Download PDF
CC BY-NC-ND 4.0 · Yearb Med Inform 2023; 32(01): 244-252
DOI: 10.1055/s-0043-1768752
Section 10: Natural Language Processing
Synopsis

Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

Cyril Grouin
1   Université Paris Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France
,
Natalia Grabar
2   UMR8163 STL, CNRS, Université de Lille, Domaine du Pont-de-bois, 59653 Villeneuve-d'Ascq cedex, France
,
Section Editors for the IMIA Yearbook Section on Natural Language Processing › Author Affiliations
› Further Information
   Permissions and Reprints

Summary

Objectives: To analyse the content of publications within the medical Natural Language Processing (NLP) domain in 2022.

Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Results: Three best papers have been selected. We also propose an analysis of the content of the NLP publications in 2022, stressing on some of the topics.

Conclusion: The main trend in 2022 is certainly related to the availability of large language models, especially those based on Transformers, and to their use by non-NLP researchers. This leads to the democratization of the NLP methods. We also observe the renewal of interest to languages other than English, the continuation of research on information extraction and prediction, the massive use of data from social media, and the consideration of needs and interests of patients.

Keywords

Natural language processing - best paper - topics - issues - 2022


Publication History

Article published online:
26 December 2023

© 2023. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Nadkarni PM, Ohno-Machado L, Chapman WW. Natural Language Processing: an introduction. J Am Med Inform Assoc 2011 Sep-Oct;18(5):544-51. doi: 10.1136/amiajnl-2011-000464.
    PubMed
  • 2 Friedman C and Hripcsak G. Natural language processing and its future in medicine. Acad Med 1999 Aug;74(8):890-5. doi: 10.1097/00001888-199908000-00012.
    PubMed
  • 3 Phatak A, Savage DW, Ohle R, Smith J, Mago V. Medical text simplification using reinforcement learning (TESLEA): Deep learning-based text simplification approach. JMIR Med Inform 2022 Nov 18;10(11):e38095. doi: 10.2196/38095.
    PubMed
  • 4 Cui B, Wang J, Lin H, Zhang Y, Yang L, Xu B. Emotion-based reinforcement attention network for depression detection on social media: Algorithm development and validation. JMIR Med Inform 2022 Aug 9;10(8):e37818. doi: 10.2196/37818.
    PubMed
  • 5 Sima AC, Mendes de Farias R, Anisimova M, Dessimoz C, Robinson-Rechavi M, Zbinden E, et al. Bio-SODA UX: enabling natural language question answering over knowledge graphs with user disambiguation. Distrib Parallel Databases 2022;40(2-3):409-40. doi: 10.1007/s10619-022-07414-w.
    PubMed
  • 6 Kaur M, Costello J, Willis E, Kelm K, Reformat MZ, Bolduc FV. Deciphering the diversity of mental models in neurodevelopmental disorders: Knowledge graph representation of public data using natural language processing. J Med Internet Res 2022 Aug 5;24(8):e39888. doi: 10.2196/39888.
    PubMed
  • 7 Albahli S. Twitter sentiment analysis: An Arabic text mining approach based on COVID-19. Front Public Health 2022 Oct 10;10:966779. doi: 10.3389/fpubh.2022.966779.
    PubMed
  • 8 Alasmari A, Alhothali A, Allinjawi A. Hybrid machine learning approach for Arabic medical web page credibility assessment. Health Informatics J 2022 Jan-Mar;28(1):14604582211070998. doi: 10.1177/14604582211070998.
    PubMed
  • 9 Kabir MK, Islam M, Kabir ANB, Haque A, Rhaman MdK. Detection of depression severity using Bengali social media posts on mental health: Study using natural language processing techniques. JMIR Form Res 2022 Sep 28;6(9):e36118. doi: 10.2196/36118.
    PubMed
  • 10 Shuai Z, Xiaolin D, Jing Y, Yanni H, Meng C, Yuxin W, et al. Comparison of different feature extraction methods for applicable automated ICD coding. BMC Med Inform Decis Mak 2022 Jan 12;22(1):11. doi: 10.1186/s12911-022-01753-5.
    PubMed
  • 11 Sun Y, Gao D, Shen X, Li M, Nan J, Zhang W. Multilabel classification in patient-doctor dialogues with the RoBERTa-WWM-ext + CNN (robustly optimized bidirectional encoder representations from transformers pretraining approach with whole word masking extended combining a convolutional neural network) model: Named entity study. JMIR Med Inform 2022 Apr 21;10(4):e35606. doi: 10.2196/35606.
