AIMC Journal:
Journal of the American Medical Informatics Association : JAMIA

Showing 351 to 360 of 493 articles

A graph-based method for reconstructing entities from coordination ellipsis in medical text.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Coordination ellipsis is a linguistic phenomenon abound in medical text and is challenging for concept normalization because of difficulty in recognizing elliptical expressions referencing 2 or more entities accurately. To resolve this bot...

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
OBJECTIVE: The goal of this study is to develop a robust Time Event Ontology (TEO), which can formally represent and reason both structured and unstructured temporal information.

Empirical assessment of bias in machine learning diagnostic test accuracy studies.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Machine learning (ML) diagnostic tools have significant potential to improve health care. However, methodological pitfalls may affect diagnostic test accuracy studies used to appraise such tools. We aimed to evaluate the prevalence and rep...

Development of the Gender, Sex, and Sexual Orientation ontology: Evaluation and workflow.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The study sought to create an integrated vocabulary system that addresses the lack of standardized health terminology in gender and sexual orientation.

Dr. Agent: Clinical predictive model via mimicked second opinions.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seekin...

Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological...

Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of curr...

Using word embeddings to improve the privacy of clinical notes.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In this work, we introduce a privacy technique for anonymizing clinical notes that guarantees all private health information is secured (including sensitive data, such as family history, that are not adequately covered by current technique...

Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We dev...

Spoken words as biomarkers: using machine learning to gain insight into communication as a predictor of anxiety.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The goal of this study was to explore whether features of recorded and transcribed audio communication data extracted by machine learning algorithms can be used to train a classifier for anxiety.