ArticleStudies in health technology and informatics2017
Predicting Length of Stay for Obstetric Patients via Electronic Medical Records.
Article in Studies in health technology and informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Three machine learning algorithms and their utility in exploring risk factors associated with primary cesarean section in low-risk women: A methods paper.Research in nursing & health · 2021Article
- An Overview of Finnish Maternal Health Care As a Potential Model for Decreasing Maternal Mortality in the United States.Women's health reports (New Rochelle, N.Y.) · 2021Article
- Article
- Provider Networks in the Neonatal Intensive Care Unit Associate with Length of Stay.... IEEE Conference on Collaboration and Internet Computing. IEEE Conference on Collaboration and Internet Computing · 2019Article
- Interaction patterns of trauma providers are associated with length of stay.Journal of the American Medical Informatics Association : JAMIA · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Obstetric care refers to the care provided to patients during ante-, intra-, and postpartum periods. Predicting length of stay (LOS) for these patients during their hospitalizations can assist healthcare organizations in allocating hospital resources more effectively and efficiently, ultimately improving maternal care quality and reducing costs to patients. In this paper, we investigate the extent to which LOS can be forecast from a patient's medical history. We introduce a machine learning framework to incorporate a patient's prior conditions (e.g., diagnostic codes) as features in a predictive model for LOS. We evaluate the framework with three years of historical billing data from the electronic medical records of 9188 obstetric patients in a large academic medical center. The results indicate that our framework achieved an average accuracy of 49.3%, which is higher than the baseline accuracy 37.7% (that relies solely on a patient's age). The most predictive features were found to have statistically significant discriminative ability. These features included billing codes for normal delivery (indicative of shorter stay) and antepartum hypertension (indicative of longer stay).
Indexed as
Identifiers
29295255PMC5860660What OpenQuestion holds
Registered trials
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