Download PDF
CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 243-253
DOI: 10.1055/s-0042-1742510
Section 10: Natural Language Processing
Survey

Natural Language Processing: from Bedside to Everywhere

Eiji Aramaki
1   Nara Institute of Science and Technology (NAIST), Nara, Japan
,
Shoko Wakamiya
1   Nara Institute of Science and Technology (NAIST), Nara, Japan
,
Shuntaro Yada
1   Nara Institute of Science and Technology (NAIST), Nara, Japan
,
Yuta Nakamura
2   Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
› Author Affiliations
› Further Information
   Permissions and Reprints

Summary

Objectives: Owing to the rapid progress of natural language processing (NLP), the role of NLP in the medical field has radically gained considerable attention from both NLP and medical informatics. Although numerous medical NLP papers are published annually, there is still a gap between basic NLP research and practical product development. This gap raises questions, such as what has medical NLP achieved in each medical field, and what is the burden for the practical use of NLP? This paper aims to clarify the above questions.

Methods: We explore the literature on potential NLP products/services applied to various medical/clinical/healthcare areas.

Results: This paper introduces clinical applications (bedside applications), in which we introduce the use of NLP for each clinical department, internal medicine, pre-surgery, post-surgery, oncology, radiology, pathology, psychiatry, rehabilitation, obstetrics, and gynecology. Also, we clarify technical problems to be addressed for encouraging bedside applications based on NLP.

Conclusions: These results contribute to discussions regarding potentially feasible NLP applications and highlight research gaps for future studies.

Keywords

Natural language processing - medical application - chatbot - randomized controlled trial - social media


