This paper is in the following e-collection/theme issue:

Advanced Data Analytics in eHealth (142) Electronic Health Records (1068) Natural Language Processing (706) Geroinformatics and Electronic Clinical Information/Decision Making in Geriatrics (22)

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

Authors of this article:

Tao Chen1 Author Orcid Image ;   Mark Dredze2 Author Orcid Image ;   Jonathan P Weiner3 Author Orcid Image ;   Leilani Hernandez4 Author Orcid Image ;   Joe Kimura4 Author Orcid Image ;   Hadi Kharrazi3, 5 Author Orcid Image
Article Authors Cited by (28) Tweetations (9) Metrics

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;24(5):601 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
  9. Newman-Griffis D, Camacho Maldonado J, Ho P, Sacco M, Jimenez Silva R, Porcino J, Chan L. Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing. Frontiers in Rehabilitation Sciences 2021;2 View
  10. Sarwar T, Seifollahi S, Chan J, Zhang X, Aksakalli V, Hudson I, Verspoor K, Cavedon L. The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges. ACM Computing Surveys 2023;55(2):1 View
  11. Chen L, Li N, Zheng Y, Gao L, Ge N, Xie D, Yue J. A novel semiautomatic Chinese keywords instrument screening delirium based on electronic medical records. BMC Geriatrics 2022;22(1) View
  12. Hatef E, Ma X, Shaikh Y, Kharrazi H, Weiner J, Gaskin D. Internet Access, Social Risk Factors, and Web-Based Social Support Seeking Behavior: Assessing Correlates of the “Digital Divide” Across Neighborhoods in The State of Maryland. Journal of Medical Systems 2021;45(11) View
  13. Maclagan L, Abdalla M, Harris D, Stukel T, Chen B, Candido E, Swartz R, Iaboni A, Jaakkimainen R, Bronskill S. Can Patients with Dementia Be Identified in Primary Care Electronic Medical Records Using Natural Language Processing?. Journal of Healthcare Informatics Research 2023;7(1):42 View
  14. Kharrazi H, Chang H, Weiner J, Gudzune K. Assessing the Added Value of Blood Pressure Information Derived from Electronic Health Records in Predicting Health Care Cost and Utilization. Population Health Management 2022;25(3):323 View
  15. Hatef E, Singh Deol G, Rouhizadeh M, Li A, Eibensteiner K, Monsen C, Bratslaver R, Senese M, Kharrazi H. Measuring the Value of a Practical Text Mining Approach to Identify Patients With Housing Issues in the Free-Text Notes in Electronic Health Record: Findings of a Retrospective Cohort Study. Frontiers in Public Health 2021;9 View
  16. Alkhalaf M, Zhang Z, Chang H, Wei W, Yin M, Deng C, Yu P. Malnutrition and its contributing factors for older people living in residential aged care facilities: Insights from natural language processing of aged care records. Technology and Health Care 2023;31(6):2267 View
  17. Linfield G, Patel S, Ko H, Lacar B, Gottlieb L, Adler-Milstein J, Singh N, Pantell M, De Marchis E. Evaluating the comparability of patient-level social risk data extracted from electronic health records: A systematic scoping review. Health Informatics Journal 2023;29(3) View
  18. Mehta S, Lyles C, Rubinsky A, Kemper K, Auerbach J, Sarkar U, Gottlieb L, Brown III W. Social Determinants of Health Documentation in Structured and Unstructured Clinical Data of Patients With Diabetes: Comparative Analysis. JMIR Medical Informatics 2023;11:e46159 View
  19. Silva R, Pollettini J, Pazin Filho A. Processamento de linguagem natural não supervisionado na identificação de pacientes suspeitos de infecção por COVID-19. Cadernos de Saúde Pública 2023;39(11) View
  20. Silva R, Pollettini J, Pazin Filho A. Unsupervised natural language processing in the identification of patients with suspected COVID-19 infection. Cadernos de Saúde Pública 2023;39(11) View
  21. Cheligeer C, Wu G, Lee S, Pan J, Southern D, Martin E, Sapiro N, Eastwood C, Quan H, Xu Y. BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study. JMIR Medical Informatics 2024;12:e48995 View
  22. Wieland-Jorna Y, van Kooten D, Verheij R, de Man Y, Francke A, Oosterveld-Vlug M. Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review. JAMIA Open 2024;7(2) View
  23. Morales-Sánchez R, Montalvo S, Riaño A, Martínez R, Velasco M. Early diagnosis of HIV cases by means of text mining and machine learning models on clinical notes. Computers in Biology and Medicine 2024;179:108830 View
  24. Osman M, Cooper R, Sayer A, Witham M. The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review. Age and Ageing 2024;53(7) View
  25. Nunes M, Bone J, Ferreira J, Elvas L. Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review. JMIR Medical Informatics 2024;12:e60164 View

Books/Policy Documents

  1. Tyagi N, Bhushan B. Enabling Technologies for Effective Planning and Management in Sustainable Smart Cities. View
  2. Kim H, Skurla M, Rahman A, Vahia I. The American Psychiatric Association Publishing Textbook of Geriatric Psychiatry. View
  3. Guellil I, Andres S, Guthrie B, Anand A, Zhang H, Hasan A, Wu H, Alex B. Natural Language Processing and Information Systems. View