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

Methods and Instruments in Medical Informatics (185) Natural Language Processing (898) New Technologies (106) Data Science (355) Epublishing and Open Access (103) Information Retrieval (345) New Methods (1123)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54653, first published .
Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing–Assisted Intelligent Systematic Literature Review System

Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing–Assisted Intelligent Systematic Literature Review System

Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing–Assisted Intelligent Systematic Literature Review System

Authors of this article:

Frank J Manion1 Author Orcid Image ;   Jingcheng Du1 Author Orcid Image ;   Dong Wang2 Author Orcid Image ;   Long He1 ;   Bin Lin1 ;   Jingqi Wang1 Author Orcid Image ;   Siwei Wang1 Author Orcid Image ;   David Eckels2 Author Orcid Image ;   Jan Cervenka2 Author Orcid Image ;   Peter C Fiduccia2 Author Orcid Image ;   Nicole Cossrow2 Author Orcid Image ;   Lixia Yao2 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics Author Video

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  3. Rodionov D, Lyamin B, Konnikov E, Obukhova E, Golikov G, Polyakov P. Integration of Associative Tokens into Thematic Hyperspace: A Method for Determining Semantically Significant Clusters in Dynamic Text Streams. Big Data and Cognitive Computing 2025;9(8):197 View