Publication History

Article published online:
02 June 2022

© 2022. 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 Agurto C, Cecchi GA, Norel R, Ostrand R, Kirkpatrick M, Baggott MJ, et al. Detection of acute 3,4-methylenedioxymethamphetamine (MDMA) effects across protocols using automated natural language processing. Neuropsychopharmacology.2020 Apr;45(5):823-2.
    PubMed
  • 2 Pestian JP, Grupp-Phelan J, Bretonnel Cohen K, Meyers G, Richey LA, Matykiewicz P, et al. A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department. Suicide Life Threat Behav 2016 Apr;46(2):154-9.
    PubMed
  • 3 Boyé M, Grabar N, Thi Tran M. Contrastive conversational analysis of language production by Alzheimer's and control people. Stud Health Technol Inform 2014;205:682-6.
    PubMed
  • 4 Xia Z, Secor E, Chibnik LB, Bove RM, Cheng S, Chitnis T, et al. Modeling disease severity in multiple sclerosis using electronic health records. PLoS One 2013 Nov 11;8(11):e78927.
    PubMed
  • 5 Mendonça EA, Haas J, Shagina L, Larson E, Friedman C. Extracting information on pneumonia in infants using natural language processing of radiology reports. J Biomed Inform 2005 Aug;38(4):314-21.
    PubMed
  • 6 Ananthakrishnan AN, Cai T, Savova G, Cheng SC, Chen P, Perez RG, et al. Improving case definition of Crohn's disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach. Inflamm Bowel Dis 2013 Jun;19(7):1411-20.
    PubMed
  • 7 Shiner B, Neily J, Mills PD, Watts BV. Identification of Inpatient Falls Using Automated Review of Text-Based Medical Records. J Patient Saf 2020 Sep;16(3):e174-e178.
    PubMed
  • 8 Salmasian H, Freedberg DE, Abrams JA, Friedman C. An automated tool for detecting medication overuse based on the electronic health records. Pharmacoepidemiol Drug Saf 2013 Feb;22(2):183-9.
    PubMed
  • 9 Chapman WW, Nadkarni PM, Hirschman L, D'Avolio LW, Savova GK, Uzuner Ö. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions. J Am Med Inform Assoc 2011 Sep-Oct;18(5):540-3.
    PubMed
  • 10 Uzuner Ö, Goldstein I, Luo Y, Kohane I. Identifying patient smoking status from medical discharge records. J Am Med Inform Assoc 2008 Jan-Feb;15(1):14-24.
    PubMed
  • 11 Sun W, Rumshisky A, Uzuner Ö. Evaluating temporal relations in clinical text: 2012 i2b2 Challenge. J Am Med Inform Assoc 2013 Sep-Oct;20(5):806-13.
    PubMed
  • 12 Stubbs A, Kotfila C, Xu H, Uzuner Ö. Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2. J Biomed Inform 2015 Dec;58 Suppl(Suppl):S67-S77.
    PubMed
  • 13 Roberts K, Demner-Fushman D, Voorhees EM, Bedrick S, Hersh WR. Overview of the TREC 2020 Precision Medicine Track. Text Retr Conf 2020 Nov;1266.
    PubMed
  • 14 Roberts K, Demner-Fushman D, Voorhees EM, Hersh WR, Bedrick S, Lazar AJ, et al. Overview of the TREC 2019 Precision Medicine Track. Text Retr Conf 2019 Nov;1250.
    PubMed
  • 15 Robert K, Demner-Fushman D, Voorhees EM, Hersh WR, Bedrick S, Lazar AJ, et al. Overview of the TREC 2017 Precision Medicine Track. Text Retr Conf 2017 Nov;26.
    PubMed
  • 16 Zeng Z, Deng Y, Li X, Naumann T, Luo Y. Natural Language Processing for EHR-Based Computational Phenotyping. IEEE/ACM Trans Comput Biol Bioinform 2019 Jan-Feb;16(1):139-53.
    PubMed
  • 17 Ross EG, Shah N, Leeper N. Statin Intensity or Achieved LDL? Practice-based Evidence for the Evaluation of New Cholesterol Treatment Guidelines. PLoS One 2016 May 26;11(5):e0154952.
    PubMed
  • 18 Leeper NJ, Bauer-Mehren A, Iyer SV, Lependu P, Olson C, Shah NH. Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes. PLoS One 2013 May 23;8(5):e63499.
    PubMed
  • 19 Smith DH, Johnson ES, Russell A, Hazlehurst B, Muraki C, Nichols GA, et al. Lower visual acuity predicts worse utility values among patients with type 2 diabetes. Qual Life Res 2008 Dec;17(10):1277-84.
    PubMed
  • 20 Norman CR, Leeflang MMG, Porcher R, Névéol A. Measuring the impact of screening automation on meta-analyses of diagnostic test accuracy. Syst Rev 2019 Oct 28;8(1):243.
    PubMed
  • 21 Gartlehner G, Wagner G, Lux L, Affengruber L, Dobrescu A, Kaminski-Hartenthaler A, Viswanathan M. Assessing the accuracy of machine-assisted abstract screening with DistillerAI: a user study. Syst Rev 2019 Nov 15;8(1):277.
    PubMed
