ArticleClinical pharmacology and therapeutics2020
Predicting Inpatient Medication Orders From Electronic Health Record Data.
Article in Clinical pharmacology and therapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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
12 citing papers in PubMed.
- Graph representation and learning for drug administration prediction.BMC medical informatics and decision making · 2026Article
- A hybrid ML-PBPK digital twin framework for clinically interpretable readmission risk and drug exposure stratification in diabetes.Frontiers in digital health · 2026Article
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- Retrospective evaluation of a machine learning model to facilitate pharmacogenetic testing.BMJ digital health & AI · 2025Article
- Impact of computerised provider order entry on the quality and quantity of clinical information included with investigation requests: an interrupted time series analysis.BMJ open quality · 2023Article
- Transformer- and Generative Adversarial Network-Based Inpatient Traditional Chinese Medicine Prescription Recommendation: Development Study.JMIR medical informatics · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Rethinking PICO in the Machine Learning Era: ML-PICO.Applied clinical informatics · 2021Article
- Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review.Pancreas · 2021Review
- Assessing the International Transferability of a Machine Learning Model for Detecting Medication Error in the General Internal Medicine Clinic: Multicenter Preliminary Validation Study.JMIR medical informatics · 2021Article
- Analysis of the Impact of Medical Features and Risk Prediction of Acute Kidney Injury for Critical Patients Using Temporal Electronic Health Record Data With Attention-Based Neural Network.Frontiers in medicine · 2021Article
- A learning pharmacy practice enabled by the pharmacists' patient care process.Journal of the American Pharmacists Association : JAPhAArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine-learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most frequently ordered medications based on a patient's discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty-five percent of medications ordered by physicians were ranked in the sequence model's top-10 predictions (logistic model: 49%) and 75% ranked in the top-25 (logistic model: 69%). Ninety-three percent of the sequence model's top-10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the EHR.
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Registered trials
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