    PubMed
  • 12 Zhang L, Yang X, Li S, Liao T, Pan G. Answering medical questions in Chinese using automatically mined knowledge and deep neural networks: an end-to-end solution. BMC Bioinformatics 2022 Apr 15;23(1):136. doi: 10.1186/s12859-022-04658-2.
    PubMed
  • 13 An Y, Xia X, Chen X, Wu FX, Wang J. Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF. Artif Intell Med 2022 May;127:102282. doi: 10.1016/j.artmed.2022.102282.
    PubMed
  • 14 Li C, Ma K. Entity recognition of Chinese medical text based on multi-head self-attention combined with BILSTM-CRF. Math Biosci Eng 2022;19(3):2206–18.
    PubMed
  • 15 Guo S, Yang W, Han L, Song X, Wang G. A multi-layer soft lattice based model for Chinese clinical named entity recognition. BMC Med Inform Decis Mak 2022 Jul 30;22(1):201. doi: 10.1186/s12911-022-01924-4.
    PubMed
  • 16 Zhang H, Zhang J, Ni W, Jiang Y, Liu K, Sun D, et al. Transformer- and generative adversarial network-based inpatient traditional Chinese medicine prescription recommendation: Development study. JMIR Med Inform 2022 May 31;10(5):e35239. doi: 10.2196/35239.
    PubMed
  • 17 Kaas-Hansen BS, Placido D, Rodríguez CL, Thorsen-Meyer HC, Gentile S, Nielsen AP, et al. Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records. Basic Clin Pharmacol Toxicol 2022 Oct;131(4):282-93. doi: 10.1111/bcpt.13773.
    PubMed
  • 18 Van Olmen J, Van Nooten J, Philips H, Sollie A, Daelemans W. Predicting COVID-19 symptoms from free text in medical records using artificial intelligence: Feasibility study. JMIR Med Inform 2022 Apr 27;10(4):e37771. doi: 10.2196/37771.
    PubMed
  • 19 Salmi S, Mérelle S, Gilissen R, van der Mei R, Bhulai S. Detecting changes in help seeker conversations on a suicide prevention helpline during the COVID-19 pandemic: in-depth analysis using encoder representations from transformers. BMC Public Health 2022 Mar 18;22(1):530. doi: 10.1186/s12889-022-12926-2.
    PubMed
  • 20 Sauvayre R, Vernier J, Chauvière C. An analysis of French-language tweets about COVID-19 vaccines: Supervised learning approach. JMIR Med Inform 2022 May 17;10(5):e37831. doi: 10.2196/37831.
    PubMed
  • 21 Martin GL, Jouganous J, Savidan R, Bellec A, Goehrs C, Benkebil M, et al. Validation of artificial intelligence to support the automatic coding of patient adverse drug reaction reports, using nationwide pharmacovigilance data. Drug Saf2022 May;45(5):535-48. doi: 10.1007/s40264-022-01153-8.
    PubMed
  • 22 Ahne A, Khetan V, Tannier X, Rizvi MdIH, Czernichow T, et al. Extraction of explicit and implicit cause-effect relationships in patient-reported diabetes-related tweets from 2017 to 2021: Deep learning approach. JMIR Med Inform 2022 Jul 19;10(7):e37201. doi: 10.2196/37201.
    PubMed
  • 23 Falissard L, Morgand C, Ghosn W, Imbaud C, Bounebache K, Rey G. Neural translation and automated recognition of ICD-10 medical entities from natural language: Model development and performance assessment. JMIR Med Inform 2022 Apr 11;10(4):e26353. doi: 10.2196/26353.
    PubMed
  • 24 Gérardin C, Mageau A, Mékinian A, Tannier X, Carrat F. Construction of cohorts of similar patients from automatic extraction of medical concepts: Phenotype extraction study. JMIR Med Inform 2022 Dec 19;10(12):e42379. doi: 10.2196/42379.
    PubMed
  • 25 Schiappa R, Contu S, Culie D, Thamphya B, Chateau Y, Gal J, et al. RUBY: Natural language processing of french electronic medical records for breast cancer research. JCO Clin Cancer Inform 2022 Jul;6:e2100199. doi: 10.1200/CCI.21.00199.
