ReviewMedicina (Kaunas, Lithuania)2020
Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment.
Review in Medicina (Kaunas, Lithuania), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 284 papers, 10 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
284 citing papers in PubMed, 10 syntheses or guidelines pooled it.
- Assessment of multidimensional functional recovery post-stroke: a systematic review of outcome measures and their psychometric properties.BMC neurology · 2026Pooled it
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.Renal failure · 2025Pooled it
- Deep learning models for ICU readmission prediction: a systematic review and meta-analysis.Critical care (London, England) · 2025Pooled it
- Risk prediction models for renal injury in children with IgA vasculitis: a systematic review and meta-analysis.Pediatric rheumatology online journal · 2025Pooled it
- Pooled it
- Application of artificial intelligence and psychosocial functioning in psychosis: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Prediction of frailty in community older adults based on machine learning: a systematic review and meta-analysis.Frontiers in public health · 2025Pooled it
- Pooled it
- Pooled it
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- Machine learning-enhanced baseline risk prediction models for cancer therapy related cardiac dysfunction: a systematic review and critical appraisal of methodological quality.European heart journal. Digital health · 2026Article
- Integrated Cardio-Renal-Metabolic Risk Profiling in Patients with Type 2 Diabetes: A Machine Learning-Assisted Cross-Sectional Analysis.Journal of clinical medicine · 2026Article
- Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach.BMJ health & care informatics · 2026Article
- Multi-horizon machine learning for population-level hypertension risk stratification in the UK Biobank.European heart journal. Digital health · 2026Article
- Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Reducing overconfident errors in clinical prediction models.BMC medical informatics and decision making · 2026Article
- The molecular ICU: a primer on omics, informatics and the future of precision critical care.Critical care (London, England) · 2026Review
- Article
- Identifying predictor factors for asthma using machine learning: evidence from the English Longitudinal Study of Ageing.BMC public health · 2026Article
224 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The benefits of ML comprise flexibility and scalability compared with conventional statistical approaches, which makes it deployable for several tasks, such as diagnosis and classification, and survival predictions. However, much of ML-based analysis remains scattered, lacking a cohesive structure. There is a need to evaluate and compare the performance of well-developed conventional statistical methods and ML on patient outcomes, such as survival, response to treatment, and patient-reported outcomes (PROs). In this article, we compare the usefulness and limitations of traditional statistical methods and ML, when applied to the medical field. Traditional statistical methods seem to be more useful when the number of cases largely exceeds the number of variables under study and a priori knowledge on the topic under study is substantial such as in public health. ML could be more suited in highly innovative fields with a huge bulk of data, such as omics, radiodiagnostics, drug development, and personalized treatment. Integration of the two approaches should be preferred over a unidirectional choice of either approach.
Indexed as
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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.