ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2014
Automated physician order recommendations and outcome predictions by data-mining electronic medical records.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- An atomic approach to the design and implementation of a research data warehouse.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Integrating Clinical Knowledge and Real-World Evidence for Type 2 Diabetes Treatment.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Association model data learned from clinicians stratified by patient mortality outcomes at a Tertiary Academic Center.Data in brief · 2018Article
- An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes.Journal of biomedical informatics · 2018Article
- Inpatient Clinical Order Patterns Machine-Learned From Teaching Versus Attending-Only Medical Services.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.International journal of medical informatics · 2017Article
- Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Therapy Decision Support Based on Recommender System Methods.Journal of healthcare engineering · 2017Article
- Learning Doctors' Medicine Prescription Pattern for Chronic Disease Treatment by Mining Electronic Health Records: A Multi-Task Learning Approach.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- DYNAMICALLY EVOLVING CLINICAL PRACTICES AND IMPLICATIONS FOR PREDICTING MEDICAL DECISIONS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
- Data-Mining Electronic Medical Records for Clinical Order Recommendations: Wisdom of the Crowd or Tyranny of the Mob?AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
- Building the graph of medicine from millions of clinical narratives.Scientific data · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The meaningful use of electronic medical records (EMR) will come from effective clinical decision support (CDS) applied to physician orders, the concrete manifestation of clinical decision making. CDS development is currently limited by a top-down approach, requiring manual production and limited end-user awareness. A statistical data-mining alternative automatically extracts expertise as association statistics from structured EMR data (>5.4M data elements from >19K inpatient encounters). This powers an order recommendation system analogous to commercial systems (e.g., Amazon.com's "Customers who bought this…"). Compared to a standard benchmark, the association method improves order prediction precision from 26% to 37% (p<0.01). Introducing an inverse frequency weighted recall metric demonstrates a quantifiable improvement from 3% to 17% (p<0.01) in recommending more specifically relevant orders. The system also predicts clinical outcomes, such as 30 day mortality and 1 week ICU intervention, with ROC AUC of 0.88 and 0.78 respectively, comparable to state-of-the-art prognosis scores.
Identifiers
25717414PMC4333710What 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.