    PubMed
  • 26 Blanc C, Bailly A, Francis E, Guillotin T, Jamal F, Wakim B, et al. FlauBERT vs. CamemBERT: Understanding patient's answers by a French medical chatbot. Artif Intell Med 2022 May;127:102264. doi: 10.1016/j.artmed.2022.102264.
    PubMed
  • 27 Lentzen M, Madan S, Lage-Rupprecht V, Kühnel L, Fluck J, Jacobs M, et al. Critical assessment of transformer-based AI models for German clinical notes. JAMIA Open 2022 Nov 15;5(4):ooac087. doi: 10.1093/jamiaopen/ooac087.
    PubMed
  • 28 Malamas N, Papangelou K, Symeonidis AL. Upon improving the performance of localized healthcare virtual assistants. Healthcare (Basel) 2022 Jan 4;10(1):99. doi: 10.3390/healthcare10010099..
    PubMed
  • 29 Jain V, Kashyap KL. Ensemble hybrid model for Hindi COVID-19 text classification with metaheuristic optimization algorithm. Multimed Tools Appl. 2023;82(11):16839-16859. doi: 10.1007/s11042-022-13937-2.
    PubMed
  • 30 Wang H, Gupta S, Singhal A, Muttreja P, Singh S, Sharma P, et al. An artificial intelligence chatbot for young people's sexual and reproductive health in India (SnehAI): Instrumental case study. J Med Internet Res 2022 Jan 3;24(1):e29969. doi: 10.2196/29969.
    PubMed
  • 31 Damier P, Henderson EJ, Romero-Imbroda J, Galimam L, Kronfeld N, Warnecke T. Impact of off-time on quality of life in Parkinson's patients and their caregivers: Insights from social media. Parkinsons Dis 2022 Dec 3;2022:1800567. doi: 10.1155/2022/1800567.
    PubMed
  • 32 Stracqualursi L, Agati P. Covid-19 vaccines in Italian public opinion: Identifying key issues using twitter and natural language processing. PLoS One 2022 Nov 17;17(11):e0277394. doi: 10.1371/journal.pone.0277394.
    PubMed
  • 33 Semeraro A, Vilella S, Ruffo G, Stella M. Emotional profiling and cognitive networks unravel how mainstream and alternative press framed AstraZeneca, Pfizer and COVID-19 vaccination campaigns. Sci Rep 2022 Aug 24;12(1):14445. doi: 10.1038/s41598-022-18472-6.
    PubMed
  • 34 Lanera C, Baldi I, Francavilla A, Barbieri E, Tramontan L, Scamarcia A, et al. A deep learning approach to estimate the incidence of infectious disease cases for routinely collected ambulatory records: The example of Varicella-Zoster. Int J Environ Res Public Health 2022 May 13;19(10):5959. doi: 10.3390/ijerph19105959.
    PubMed
  • 35 Kishimoto T, Nakamura H, Kano Y, Eguchi Y, Kitazawa M, Liang- KC, et al. Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology. Front Psychiatry 2022 Dec 1;13:954703. doi: 10.3389/fpsyt.2022.954703.
    PubMed
  • 36 Mashima Y, Tamura T, Kunikata J, Tada S, Yamada A, Tanigawa M, et al. Using natural language processing techniques to detect adverse events from progress notes due to chemotherapy. Cancer Inform 2022 Mar 22;21:11769351221085064. doi: 10.1177/11769351221085064.
    PubMed
  • 37 Kim Y, Kim JH, Lee JM, Jang MJ, Yum YJ, Kim, S et al. Sci Rep 2022 Aug 16;12(1):13847. doi: 10.1038/s41598-022-17806-8.
    PubMed
  • 38 Eom G, Yun S, Byeon H. Predicting the sentiment of South Korean Twitter users toward vaccination after the emergence of COVID-19 Omicron variant using deep learning-based natural language processing. Front Med (Lausanne) 2022 Sep 14;9:948917. doi: 10.3389/fmed.2022.948917.
    PubMed
  • 39 Tan WM, Teoh KH, Ganggayah MD, Taib NA, Zaini HS, Dhillon SK. Automated generation of synoptic reports from narrative pathology reports in university Malaya medical center using natural language processing. Diagnostics (Basel) 2022 Apr 1;12(4):879. doi: 10.3390/diagnostics12040879.
