ArticleJournal of healthcare engineering2021
Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Uncertainty-calibrated adaptation of clinical transformer foundation models enhances in-hospital mortality and hospital readmission prediction.npj health systems · 2026Article
- Smartphone as a Sensor in mHealth: Narrative Overview, SWOT Analysis, and Proposal of Mobile Biomarkers.Sensors (Basel, Switzerland) · 2025Review
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- QTc interval prolongation impact on in-hospital mortality in acute coronary syndromes patients using artificial intelligence and machine learning.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2024Article
- Applying Sequential Pattern Mining to Investigate the Temporal Relationships between Commonly Occurring Internal Medicine Diseases and Intervals for the Risk of Concurrent Disease in Canine Patients.Animals : an open access journal from MDPI · 2023Article
- Network-medicine framework for studying disease trajectories in U.S. veterans.Scientific reports · 2022Article
- A machine learning-based risk warning platform for potentially inappropriate prescriptions for elderly patients with cardiovascular disease.Frontiers in pharmacology · 2022Article
- Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects.Vascular health and risk management · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events' prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.
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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.