ArticleJournal of the American Medical Informatics Association : JAMIA
Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 61 citations in OpenAlex.
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- Extracting Temporal Expressions of First Seizure Onset from Epilepsy Patient Discharge Summaries.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records.The journal of allergy and clinical immunology. In practice · 2022Article
- Temporal disambiguation of relative temporal expressions in clinical texts.Frontiers in research metrics and analytics · 2022Article
- Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach.JMIR medical informatics · 2020Article
- Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Temporal information extraction from mental health records to identify duration of untreated psychosis.Journal of biomedical semantics · 2020Article
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- Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study.JMIR medical informatics · 2019Article
- Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries.Journal of biomedical informatics · 2019Article
- Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier.BMC bioinformatics · 2019Article
- Extraction of Temporal Information from Clinical Narratives.Journal of healthcare informatics research · 2019Article
- Early Identification of Childhood Asthma: The Role of Informatics in an Era of Electronic Health Records.Frontiers in pediatrics · 2019Review
- Achievability to Extract Specific Date Information for Cancer Research.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Methodological variations in lagged regression for detecting physiologic drug effects in EHR data.Journal of biomedical informatics · 2018Article
- A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts.BMC medical informatics and decision making · 2018Article
- Entity recognition from clinical texts via recurrent neural network.BMC medical informatics and decision making · 2017Article
- Correlating Lab Test Results in Clinical Notes with Structured Lab Data: A Case Study in HbA1c and Glucose.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain.Computer methods and programs in biomedicine · 2016Review
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundTemporal information detection systems have been developed by the Mayo Clinic for the 2012 i2b2 Natural Language Processing Challenge.
objectiveTo construct automated systems for EVENT/TIMEX3 extraction and temporal link (TLINK) identification from clinical text. MATERIALS AND
methodsThe i2b2 organizers provided 190 annotated discharge summaries as the training set and 120 discharge summaries as the test set. Our Event system used a conditional random field classifier with a variety of features including lexical information, natural language elements, and medical ontology. The TIMEX3 system employed a rule-based method using regular expression pattern match and systematic reasoning to determine normalized values. The TLINK system employed both rule-based reasoning and machine learning. All three systems were built in an Apache Unstructured Information Management Architecture framework.
resultsOur TIMEX3 system performed the best (F-measure of 0.900, value accuracy 0.731) among the challenge teams. The Event system produced an F-measure of 0.870, and the TLINK system an F-measure of 0.537.
conclusionsOur TIMEX3 system demonstrated good capability of regular expression rules to extract and normalize time information. Event and TLINK machine learning systems required well-defined feature sets to perform well. We could also leverage expert knowledge as part of the machine learning features to further improve TLINK identification performance.
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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.