    PubMed
  • 40 Anetta K, Horak A, Wojakowski W, Wita K, Jadczyk T. Deep learning analysis of Polish electronic health records for diagnosis prediction in patients with cardiovascular diseases. J Pers Med 2022 May 25;12(6):869. doi: 10.3390/jpm12060869.
    PubMed
  • 41 Oliveira LESE, Peters AC, Pucca da Silva AM, Gebeluca CP, Gumiel YB, Cintho LMM, et al. SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for portuguese clinical NLP tasks. J Biomed Semantics 2022 May 8;13(1):13. doi: 10.1186/s13326-022-00269-1.
    PubMed
  • 42 Coutinho I, Martins B. Transformer-based models for ICD-10 coding of death certificates with Portuguese text. J Biomed Inform 2022 Dec;136:104232. doi: 10.1016/j.jbi.2022.104232.
    PubMed
  • 43 Ljajić A, Prodanović N, Medvecki D, Bašaragin B, Mitrović J. Uncovering the reasons behind COVID-19 vaccine hesitancy in Serbia: Sentiment-based topic modeling. J Med Internet Res 2022 Nov 17;24(11):e42261. doi: 10.2196/42261.
    PubMed
  • 44 Blanco A, Remmer S, Pérez A, Dalianis H, Casillas A. Implementation of specialised attention mechanisms: ICD-10 classification of gastrointestinal discharge summaries in English, Spanish and Swedish. J Biomed Inform 2022 Jun;130:104050. doi: 10.1016/j.jbi.2022.104050.
    PubMed
  • 45 Pabón OS, Montenegro O, Torrente M, González AR, Provencio M, Menasalvas E. Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach. PeerJ Comput Sci 2022 Mar 7;8:e913. doi: 10.7717/peerj-cs.913.
    PubMed
  • 46 Chiu C, Villena F, Martin K, Núñez F, Besa C, Dunstan J. Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish. Front Artif Intell 2022 Sep 21;5:970517. doi: 10.3389/frai.2022.970517.
    PubMed
  • 47 Mermer G, Özsezer G. Discussions about COVID-19 vaccination on Twitter in Turkey: Sentiment analysis. Disaster Med Public Health Prep 2022 Oct 13;17:e266. doi: 10.1017/dmp.2022.229.
    PubMed
  • 48 Yuan Z, Zhao Z, Sun H, Li J, Wang F, Yu S. CODER: Knowledge-infused cross-lingual medical term embedding for term normalization. J Biomed Inform 2022 Feb;126:103983. doi: 10.1016/j.jbi.2021.103983.
    PubMed
  • 49 Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proc of NAACL-HLT 2019, Minneapolis, Minnesota. Association for Computational Linguistics; 2019. p. 4171-86.
    PubMed
  • 50 Beltagy I, Lo K, Cohan A. SciBERT: A pretrained language model for scientific text. In: Proc of the 2019 Conf on Empirical Methods in Natural Language Processing and the 9th Inter Joint Conf on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, November 2019. Association for Computational Linguistics; 2019. p. 3615-20.
    PubMed
  • 51 Alsentzer E, Murphy J, Boag W, Weng WH, Jindi D, Naumann T, et al. Publicly available clinical BERT embeddings. In: Proc of the 2nd Clinical Natural Language Processing Workshop, 2019. p. 72-8.
    PubMed
  • 52 Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 2020 Feb 15;36(4):1234-40. doi: 10.1093/bioinformatics/btz682.
    PubMed
  • 53 Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, et al. Domain-specific language model pretraining for biomedical natural language processing. ACM Trans Comput Healthc 2020;3(1):1–23.
    PubMed
  • 54 Alrowili S, Shanker V. BioM-transformers: Building large biomedical language models with BERT, ALBERT and ELECTRA. In: Proc of the 20th Workshop on Biomedical Language Processing, Online, June 2021. Association for Computational Linguistics; 2021. p. 221-7.
    PubMed
  • 55 Le H, Vial L, Frej J, Segonne V, Coavoux M, Lecouteux B, et al. FlauBERT: Unsupervised language model pre-training for French. In: Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France, 2020. European Language Resources Association; 2020. p. 2479-90.
    PubMed
  • 56 Martin L, Muller B, Suárez PJO, Dupont Y, Romary L, Villemonte de La Clergerie E, et al. CamemBERT: a tasty French language model. In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Seattle / Virtual, United States, 2020.
