ReviewAntimicrobial stewardship & healthcare epidemiology : ASHE2021
Machine learning and artificial intelligence: applications in healthcare epidemiology.
Review in Antimicrobial stewardship & healthcare epidemiology : ASHE, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning methods for predicting adverse drug events: A systematic review.British journal of clinical pharmacology · 2026Pooled it
- Artificial intelligence in the prevention and early detection of postpartum depression: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Temporal Changes in Inflammatory Markers and Laboratory Parameters in Relation to Risk of Neonatal Sepsis.Journal of clinical medicine · 2026Article
- Machine learning constructs a diagnostic prediction model for gangrenous perforation of acute appendicitis in elderly patients.BMC surgery · 2026Article
- AI-enabled proactive health education: perceptions of public health students - a qualitative study.BMC medical education · 2026Article
- The role and future of the population health observatory: advancing public health intelligence in Saudi Arabia.Frontiers in public health · 2026Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- How New Chatbots Can Support Personalized Medicine.Healthcare informatics research · 2025Article
- Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models.Frontiers in medicine · 2025Article
- Bridging Classical Methodologies inMicroorganisms · 2024Review
- Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions.BMC medicine · 2024Review
- Exploring Prior Antibiotic Exposure Characteristics for COVID-19 Hospital Admission Patients: OpenSAFELY.Antibiotics (Basel, Switzerland) · 2024Article
- Twenty-four-hour physical activity patterns associated with depressive symptoms: a cross-sectional study using big data-machine learning approach.BMC public health · 2024Article
- Fear of falling in community-dwelling older adults: What their gait acceleration pattern reveals.Computer methods and programs in biomedicine · 2024Article
- Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation.JMIR formative research · 2024Article
- Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.Frontiers in veterinary science · 2024Article
- Machine learning in mental health and its relationship with epidemiological practice.Frontiers in psychiatry · 2024Article
- Building a pathway to One Health surveillance and response in Asian countries.Science in One Health · 2024Review
- Machine learning data sources in pediatric sleep research: assessing racial/ethnic differences in electronic health record-based clinical notes prior to model training.Frontiers in sleep · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.