ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014
Automatically Detecting Acute Myocardial Infarction Events from EHR Text: A Preliminary Study.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 5 citations in OpenAlex.
- Knowledge Graph Augmented Large Language Models for Disease Prediction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death.Journal of biomedical semantics · 2019Article
- Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning.JMIR public health and surveillance · 2018Article
- Generalized Extraction and Classification of Span-Level Clinical Phrases.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
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 at 2 institutions in 1 country.
Funding
Abstract
The Worcester Heart Attack Study (WHAS) is a population-based surveillance project examining trends in the incidence, in-hospital, and long-term survival rates of acute myocardial infarction (AMI) among residents of central Massachusetts. It provides insights into various aspects of AMI. Much of the data has been assessed manually. We are developing supervised machine learning approaches to automate this process. Since the existing WHAS data cannot be used directly for an automated system, we first annotated the AMI information in electronic health records (EHR). With strict inter-annotator agreement over 0.74 and un-strict agreement over 0.9 of Cohen's κ, we annotated 105 EHR discharge summaries (135k tokens). Subsequently, we applied the state-of-the-art supervised machine-learning model, Conditional Random Fields (CRFs) for AMI detection. We explored different approaches to overcome the data sparseness challenge and our results showed that cluster-based word features achieved the highest performance.
Indexed as
Identifiers
25954440PMC4419972W2417984089What 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.