ArticleNeural computing & applications2020
Big data analytics for preventive medicine.
Article in Neural computing & applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 citing papers in PubMed, 2 syntheses or guidelines pooled it, 312 citations in OpenAlex.
- Global research trends, reporting and handling of missing data in observational studies of type 2 diabetes mellitus with mild cognitive impairment from 2020 to 2025: a systematic review.Frontiers in endocrinology · 2026Pooled it
- Indications, Challenges, and Characteristics of Successful Implementation of Perioperative Registries in Low Resource Settings: A Systematic Review.World journal of surgery · 2023Pooled it
- Participatory Design of AI-Based Clinical Decision Support Systems: Scoping Review.JMIR medical informatics · 2026Article
- Assessing the risk of worsening conditions for diabetes mellitus patients in Italy: a non-parametric and semi-parametric approach to time-to-event data.Population health metrics · 2026Article
- Supervised machine learning models for predicting sepsis-associated acute kidney injury in children: a real-world evaluation.World journal of pediatrics : WJP · 2026Article
- Innovative statistical method for longitudinal and hierarchical data modeling: the GMEXGBoost method.BMC medical research methodology · 2026Article
- Large language models in healthcare quality management: a European perspective on process automation and compliance.Frontiers in digital health · 2026Review
- Secure and Decentralized Hybrid Multi-Face Recognition for IoT Applications.Sensors (Basel, Switzerland) · 2025Article
- New care pathways for supporting transitional care from hospitals to home using AI and personalized digital assistance.Scientific reports · 2025Article
- Understanding Dengue Underreporting: An Analysis of the Impacts for the World, Latin America and Brazil.TheScientificWorldJournal · 2025Review
- Article
- Use of machine learning to predict hypertension based on BMI and routine lab data.Bioinformation · 2025Article
- Ontologies as the semantic bridge between artificial intelligence and healthcare.Frontiers in digital health · 2025Article
- Revolutionizing Maternal Health: The Role of Artificial Intelligence in Enhancing Care and Accessibility.Cureus · 2024Review
- Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks.Infection prevention in practice · 2024Review
- Real-World Research on Retinal Diseases Using Health Claims Database: A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- Review
- Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review.Heliyon · 2024Review
- Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.BMC medical informatics and decision making · 2024Article
- Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.Veterinary research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.
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.