    PubMed
  • 57 Cañete J, Chaperon G, Fuentes R, Ho JH, Kang H, and Pérez J. Spanish pre-trained bert model and evaluation data. In: PML4DC at ICLR 2020; 2020.
    PubMed
  • 58 de Vries W, van Cranenburgh A, Bisazza A, Caselli T, van Noord G, and Nissim M. Bertje MN: A dutch bert model. ArXiv, December 2019.
    PubMed
  • 59 Virtanen A, Kanerva J, Ilo R, Luoma J, Luotolahti J, Salakoski T, et al. Multilingual is not enough: BERT for Finnish. ArXiv, 2019.
    PubMed
  • 60 Polignano M, Basile P, de Gemmis M, Semeraro G, Basile V. AlBERTo: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on Tweets. In: Proceedings of the Sixth Italian Conference on Computational Linguistics (CliC-it 2019), volume 2481. CEUR; 2019.
    PubMed
  • 61 Parisi L, Francia S, and Magnani P. Umberto: an italian language model trained with whole word masking. https://github.com/musixmatchresearch/umberto. 2020.
    PubMed
  • 62 Souza F, Nogueira R, Lotufo R. Bertimbau: Pretrained bert models for Brazilian Portuguese. In Ricardo Cerri and Ronaldo C. Prati, editors, Intelligent Systems. Cham: Springer International Publishing; 2020. p. 403-17
    PubMed
  • 63 Shibayama N and Shinnou H. Construction and evaluation of Japanese sentence-BERT models. In Proc of the 35th Pacific Asia Conf on Language, Information and Computation, Shanghai, China, 11 2021. Association for Computational Lingustics; 2021. p. 731-8.
    PubMed
  • 64 Labrak Y, Bazoge A, Dufour R, Rouvier M, Morin E, Daille B, et al. DrBERT: A robust pre-trained model in French for biomedical and clinical domains. In: ACL, editor. Proc of the 61st Ann Meeting of the Ass for Comp Linguistics, Toronto (CA), Canada; 2023.
    PubMed
  • 65 Kawazoe Y, Shibata D, Shinohara E, Aramaki E, Ohe K. A clinical specific BERT developed using a huge Japanese clinical text corpus. PLoS One 2021 Nov 9;16(11):e0259763. doi: 10.1371/journal.pone.0259763.
    PubMed
  • 66 Portelli B, Scaboro S, Tonino R, Chersoni E, Santus E, Serra G. Monitoring user opinions and side effects on COVID-19 vaccines in the twittersphere: Infodemiology study of tweets. J Med Internet Res 2022 May 13;24(5):e35115. doi: 10.2196/35115.
    PubMed
  • 67 Xue H, Gong X, Stevens H. COVID-19 vaccine fact-checking posts on facebook: Observational study. J Med Internet Res 2022 Jun 21;24(6):e38423. doi: 10.2196/38423.
    PubMed
  • 68 Rouillard CJ, Nasser MA, Hu H, Roblin DW. Evaluation of a Natural Language Processing approach to identify social determinants of health in electronic health records in a diverse community cohort. Med Care 2022 Mar 1;60(3):248-55. doi: 10.1097/MLR.0000000000001683.
    PubMed
  • 69 Rathee S, MacMahon M, Liu A, Katritsis NM, Youssef G, Hwang W, et al. DILI C : An AI-based classifier to search for drug-induced liver injury literature. Front Genet 2022 Jun 29;13:867946. doi: 10.3389/fgene.2022.867946.
    PubMed
  • 70 Zaman S, Petri C, Vimalesvaran K, Howard J, Bharath A, Francis D, et al. Automatic diagnosis labeling of cardiovascular MRI by using semisupervised natural language processing of text reports. Radiol Artif Intell 2021 Nov 24;4(1):e210085. doi: 10.1148/ryai.210085.
    PubMed
  • 71 Bhasuran B. BioBERT and similar approaches for relation extraction. Methods Mol Biol 2022;2496:221-35. doi: 10.1007/978-1-0716-2305-3_12.
    PubMed
  • 72 Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al. Language models are few-shot learners. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, editors. Advances in Neural Information Processing Systems, volume 33. Curran Associates, Inc.; 2020. p. 1877-1901.
    PubMed
  • 73 Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform 2022 Nov 19;23(6):bbac409. doi: 10.1093/bib/bbac409.
