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

Journals

  1. Benda N, Rogers C, Sharma M, Narain W, Diamond L, Ancker J, Seier K, Stetson P, Sulieman L, Armstrong M, Peng Y. Identifying Nonpatient Authors of Patient Portal Secure Messages in Oncology: A Proof-of-Concept Demonstration of Natural Language Processing Methods. JCO Clinical Cancer Informatics 2022;(6) View
  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
  6. Catanuto G, Rocco N, Balafa K, Masannat Y, Karakatsanis A, Maglia A, Barry P, Pappalardo F, Nava M, Caruso F. Natural Language Processing to Extract Meaningful Information from a Corpus of Written Knowledge in Breast Cancer: Transforming Books into Data. Breast Care 2023;18(3):209 View
  7. Sim J, Huang X, Horan M, Stewart C, Robison L, Hudson M, Baker J, Huang I. Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. Artificial Intelligence in Medicine 2023;146:102701 View
  8. Lee Y, Bacchi S, Macri C, Tan Y, Casson R, Chan W. Ophthalmology Operation Note Encoding with Open-Source Machine Learning and Natural Language Processing. Ophthalmic Research 2023:928 View
  9. Rodriguez D, Chen J, Viswanadham R, Lawrence K, Mann D. Leveraging Machine Learning to Develop Digital Engagement Phenotypes of Users in a Digital Diabetes Prevention Program: An Evaluation Study (Preprint). JMIR AI 2023 View
  10. Xie F, Chang J, Luong T, Wu B, Lustigova E, Shrader E, Chen W. Identifying Symptoms Prior to Pancreatic Ductal Adenocarcinoma Diagnosis in Real-World Care Settings: Natural Language Processing Approach. JMIR AI 2024;3:e51240 View
  11. Sim J, Huang X, Horan M, Baker J, Huang I. Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review. Expert Review of Pharmacoeconomics & Outcomes Research 2024;24(4):467 View
  12. Yang T, Sucholutsky I, Jen K, Schonlau M. exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies. PeerJ Computer Science 2024;10:e1888 View
  13. Bitterman D, Downing A, Maués J, Lustberg M. Promise and Perils of Large Language Models for Cancer Survivorship and Supportive Care. Journal of Clinical Oncology 2024;42(14):1607 View
  14. Wang H, Alanis N, Haygood L, Swoboda T, Hoot N, Phillips D, Knowles H, Stinson S, Mehta P, Sambamoorthi U. Using natural language processing in emergency medicine health service research: A systematic review and meta‐analysis. Academic Emergency Medicine 2024;31(7):696 View
  15. Bryant A, Zamora‐Resendiz R, Dai X, Morrow D, Lin Y, Jungles K, Rae J, Tate A, Pearson A, Jiang R, Fritsche L, Lawrence T, Zou W, Schipper M, Ramnath N, Yoo S, Crivelli S, Green M. Artificial intelligence to unlock real‐world evidence in clinical oncology: A primer on recent advances. Cancer Medicine 2024;13(12) View