ArticleJMIR medical informatics2017
Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies.
Article in JMIR medical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Racial differences in venous thromboembolism: A surveillance program in Durham County, North Carolina.Research and practice in thrombosis and haemostasis · 2022Article
- Exploring the Applicability of Using Natural Language Processing to Support Nationwide Venous Thromboembolism Surveillance: Model Evaluation Study.JMIR bioinformatics and biotechnology · 2022Article
- Natural language processing in low back pain and spine diseases: A systematic review.Frontiers in surgery · 2022Review
- A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Adapting State-of-the-Art Deep Language Models to Clinical Information Extraction Systems: Potentials, Challenges, and Solutions.JMIR medical informatics · 2019Article
- Improved Identification of Venous Thromboembolism From Electronic Medical Records Using a Novel Information Extraction Software Platform.Medical care · 2018Article
- Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore.JMIR medical informatics · 2018Article
- Automated Information Extraction on Treatment and Prognosis for Non-Small Cell Lung Cancer Radiotherapy Patients: Clinical Study.JMIR medical informatics · 2018Article
- Automatic extraction of 12 cardiovascular concepts from German discharge letters using pre-trained language models.Digital healthArticle
- Clinical concept annotation with contextual word embedding in active transfer learning environment.Digital healthArticle
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Authors and funding
6 authors.
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Abstract
backgroundExtracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedbacks for improving the extraction algorithm in real time.
objectiveOur goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results.
methodsA clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedbacks to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction.
resultsThree datasets were used for experiments based on report styles: 100 cardiac catheterization procedure reports, 100 coronary angiographic reports, and 100 integrated reports-each combines history and physical report, discharge summary, outpatient clinic notes, outpatient clinic letter, and inpatient discharge medication report. Data extraction was performed by 3 methods: online machine learning, controlled vocabularies, and a combination of these. The system delivers results with F1 scores greater than 95%.
conclusionsIDEAL-X adopts a unique online machine learning-based approach combined with controlled vocabularies to support data extraction for clinical reports. The system can quickly learn and improve, thus it is highly adaptable.
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