Literature DB >> 35308911

Cognitive Function Characterization Using Electronic Health Records Notes.

Adrienne Pichon1, Betina Idnay2,3,4, Karen Marder3,4, Rebecca Schnall2, Chunhua Weng1.   

Abstract

Cognitive impairment is a defining feature of neurological disorders such as Alzheimer's disease (AD), one of the leading causes of disability and mortality in the elderly population. Assessing cognitive impairment is important for diagnostic, clinical management, and research purposes. The Folstein Mini-Mental State Examination (MMSE) is the most common screening measure of cognitive function, yet this score is not consistently available in the electronic health records. We conducted a pilot study to extract frequently used concepts characterizing cognitive function from the clinical notes of AD patients in an Aging and Dementia clinical practice. Then we developed a model to infer the severity of cognitive impairment and created a subspecialized taxonomy for concepts associated with MMSE scores. We evaluated the taxonomy and the severity prediction model and presented example use cases of this model. ©2021 AMIA - All rights reserved.

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Year:  2022        PMID: 35308911      PMCID: PMC8861713     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  20 in total

1.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician.

Authors:  M F Folstein; S E Folstein; P R McHugh
Journal:  J Psychiatr Res       Date:  1975-11       Impact factor: 4.791

2.  ADO: a disease ontology representing the domain knowledge specific to Alzheimer's disease.

Authors:  Ashutosh Malhotra; Erfan Younesi; Michaela Gündel; Bernd Müller; Michael T Heneka; Martin Hofmann-Apitius
Journal:  Alzheimers Dement       Date:  2013-07-03       Impact factor: 21.566

3.  An Automated Approach to Identifying Patients with Dementia Using Electronic Medical Records.

Authors:  David B Reuben; Andrew S Hackbarth; Neil S Wenger; Zaldy S Tan; Lee A Jennings
Journal:  J Am Geriatr Soc       Date:  2017-02-02       Impact factor: 5.562

4.  Unstructured clinical documentation reflecting cognitive and behavioral dysfunction: toward an EHR-based phenotype for cognitive impairment.

Authors:  Andrea L Gilmore-Bykovskyi; Laura M Block; Lily Walljasper; Nikki Hill; Carey Gleason; Manish N Shah
Journal:  J Am Med Inform Assoc       Date:  2018-09-01       Impact factor: 4.497

5.  Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records.

Authors:  Nonie Alexander; Daniel C Alexander; Frederik Barkhof; Spiros Denaxas
Journal:  Stud Health Technol Inform       Date:  2020-06-16

6.  Age- and education-specific reference values for the Mini-Mental and modified Mini-Mental State Examinations derived from a non-demented elderly population.

Authors:  G Bravo; R Hébert
Journal:  Int J Geriatr Psychiatry       Date:  1997-10       Impact factor: 3.485

7.  Common Alzheimer's Disease Research Ontology: National Institute on Aging and Alzheimer's Association collaborative project.

Authors:  Lorenzo M Refolo; Heather Snyder; Charlene Liggins; Laurie Ryan; Nina Silverberg; Suzana Petanceska; Maria C Carrillo
Journal:  Alzheimers Dement       Date:  2012-07       Impact factor: 21.566

8.  The MMSE orientation for time domain is a strong predictor of subsequent cognitive decline in the elderly.

Authors:  Elizabeth Guerrero-Berroa; Xiaodong Luo; James Schmeidler; Michael A Rapp; Karen Dahlman; Hillel T Grossman; Vahram Haroutunian; Michal Schnaider Beeri
Journal:  Int J Geriatr Psychiatry       Date:  2009-12       Impact factor: 3.485

9.  Population-based norms for the Mini-Mental State Examination by age and educational level.

Authors:  R M Crum; J C Anthony; S S Bassett; M F Folstein
Journal:  JAMA       Date:  1993-05-12       Impact factor: 56.272

10.  Alzheimer's disease drug development pipeline: 2019.

Authors:  Jeffrey Cummings; Garam Lee; Aaron Ritter; Marwan Sabbagh; Kate Zhong
Journal:  Alzheimers Dement (N Y)       Date:  2019-07-09
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