ArticleJournal of the American Medical Informatics Association : JAMIA
N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.
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 27 papers.
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Who cites it
27 citing papers in PubMed, 83 citations in OpenAlex.
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- Impact of Different Approaches to Preparing Notes for Analysis With Natural Language Processing on the Performance of Prediction Models in Intensive Care.Critical care explorations · 2021Article
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.Informatics (MDPI) · 2021Article
- Applications of machine learning in acute care research.Journal of the American College of Emergency Physicians open · 2020Article
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- Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.JAMA network open · 2018Article
- Predicting adverse hemodynamic events in critically ill patients.Current opinion in critical care · 2018Review
- Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.BMC medical informatics and decision making · 2017Article
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- Interpretable Predictions of Clinical Outcomes with An Attention-based Recurrent Neural Network.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2017Article
- DeepDeath: Learning to predict the underlying cause of death with Big Data.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2017Article
- Content Coding of Psychotherapy Transcripts Using Labeled Topic Models.IEEE journal of biomedical and health informatics · 2017Article
- Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection.PloS one · 2017Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExisting risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times.
objectiveTo develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment. MATERIALS AND
methodsWe selected notes from 2001-2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx).
resultsOur classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task. DISCUSSION: Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures.
conclusionsSVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods.
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