ReviewSensors (Basel, Switzerland)2023
A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 3 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
86 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning for personalized risk assessment of HIV, syphilis, gonorrhoea and chlamydia: A systematic review and meta-analysis.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2025Pooled it
- Integrating machine learning into acupuncture research: a scoping review.Frontiers in neurology · 2025Pooled it
- The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Climate Informed Dengue Prediction and Future Risk Under a Changing Monsoon Climate in Kerala, India.GeoHealth · 2026Article
- A framework for assessing algorithmic discrimination risks in training data: a case of pediatric type 1 diabetes.JAMIA open · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Article
- Impact of artificial intelligence on cardiovascular workflow, engagement, and outcomes: a systematic review.NPJ digital medicine · 2026Article
- Developing psychosocial phenotypes to understand engagement with digital health technologies for heart failure.NPJ digital medicine · 2026Article
- Interpretable Machine Learning for LPR Risk Estimation: A Single-Center Retrospective Case-Control Study.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.Journal of cheminformatics · 2026Review
- Network toxicology integrated with machine learning and SHAP analysis identifies overlapping immune signatures between Di(2-ethylhexyl) phthalate (DEHP) and Sjögren's syndrome.BMC pharmacology & toxicology · 2026Article
- Enhancing logistic regression classification: insights from simulation and real-world applications through ranked set sampling.Scientific reports · 2026Article
- Artificial intelligence in cardiology: a narrative review with focus on patient outcomes.Cardiovascular diagnosis and therapy · 2026Review
- Machine learning for dysphagia risk prediction in older adults after acute ischemic stroke.BMC medical informatics and decision making · 2026Article
- Machine learning evaluation of the discriminative ability of Castelli Risk Index-I and other non-traditional lipid indices for sarcopenia: a cross-sectional study based on CHARLS.Lipids in health and disease · 2026Article
- Multi-Omics Analysis of Morbid Obesity Using a Patented Unsupervised Machine Learning Platform: Genomic, Biochemical, and Glycan Insights.International journal of molecular sciences · 2026Article
- Machine learning for optimizing mAs in KUB radiography with metal implants.Journal of applied clinical medical physics · 2026Article
- Artificial intelligence applications in oxaliplatin-based chemotherapy for colon cancer: advancing prognosis, toxicity prediction, and dose personalization.Frontiers in pharmacology · 2026Review
- Association of the platelet-to-albumin ratio with diabetic nephropathy lesions via a fine-tuning-free large language model framework.Frontiers in medicine · 2026Article
26 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).
Indexed as
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.