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

Patient-Reported Outcome Measures (PROMs) (237) Natural Language Processing (507) Machine Learning (1054) Ontologies, Classifications, and Coding (165) Artificial Intelligence (715) Pain in Children and Adolescents (35) Children and Young People With Cancer, Pediatric Oncology (41)

Published on in Vol 23 , No 11 (2021) :November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26777, first published .
Natural Language Processing and Machine Learning Methods to Characterize Unstructured Patient-Reported Outcomes: Validation Study

Natural Language Processing and Machine Learning Methods to Characterize Unstructured Patient-Reported Outcomes: Validation Study

Natural Language Processing and Machine Learning Methods to Characterize Unstructured Patient-Reported Outcomes: Validation Study

Authors of this article:

Zhaohua Lu 1 Author Orcid Image ;   Jin-ah Sim 2, 3 Author Orcid Image ;   Jade X Wang 1 Author Orcid Image ;   Christopher B Forrest 4 Author Orcid Image ;   Kevin R Krull 2 Author Orcid Image ;   Deokumar Srivastava 1 Author Orcid Image ;   Melissa M Hudson 5 Author Orcid Image ;   Leslie L Robison 2 Author Orcid Image ;   Justin N Baker 5 Author Orcid Image ;   I-Chan Huang 2 Author Orcid Image
Article Authors Cited by (5) Tweetations (34) Metrics

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  2. Horan M, Sim J, Krull K, Ness K, Yasui Y, Robison L, Hudson M, Baker J, Huang I. Ten Considerations for Integrating Patient-Reported Outcomes into Clinical Care for Childhood Cancer Survivors. Cancers 2023;15(4):1024 View
  3. Li S, Deng L, Zhang X, Chen L, Yang T, Qi Y, Jiang T. Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation. Journal of Medical Internet Research 2022;24(6):e37213 View
  4. Ardahan Sevgili S, Şenol S. Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations. Pediatric Research 2023;93(2):390 View
  5. Wang J, Li Y, Li X, Lu Z. Alzheimer's Disease Classification Through Imaging Genetic Data With IGnet. Frontiers in Neuroscience 2022;16 View