  • 22 Schmidt L, Olorisade BK, McGuinness LA, Thomas J, Higgins JPT. Data extraction methods for systematic review (semi)automation: A living review protocol. F1000Res 2020 Mar 25;9:210.
    PubMed
  • 23 Zimmerman J, Soler RE, Lavinder J, Murphy S, Atkins C, Hulbert L, et al. Iterative guided machine learning-assisted systematic literature reviews: a diabetes case study. Syst Rev 2021 Apr 2;10(1):97.
    PubMed
  • 24 Wissel BD, Greiner HM, Glauser TA, Holland-Bouley KD, Mangano FT, Santel D, et al. Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery. Epilepsia 2020 Jan;61(1):39-48.
    PubMed
  • 25 Seol HY, Shrestha P, Muth JF, Wi CI, Sohn S, Ryu E, et al. Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial. PLoS One 2021 Aug 2;16(8):e0255261.
    PubMed
  • 26 Zheng L, Wang Y, Hao S, Shin AY, Jin B, Ngo AD, et al. Web-based Real-Time Case Finding for the Population Health Management of Patients With Diabetes Mellitus: A Prospective Validation of the Natural Language Processing-Based Algorithm With Statewide Electronic Medical Records. JMIR Med Inform 2016 Nov 11;4(4):e37.
    PubMed
  • 27 Wang Y, Luo J, Hao S, Xu H, Shin AY, Jin B, et al. NLP based congestive heart failure case finding: A prospective analysis on statewide electronic medical records. Int J Med Inform 2015 Dec;84(12):1039-47.
    PubMed
  • 28 Tian Z, Sun S, Eguale T, Rochefort CM. Automated Extraction of VTE Events From Narrative Radiology Reports in Electronic Health Records: A Validation Study. Med Care 2017 Oct;55(10):e73-e80.
    PubMed
  • 29 Bucher BT, Shi J, Ferraro JP, Skarda DE, Samore MH, Hurdle JF, et al. Portable Automated Surveillance of Surgical Site Infections Using Natural Language Processing: Development and Validation. Ann Surg 2020 Oct;272(4):629-36.
    PubMed
  • 30 Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Med Inform 2019 Apr 27;7(2):e12239.
    PubMed
  • 31 Shi X, Hu Y, Zhang Y, Li W, Hao Y, Alelaiwi A, et al. Multiple disease risk assessment with uniform model based on medical clinical notes. IEEE Access 2016;4:7074”83.
    PubMed
  • 32 Watson AJ, O'Rourke J, Jethwani K, Cami A, Stern TA, Kvedar JC, et al. Linking electronic health record-extracted psychosocial data in real-time to risk of readmission for heart failure. Psychosomatics. 2011 Jul-Aug;52(4):319-27.
    PubMed
  • 33 Wang SV, Rogers JR, Jin Y, Bates DW, Fischer MA. Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention. J Am Med Inform Assoc 2017 Mar 1;24(2):339-44.
    PubMed
  • 34 Buchan K, Filannino M, Uzuner Ö. Automatic prediction of coronary artery disease from clinical narratives. J Biomed Inform 2017 Aug;72:23-32.
    PubMed
  • 35 Chase HS, Mitrani LR, Lu GG, Fulgieri DJ. Early recognition of multiple sclerosis using natural language processing of the electronic health record. BMC Med Inform Decis Mak 2017 Feb 28;17(1):24.
    PubMed
  • 36 Mellia JA, Basta MN, Toyoda Y, Othman S, Elfanagely O, Morris MP, et al. Natural Language Processing in Surgery: A Systematic Review and Meta-analysis. Ann Surg 2021 May 1;273(5):900-8.
    PubMed
  • 37 Wyatt JM, Booth GJ, Goldman AH. Natural Language Processing and Its Use in Orthopaedic Research. Curr Rev Musculoskelet Med 2021 Dec;14(6):392-6.
    PubMed
  • 38 Fonferko-Shadrach B, Lacey AS, Roberts A, Akbari A, Thompson S, Ford DV, et al. Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system. BMJ Open 2019 Apr 1;9(4):e023232.
    PubMed
  • 39 Selby LV, Narain WR, Russo A, Strong VE, Stetson P. Autonomous detection, grading, and reporting of postoperative complications using natural language processing. Surgery 2018 Dec;164(6):1300-5.
    PubMed
  • 40 Borjali A, Magnéli M, Shin D, Malchau H, Muratoglu OK, Varadarajan KM. Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement dislocation. Comput Biol Med 2021 Feb;129:104140.
    PubMed
  • 41 Tibbo ME, Wyles CC, Fu S, Sohn S, Lewallen DG, Berry DJ, et al. Use of Natural Language Processing Tools to Identify and Classify Periprosthetic Femur Fractures. J Arthroplasty 2019 Oct;34(10):2216-9
    PubMed
  • 42 Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 2020 Dec 9;4(1):24.
    PubMed
  • 43 Datta S, Bernstam EV, Roberts K. A frame semantic overview of NLP-based information extraction for cancer-related EHR notes. J Biomed Inform 2019 Dec;100:103301.
