ReviewJournal of personalized medicine2024
The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment.
Review in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Integrating digital and narrative medicine in modern healthcare: a systematic review.Medical education online · 2025Pooled it
- Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review.Journal of medical Internet research · 2025Pooled it
- Electronic Devices and Digital Therapeutics in Neuromuscular Diseases: Narrative Review on Current Applications in Patients' Management and Pharmacovigilance.Health care science · 2026Review
- Hereditary spastic paraplegia: from decades of therapy to future innovations.Therapeutic advances in neurological disorders · 2026Review
- Data-Driven Strategies in Orthopaedics: Optimizing Surgical Precision and Patient Outcomes.Indian journal of orthopaedics · 2025Review
- Diagnostics and Therapy for Malignant Tumors.Biomedicines · 2024Review
- Overactive Bladder Syndrome: The Urgency of Integrating Emerging Methodologies to Improve Therapeutic Outcomes.International urogynecology journal · 2024Article
- Use machine learning to predict bone metastasis of esophageal cancer: A population-based study.Digital healthArticle
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.
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.