for 'training(testing)'. Our CPD driven MLP framework in challenge of lower (<5%) COVID-19 predominance affords greater negative predictive values (NPV >99%). Higher accuracy (%correct 92.5) was offered during prospective validation using independent dataset. Sensitivity analysis advances illusive accuracy (%correct 94.1) and NPV (96.9%). LY-WZ, Blasts/Abn Lympho?, 'HGB Interf?', and 'RBC Agglutination?' are among novel enlightening study attributes. Conclusion: CPD driven ML tools offer efficient screening of COVID-19 patients at presentation to hospital to backing early expulsion and directing patients' flow-from amid the initial presentation to hospital.