ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2011
Modeling drug exposure data in electronic medical records: an application to warfarin.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 18 citations in OpenAlex.
- Skin in the game: a review of computational models of the skin.BioData mining · 2025Review
- Ascertaining accurate exposure to aspirin and other antithrombotic medications using structured electronic health record data.Research and practice in thrombosis and haemostasis · 2024Article
- Overview of the 2022 n2c2 shared task on contextualized medication event extraction in clinical notes.Journal of biomedical informatics · 2023Review
- Extracting medication changes in clinical narratives using pre-trained language models.Journal of biomedical informatics · 2023Article
- Toward Understanding Clinical Context of Medication Change Events in Clinical Narratives.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Ascertainment of Aspirin Exposure Using Structured and Unstructured Large-scale Electronic Health Record Data.Medical care · 2019Article
- Using natural language processing methods to classify use status of dietary supplements in clinical notes.BMC medical informatics and decision making · 2018Article
- Clinical information extraction applications: A literature review.Journal of biomedical informatics · 2018Review
- A comparative study of different methods for automatic identification of clopidogrel-induced bleedings in electronic health records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- Secondary use of clinical data: the Vanderbilt approach.Journal of biomedical informatics · 2014Article
- Towards generating a patient's timeline: extracting temporal relationships from clinical notes.Journal of biomedical informatics · 2013Article
- Annotating temporal information in clinical narratives.Journal of biomedical informatics · 2013Article
- Electronic medical records as a tool in clinical pharmacology: opportunities and challenges.Clinical pharmacology and therapeutics · 2012Review
- Temporal reasoning over clinical text: the state of the art.Journal of the American Medical Informatics Association : JAMIAReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Identification of patients' drug exposure information is critical to drug-related research that is based on electronic medical records (EMRs). Drug information is often embedded in clinical narratives and drug regimens change frequently because of various reasons like intolerance or insurance issues, making accurate modeling challenging. Here, we developed an informatics framework to determine patient drug exposure histories from EMRs by combining natural language processing (NLP) and machine learning (ML) technologies. Our framework consists of three phases: 1) drug entity recognition - identifying drug mentions; 2) drug event detection - labeling drug mentions with a status (e.g., "on" or "stop"); and 3) drug exposure modeling - predicting if a patient is taking a drug at a given time using the status and temporal information associated with the mentions. We applied the framework to determine patient warfarin exposure at hospital admissions and achieved 87% precision, 79% recall, and an area under the receiver-operator characteristic curve of 0.93.
Indexed as
Identifiers
22195139PMC3243123W120687998What 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.