Artificial Intelligence and Machine Learning in Tropical Disease Management

Health information science, Journal Year: 2025, Volume and Issue: unknown, P. 155 - 182

Published: Jan. 1, 2025

Language: Английский

---

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

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 61 - 79

Published: Jan. 1, 2025

Language: Английский

---

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

Elsevier eBooks, Journal Year: 2022, Volume and Issue: unknown, P. 263 - 287

Published: Jan. 1, 2022

Language: Английский

---

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

Cureus, Journal Year: 2023, Volume and Issue: unknown

Published: May 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

Language: Английский

---

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

Informatics in Medicine Unlocked, Journal Year: 2024, Volume and Issue: 50, P. 101571 - 101571

Published: Jan. 1, 2024

Language: Английский

---