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Early temporal characteristics of elderly patient cognitive impairment in electronic health records
BACKGROUND: The 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 p...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686236/ https://www.ncbi.nlm.nih.gov/pubmed/31391041 http://dx.doi.org/10.1186/s12911-019-0858-0 |
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author | Goudarzvand, Somaieh St. Sauver, Jennifer Mielke, Michelle M. Takahashi, Paul Y. Lee, Yugyung Sohn, Sunghwan |
author_facet | Goudarzvand, Somaieh St. Sauver, Jennifer Mielke, Michelle M. Takahashi, Paul Y. Lee, Yugyung Sohn, Sunghwan |
author_sort | Goudarzvand, Somaieh |
collection | PubMed |
description | BACKGROUND: The 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. METHODS: The 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. RESULTS: The 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. CONCLUSIONS: There 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. |
format | Online Article Text |
id | pubmed-6686236 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66862362019-08-12 Early temporal characteristics of elderly patient cognitive impairment in electronic health records Goudarzvand, Somaieh St. Sauver, Jennifer Mielke, Michelle M. Takahashi, Paul Y. Lee, Yugyung Sohn, Sunghwan BMC Med Inform Decis Mak Research BACKGROUND: The 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. METHODS: The 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. RESULTS: The 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. CONCLUSIONS: There 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. BioMed Central 2019-08-08 /pmc/articles/PMC6686236/ /pubmed/31391041 http://dx.doi.org/10.1186/s12911-019-0858-0 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Goudarzvand, Somaieh St. Sauver, Jennifer Mielke, Michelle M. Takahashi, Paul Y. Lee, Yugyung Sohn, Sunghwan Early temporal characteristics of elderly patient cognitive impairment in electronic health records |
title | Early temporal characteristics of elderly patient cognitive impairment in electronic health records |
title_full | Early temporal characteristics of elderly patient cognitive impairment in electronic health records |
title_fullStr | Early temporal characteristics of elderly patient cognitive impairment in electronic health records |
title_full_unstemmed | Early temporal characteristics of elderly patient cognitive impairment in electronic health records |
title_short | Early temporal characteristics of elderly patient cognitive impairment in electronic health records |
title_sort | early temporal characteristics of elderly patient cognitive impairment in electronic health records |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686236/ https://www.ncbi.nlm.nih.gov/pubmed/31391041 http://dx.doi.org/10.1186/s12911-019-0858-0 |
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