ArticleJMIR medical informatics2020
Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models.
Article in JMIR medical informatics, 2020. 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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13 citing papers in PubMed.
- Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes Using a Local Large Language Model.Translational vision science & technology · 2026Article
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- The impact of diet and gut microbiota on development, treatment, and prognosis in prostate cancer.Frontiers in nutrition · 2025Review
- Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports of Lung Cancer Screening Patients Using Transformer Models.Journal of healthcare informatics research · 2024Article
- Deep Learning for Combating Misinformation in Multicategorical Text Contents.Sensors (Basel, Switzerland) · 2023Article
- Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2023Article
- Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.Journal of digital imaging · 2023Article
- A large language model for electronic health records.NPJ digital medicine · 2022Article
- The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing.JMIR medical informatics · 2022Article
- Identify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods.BMC medical informatics and decision making · 2022Article
- Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach.JMIR medical informatics · 2022Article
- The Value of Extracting Clinician-Recorded Affect for Advancing Clinical Research on Depression: Proof-of-Concept Study Applying Natural Language Processing to Electronic Health Records.JMIR formative research · 2022Article
- A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
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5 authors.
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Abstract
backgroundPatients' family history (FH) is a critical risk factor associated with numerous diseases. However, FH information is not well captured in the structured database but often documented in clinical narratives. Natural language processing (NLP) is the key technology to extract patients' FH from clinical narratives. In 2019, the National NLP Clinical Challenge (n2c2) organized shared tasks to solicit NLP methods for FH information extraction.
objectiveThis study presents our end-to-end FH extraction system developed during the 2019 n2c2 open shared task as well as the new transformer-based models that we developed after the challenge. We seek to develop a machine learning-based solution for FH information extraction without task-specific rules created by hand.
methodsWe developed deep learning-based systems for FH concept extraction and relation identification. We explored deep learning models including long short-term memory-conditional random fields and bidirectional encoder representations from transformers (BERT) as well as developed ensemble models using a majority voting strategy. To further optimize performance, we systematically compared 3 different strategies to use BERT output representations for relation identification.
resultsOur system was among the top-ranked systems (3 out of 21) in the challenge. Our best system achieved micro-averaged F1 scores of 0.7944 and 0.6544 for concept extraction and relation identification, respectively. After challenge, we further explored new transformer-based models and improved the performances of both subtasks to 0.8249 and 0.6775, respectively. For relation identification, our system achieved a performance comparable to the best system (0.6810) reported in the challenge.
conclusionsThis study demonstrated the feasibility of utilizing deep learning methods to extract FH information from clinical narratives.
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