ArticleJournal of biomedical informatics2016
Classification of hospital acquired complications using temporal clinical information from a large electronic health record.
Article in Journal of biomedical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Improving acute care outcome in internal medicine: the role of early stabilization and intermediate care unit.Internal and emergency medicine · 2025Observational
- Evidence of the use of the Perme Intensive Care Unit Mobility Score in hospitalized adults: a scoping review.Revista latino-americana de enfermagem · 2025Article
- Outcomes and costs of home hospitalisation compared to traditional hospitalisation for infectious diseases in Israel: a cohort study.BMJ open · 2024Article
- In-hospital complications in an acute care geriatric unitBiomedica : revista del Instituto Nacional de Salud · 2021Observational
- Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications.JAMA network open · 2021Article
- Economic evaluations of big data analytics for clinical decision-making: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Analysis of Early Assessable Risk Factors for Poor Outcome in Dogs With Cluster Seizures and Status Epilepticus.Frontiers in veterinary science · 2020Article
- Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?Clinical orthopaedics and related research · 2019Article
- Temporal biomedical data analytics.Journal of biomedical informatics · 2019Article
- Predictive modeling in urgent care: a comparative study of machine learning approaches.JAMIA open · 2018Article
- Careflow Mining Techniques to Explore Type 2 Diabetes Evolution.Journal of diabetes science and technology · 2018Article
- A web-based data visualization tool for the MIMIC-II database.BMC medical informatics and decision making · 2016Article
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
Abstract
Hospital acquired complications (HACs) are serious problems affecting modern day healthcare institutions. It is estimated that HACs result in an approximately 10% increase in total inpatient hospital costs across US hospitals. With US hospital spending totaling nearly $900 billion per annum, the damages caused by HACs are no small matter. Early detection and prevention of HACs could greatly reduce strains on the US healthcare system and improve patient morbidity & mortality rates. Here, we describe a machine-learning model for predicting the occurrence of HACs within five distinct categories using temporal clinical data. Using our approach, we find that at least $10 billion of excessive hospital costs could be saved in the US alone, with the institution of effective preventive measures. In addition, we also identify several keystone features that demonstrate high predictive power for HACs over different time periods following patient admission. The classifiers and features analyzed in this study show high promise of being able to be used for accurate prediction of HACs in clinical settings, and furthermore provide novel insights into the contribution of various clinical factors to the risk of developing HACs as a function of healthcare system exposure.
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.