ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Neural Networks for Clinical Order Decision Support.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Deep Learning-driven classification of external DICOM studies for PACS archiving.European radiology · 2022Article
- MedGCN: Medication recommendation and lab test imputation via graph convolutional networks.Journal of biomedical informatics · 2022Article
- Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Machine Learning Predictability of Clinical Next Generation Sequencing for Hematologic Malignancies to Guide High-Value Precision Medicine.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Article
- Complex Oncological Decision-Making Utilizing Fast-and-Frugal Trees in a Community Setting-Role of Academic and Hybrid Modeling.Journal of clinical medicine · 2020Article
- Utilizing a novel unified healthcare model to compare practice patterns between telemedicine and in-person visits.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
5 authors.
Funding
Abstract
Consistent and high quality medical decisions are difficult as the amount of literature, data, and treatment options grow. We developed a model to provide automated physician order decision support suggestions for inpatient care through a feed-forward neural network. Given a patient's current status based on information data-mined and extracted from the Electronic Health Record (EHR), our model predicts clinical orders a physician enters for a patient within 24 hours. As a reference benchmark of real-world standard-of-care clinical decision support, existing manually-curated order sets implemented in the hospital demonstrate precision: 0.21, recall: 0.48, AUROC: 0.75 relative to what clinicians actually order within 24 hours. Our feed-forward model provides an automated, scalable, and robust system that achieves precision: 0.41, recall: 0.61, AUROC: 0.80.
Identifiers
31258984PMC6568109What 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.