ArticleBMC medical informatics and decision making2019
Early temporal characteristics of elderly patient cognitive impairment in electronic health records.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Real-world health care costs and resource utilization associated with mild cognitive impairment in the United States: A retrospective cohort study of commercial and Medicare data.Journal of managed care & specialty pharmacy · 2025Article
- Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review.JAMIA open · 2024Review
- Machine learning functional impairment classification with electronic health record data.Journal of the American Geriatrics Society · 2023Article
- Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Prediction of Incident Dementia Using Patient Temporal Health Status.Studies in health technology and informatics · 2022Article
- Detection of Dementia Signals from Longitudinal Clinical Visits Using One-Class Classification.Proceedings. IEEE International Conference on Healthcare Informatics · 2022Article
- Early Alert of Elderly Cognitive Impairment using Temporal Streaming Clustering.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- Deep Learning Prediction of Mild Cognitive Impairment using Electronic Health Records.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
- Selected articles from the Third International Workshop on Semantics-Powered Data Analytics (SEPDA 2018).BMC medical informatics and decision making · 2019Article
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6 authors.
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Abstract
backgroundThe aging population has led to an increase in cognitive impairment (CI) resulting in significant costs to patients, their families, and society. A research endeavor on a large cohort to better understand the frequency and severity of CI is urgent to respond to the health needs of this population. However, little is known about temporal trends of patient health functions (i.e., activity of daily living [ADL]) and how these trends are associated with the onset of CI in elderly patients. Also, the use of a rich source of clinical free text in electronic health records (EHRs) to facilitate CI research has not been well explored. The aim of this study is to characterize and better understand early signals of elderly patient CI by examining temporal trends of patient ADL and analyzing topics of patient medical conditions in clinical free text using topic models.
methodsThe study cohort consists of physician-diagnosed CI patients (n = 1,435) and cognitively unimpaired (CU) patients (n = 1,435) matched by age and sex, selected from patients 65 years of age or older at the time of enrollment in the Mayo Clinic Biobank. A corpus analysis was performed to examine the basic statistics of event types and practice settings where the physician first diagnosed CI. We analyzed the distribution of ADL in three different age groups over time before the development of CI. Furthermore, we applied three different topic modeling approaches on clinical free text to examine how patients' medical conditions change over time when they were close to CI diagnosis.
resultsThe trajectories of ADL deterioration became steeper in CI patients than CU patients approximately 1 to 1.5 year(s) before the actual physician diagnosis of CI. The topic modeling showed that the topic terms were mostly correlated and captured the underlying semantics relevant to CI when approaching to CI diagnosis.
conclusionsThere exist notable differences in temporal trends of basic and instrumental ADL between CI and CU patients. The trajectories of certain individual ADL, such as bathing and responsibility of own medication, were closely associated with CI development. The topic terms obtained by topic modeling methods from clinical free text have a potential to show how CI patients' conditions evolve and reveal overlooked conditions when they close to CI diagnosis.
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