    PubMed
  • 74 Sirrianni J, Sezgin E, Claman D, Linwood SL. Medical text prediction and suggestion using generative pretrained transformer models with dental medical notes. Methods Inf Med 2022 Dec;61(5-06):195-200. doi: 10.1055/a-1900-7351.
    PubMed
  • 75 Nath S, Marie A, Ellershaw S, Korot E, Keane PA. New meaning for NLP: the trials and tribulations of natural language processing with GPT-3 in ophthalmology. Br J Ophthalmol 2022 Jul;106(7):889-92. doi: 10.1136/bjophthalmol-2022-321141.
    PubMed
  • 76 Habibabadi SK, Haghighi PD, Burstein F, Buttery J. Vaccine adverse event mining of twitter conversations: 2-phase classification study. JMIR Med Inform 2022 Jun 16;10(6):e34305. doi: 10.2196/34305.
    PubMed
  • 77 Dirkson A, Verberne S, van Oortmerssen G, Gelderblom H, Kraaij W. How do others cope? Extracting coping strategies for adverse drug events from social media. J Biomed Inform 2023 Mar;139:104228. doi: 10.1016/j.jbi.2022.104228.
    PubMed
  • 78 Yu D, Vydiswaran VGV. An assessment of mentions of adverse drug events on social media with natural language processing: Model development and analysis. JMIR Med Inform 2022 Sep 28;10(9):e38140. doi: 10.2196/38140.
    PubMed
  • 79 Benítez-Andrades JA, Alija-Pérez JM, Vidal ME, Pastor-Vargas R, García-Ordás MR. Traditional machine learning models and bidirectional encoder representations from transformer (BERT)-based automatic classification of tweets about eating disorders: Algorithm development and validation study. JMIR Med Inform 2022 Feb 24;10(2):e34492. doi: 10.2196/34492.
    PubMed
  • 80 Metzler H, Baginski H, Niederkrotenthaler T, Garcia D. Detecting potentially harmful and protective suicide-related content on twitter: Machine learning approach. J Med Internet Res 2022 Aug 17;24(8):e34705. doi: 10.2196/34705.
    PubMed
  • 81 Merhbene G, Nath S, Puttick AR, Kurpicz-Briki M. Burnoutensemble: Augmented intelligence to detect indications for burnout in clinical psychology. Front Big Data 2022 Apr 5;5:863100. doi: 10.3389/fdata.2022.863100.
    PubMed
  • 82 Golder S, Weissenbacher D, O'Connor K, Hennessy S, Gross R, Gonzalez Hernandez G. Patient-reported reasons for switching or discontinuing statin therapy: A mixed methods study using social media. Drug Saf 2022 Sep;45(9):971-81. doi: 10.1007/s40264-022-01212-0.
    PubMed
  • 83 Sarker A, Nataraj N, Siu W, Li S, Jones CM, Sumner SA. Concerns among people who use opioids during the COVID-19 pandemic: a natural language processing analysis of social media posts. Subst Abuse Treat Prev Policy 2022 Mar 5;17(1):16. doi: 10.1186/s13011-022-00442-w.
    PubMed
  • 84 Sarker A, Al-Garadi MA, Ge Y, Nataraj N, Jones CM, Sumner SA. Signals of increasing co-use of stimulants and opioids from online drug forum data. Harm Reduct J 2022 May 25;19(1):51. doi: 10.1186/s12954-022-00628-2.
    PubMed
  • 85 Segura-Bedmar I, Camino-Perdones D, Guerrero-Aspizua S. Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts. BMC Bioinformatics 2022 Jul 6;23(1):263. doi: 10.1186/s12859-022-04810-y.
    PubMed
  • 86 Chen Z, Zhang H, Yang X, Wu S, He X, Xu J, et al. Assess the documentation of cognitive tests and biomarkers in electronic health records via natural language processing for Alzheimer's disease and related dementias. Int J Med Inform 2023 Feb;170:104973. doi: 10.1016/j.ijmedinf.2022.104973.
    PubMed
  • 87 Penfold RB, Carrell DS, Cronkite DJ, Pabiniak C, Dodd T, Glass AM, et al. Development of a machine learning model to predict mild cognitive impairment using natural language processing in the absence of screening. BMC Med Inform Decis Mak 2022 May 12;22(1):129. doi: 10.1186/s12911-022-01864-z.
