Published on in Vol 7, No 1 (2019): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13039, first published .
Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods

Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods

Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods

Journals

  1. Hatef E, Rouhizadeh M, Tia I, Lasser E, Hill-Briggs F, Marsteller J, Kharrazi H. Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis of a Multilevel Health Care System. JMIR Medical Informatics 2019;7(3):e13802 View
  2. Bery A, Anzaldi L, Boyd C, Leff B, Kharrazi H. Potential value of electronic health records in capturing data on geriatric frailty for population health. Archives of Gerontology and Geriatrics 2020;91:104224 View
  3. Ambagtsheer R, Shafiabady N, Dent E, Seiboth C, Beilby J. The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set. International Journal of Medical Informatics 2020;136:104094 View
  4. Chen T, Dredze M, Weiner J, Kharrazi H. Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records. Journal of the American Medical Informatics Association 2019;26(8-9):787 View
  5. Chen X, Xie H, Cheng G, Poon L, Leng M, Wang F. Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis. Applied Sciences 2020;10(6):2157 View
  6. Sai Prashanthi G, Deva A, Vadapalli R, Das A. Automated Categorization of Systemic Disease and Duration From Electronic Medical Record System Data Using Finite-State Machine Modeling: Prospective Validation Study. JMIR Formative Research 2020;4(12):e24490 View
  7. Kharrazi H, Ma X, Chang H, Richards T, Jung C. Comparing the Predictive Effects of Patient Medication Adherence Indices in Electronic Health Record and Claims-Based Risk Stratification Models. Population Health Management 2021 View
  8. Newman-Griffis D, Fosler-Lussier E. Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health. Frontiers in Digital Health 2021;3 View