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

Theme Issue: Medical Informatics and COVID-19 (107) JMIR Theme Issue: COVID-19 Special Issue (2452) Decision Support for Health Professionals (1185) Outbreak and Pandemic Preparedness and Management (1727) Clinical Information and Decision Making (1407) Natural Language Processing (692) Intensive Care Unit (ICU) (112) Electronic Health Records (1053)

Published on in Vol 9, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25457, first published .
Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing

Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing

Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing

Authors of this article:

Marta Fernandes1, 2, 3 Author Orcid Image ;   Haoqi Sun1, 2, 3 Author Orcid Image ;   Aayushee Jain1, 2 Author Orcid Image ;   Haitham S Alabsi1, 3 Author Orcid Image ;   Laura N Brenner3, 4, 5 Author Orcid Image ;   Elissa Ye1, 2 Author Orcid Image ;   Wendong Ge1, 2, 3 Author Orcid Image ;   Sarah I Collens1 Author Orcid Image ;   Michael J Leone1 Author Orcid Image ;   Sudeshna Das1, 3 Author Orcid Image ;   Gregory K Robbins3, 6 Author Orcid Image ;   Shibani S Mukerji1, 3 Author Orcid Image ;   M Brandon Westover1, 2, 3, 7 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

Journals

  1. Panaite V, Devendorf A, Finch D, Bouayad L, Luther S, Schultz S. The Value of Extracting Clinician-Recorded Affect for Advancing Clinical Research on Depression: Proof-of-Concept Study Applying Natural Language Processing to Electronic Health Records. JMIR Formative Research 2022;6(5):e34436 View
  2. Zanotto B, Beck da Silva Etges A, dal Bosco A, Cortes E, Ruschel R, De Souza A, Andrade C, Viegas F, Canuto S, Luiz W, Ouriques Martins S, Vieira R, Polanczyk C, André Gonçalves M. Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers. JMIR Medical Informatics 2021;9(11):e29120 View
  3. Fernandes M, Valizadeh N, Alabsi H, Quadri S, Tesh R, Bucklin A, Sun H, Jain A, Brenner L, Ye E, Ge W, Collens S, Lin S, Das S, Robbins G, Zafar S, Mukerji S, Brandon Westover M. Classification of neurologic outcomes from medical notes using natural language processing. Expert Systems with Applications 2023;214:119171 View
  4. Al-Garadi M, Yang Y, Sarker A. The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges. Healthcare 2022;10(11):2270 View
  5. Alshahrani S, Khan N. COVID-19 advising application development for Apple devices (iOS). PeerJ Computer Science 2023;9:e1274 View
  6. Ye J, Hai J, Wang Z, Wei C, Song J. Leveraging natural language processing and geospatial time series model to analyze COVID-19 vaccination sentiment dynamics on Tweets. JAMIA Open 2023;6(2) View
  7. Gandomi A, Wu P, Clement D, Xing J, Aviv R, Federbush M, Yuan Z, Jing Y, Wei G, Hajizadeh N. ARDSFlag: an NLP/machine learning algorithm to visualize and detect high-probability ARDS admissions independent of provider recognition and billing codes. BMC Medical Informatics and Decision Making 2024;24(1) View

Books/Policy Documents

  1. Riya , Rai S, Rupal , Rani R, Niranjan V, Sharma A. Information, Communication and Computing Technology. View