    PubMed
  • 88 Syed ARP, Anbalagan R, Setlur AS, Karunakaran C, Shetty J, Kumar J, et al. Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers. BMC Bioinformatics 2022 Nov 18;23(1):496. doi: 10.1186/s12859-022-05050-w.
    PubMed
  • 89 Song J, Hobensack M, Bowles KH, McDonald MV, Cato K, Rossetti SC, et al. Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care. Biomed Inform 2022 Apr;128:104039. doi: 10.1016/j.jbi.2022.104039.
    PubMed
  • 90 Chen PF, Chen L, Lin YK, Li GH, Lai F, Lu CW, et al. Predicting postoperative mortality with deep neural networks and natural language processing: Model development and validation. JMIR Med Inform 2022 May 10;10(5):e38241. doi: 10.2196/38241.
    PubMed
  • 91 Uronen L, Salanterä S, Hakala K, Hartiala J, Moen H. Combining supervised and unsupervised named entity recognition to detect psychosocial risk factors in occupational health checks. Int J Med Inform 2022 Apr;160:104695. doi: 10.1016/j.ijmedinf.2022.104695.
    PubMed
  • 92 Wang N, Wang M, Zhou Y, Liu H, Wei L, Fei X, et al. Sequential data-based patient similarity framework for patient outcome prediction: Algorithm development. J Med Internet Res 2022 Jan 6;24(1):e30720. doi: 10.2196/30720.
    PubMed
  • 93 Fink MA, Kades K, ABischoff A, Moll M, Schnell M, Küchler M, et al. Deep learning-based assessment of oncologic outcomes from natural language processing of structured radiology reports. Radiol Artif Intell 2022 Jul 20;4(5):e220055. doi: 10.1148/ryai.220055.
    PubMed
  • 94 Burnett A, Chen N, Zeritis S, Ware S, McGillivray L, Shand F, et al. Machine learning algorithms to classify self-harm behaviours in New South Wales ambulance electronic medical records: A retrospective study. Int J Med Inform 2022 May;161:104734. doi: 10.1016/j.ijmedinf.2022.104734.
    PubMed
  • 95 Gillespie A, Reader TW. Online patient feedback as a safety valve: An automated language analysis of unnoticed and unresolved safety incidents. Risk Anal 2022 Aug 9. doi: 10.1111/risa.14002.
    PubMed
  • 96 Barnett J, Bjarnadóttir MV, Anderson D, Chen C. Understanding gender biases and differences in web-based reviews of sanctioned physicians through a machine learning approach: Mixed methods study. JMIR Form Res 2022 Sep 8;6(9):e34902. doi: 10.2196/34902.
    PubMed
  • 97 Seltzer EK, Guntuku SC, Lanza AL, Tufts C, Srinivas SK, Klinger EV, et al. Patient experience and satisfaction in online reviews of obstetric care: Observational study. JMIR Form Res 2022 Mar 31;6(3):e28379. doi: 10.2196/28379.
    PubMed
  • 98 Himmelstein G, Bates D, Zhou L. Examination of stigmatizing language in the electronic health record. JAMA Netw Open 2022 Jan 4;5(1):e2144967. doi: 10.1001/jamanetworkopen.2021.44967.
    PubMed
  • 99 Ferrari A, Pirrotta L, Bonciani M, Venturi G, Vainieri M. Higher readability of institutional websites drives the correct fruition of the abortion pathway: A cross-sectional study. PLoS One 2022 Nov 4;17(11):e0277342. doi: 10.1371/journal.pone.0277342.
    PubMed
  • 100 Saeed N, Naveed H. Medical terminology-based computing system: a lightweight post-processing solution for out-of-vocabulary multi-word terms. Front Mol Biosci 2022 Aug 12;9:928530. doi: 10.3389/fmolb.2022.928530.
    PubMed
  • 101 Hendawi R, Alian S, Li J. A smart mobile app to simplify medical documents and improve health literacy: System design and feasibility validation. JMIR Form Res 2022 Apr 1;6(4):e35069. doi: 10.2196/35069.
    PubMed
  • 102 Wang Z, Fan Y, Lv H, Deng S, Xie H, Zhang L, et al. The gap between self-rated health information literacy and internet health information-seeking ability for patients with chronic diseases in rural communities: Cross-sectional study. J Med Internet Res 2022 Jan 31;24(1):e26308. doi: 10.2196/26308.
    PubMed