Artificial Intelligence and Machine Learning in Tropical Disease Management

Health information science, Год журнала: 2025, Номер unknown, С. 155 - 182

Опубликована: Янв. 1, 2025

Язык: Английский

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Benchmark Analysis of Time Series Models for Malaria Trends in the Adamawa Region (Cameroon)

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 61 - 79

Опубликована: Янв. 1, 2025

Язык: Английский

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Applications of machine learning approaches to combat COVID-19: A survey

Elsevier eBooks, Год журнала: 2022, Номер unknown, С. 263 - 287

Опубликована: Янв. 1, 2022

Язык: Английский

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Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review

Cureus, Год журнала: 2023, Номер unknown

Опубликована: Май 1, 2023

During the early phase of COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting exploration machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal such decision support systems their use in management can aid medical community making informed decisions during risk assessment patients, especially low-resource settings. Therefore, objective this study was to systematically review studies that predicted or severity disease using ML. Following Preferred Reporting Items Systematic Reviews Meta-Analysis (PRISMA), we conducted literature search MEDLINE (OVID), Scopus, EMBASE, IEEE Xplore from January 1 June 31, 2020. The outcomes were prognostic measures as death, need mechanical ventilation, admission, acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, reports. extracted data about study's country, setting, sample size, source, dataset, diagnostic outcomes, prediction measures, type ML model, accuracy. Bias assessed Prediction model Risk Of ASsessment Tool (PROBAST). This registered International Prospective Register (PROSPERO), with number CRD42020197109. final records extraction 66. Forty-three (64%) used secondary data. majority Chinese authors (30%). Most (79%) relied on chest imaging prediction, while remainder various laboratory indicators, including hematological, biochemical, immunological markers. Thirteen explored predicting severity, rest diagnosis. Seventy percent articles deep models, 30% traditional algorithms. reported high sensitivity, specificity, accuracy models (exceeding 90%). overall concern bias "unclear" 56% studies. mainly due concerns selection bias. may help identify patients particularly context imaging. Although these reflect exhibit accuracy, novelty biases dataset make them replacement clinicians' cognitive decision-making questionable. Continued is needed enhance robustness reliability

Язык: Английский

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A systematic literature review of time series methods applied to epidemic prediction

Informatics in Medicine Unlocked, Год журнала: 2024, Номер 50, С. 101571 - 101571

Опубликована: Янв. 1, 2024

Язык: Английский

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