    PubMed
  • 44 Tucker TC, Durbin EB, McDowell JK, Huang B. Unlocking the potential of population-based cancer registries. Cancer 2019 Nov 1;125(21):3729-3737.
    PubMed
  • 45 Holmes B, Chitale D, Loving J, Tran M, Subramanian V, Berry A, et al. Customizable Natural Language Processing Biomarker Extraction Tool. JCO Clin Cancer Inform 2021 Aug;5:833-41.
    PubMed
  • 46 Aronson AR. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp 2001:17-21.
    PubMed
  • 47 Xu J, Yang P, Xue S, Sharma B, Sanchez-Martin M, Wang F, et al. Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives. Hum Genet 2019 Feb;138(2):109-24.
    PubMed
  • 48 Li J, Chen H, Wang Y, Chen MM, Liang H. Next-Generation Analytics for Omics Data. Cancer Cell 2021 Jan 11;39(1):3-6.
    PubMed
  • 49 Pons E, Braun LM, Hunink MG, Kors JA. Natural Language Processing in Radiology: A Systematic Review. Radiology 2016 May;279(2):329-43.
    PubMed
  • 50 Casey A, Davidson E, Poon M, Dong H, Duma D, Grivas A, et al. A systematic review of natural language processing applied to radiology reports. BMC Med Inform Decis Mak 2021 Jun 3;21(1):179.
    PubMed
  • 51 Davidson EM, Poon MTC, Casey A, Grivas A, Duma D, Dong H, et al. The reporting quality of natural language processing studies: systematic review of studies of radiology reports. BMC Med Imaging 2021 Oct 2;21(1):142.
    PubMed
  • 52 Valtchinov VI, Lacson R, Wang A, Khorasani R. Comparing Artificial Intelligence Approaches to Retrieve Clinical Reports Documenting Implantable Devices Posing MRI Safety Risks. J Am Coll Radiol 2020 Feb;17(2):272-9.
    PubMed
  • 53 Trivedi H, Mesterhazy J, Laguna B, Vu T, Sohn JH. Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson's Natural Language Processing Algorithm. J Digit Imaging 2018 Apr;31(2):245-251.
    PubMed
  • 54 Chillakuru YR, Munjal S, Laguna B, Chen TL, Chaudhari GR, Vu T, et al. Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing. BMC Med Inform Decis Mak 2021 Jul 12;21(1):213.
    PubMed
  • 55 DSS Inc. Radiology Decision Support (RadWise®). Available from: https://www.dssinc.com/products/integrated-clinical-products/radwise-radiology-decision-support/
    PubMed
  • 56 Letourneau-Guillon L, Camirand D, Guilbert F, Forghani R. Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics. Neuroimaging Clin N Am 2020 Nov;30(4):e1-e15.
    PubMed
  • 57 Kim Y, Lee JH, Choi S, Lee JM, Kim JH, Seok J, et al. Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records. Sci Rep 2020 Nov 20;10(1):20265.
    PubMed
  • 58 Monshi MMA, Poon J, Chung V. Deep learning in generating radiology reports: A survey. Artif Intell Med 2020 Jun;106:101878.
    PubMed
  • 59 Tizhoosh HR, Diamandis P, Campbell CJV, Safarpoor A, Kalra S, Maleki D, et al. Searching Images for Consensus: Can AI Remove Observer Variability in Pathology? Am J Pathol 2021 Oct;191(10):1702-8.
    PubMed
  • 60 Pavlopoulos J, Kougia V, Androutsopoulos I. A survey on biomedical image captioning. In: Proceedings of the second workshop on shortcomings in vision and language; 2019. p.26-36.
    PubMed
  • 61 Odisho AY, Park B, Altieri N, DeNero J, Cooperberg MR, Carroll PR, Yu B. Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation. JAMIA Open 2020 Oct 14;3(3):431-8.
    PubMed
  • 62 Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers); 2019. p. 4171”86.
    PubMed
  • 63 DeSouza DD, Robin J, Gumus M, Yeung A. Natural Language Processing as an Emerging Tool to Detect Late-Life Depression. Front Psychiatry 2021 Sep 6;12:719125.
    PubMed
  • 64 Le Glaz A, Haralambous Y, Kim-Dufor DH, Lenca P, Billot R, Ryan TC, et al. Machine Learning and Natural Language Processing in Mental Health: Systematic Review. J Med Internet Res 2021 May 4;23(5):e15708.
    PubMed
  • 65 Jan Z, Ai-Ansari N, Mousa O, Abd-Alrazaq A, Ahmed A, Alam T, et al. The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review. J Med Internet Res 2021 Nov 19;23(11):e29749.
    PubMed
  • 66 Graham SA, Lee EE, Jeste DV, Van Patten R, Twamley EW, Nebeker C, et al. Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review. Psychiatry Res 2020 Feb;284:112732.
    PubMed
  • 67 Petti U, Baker S, Korhonen A. A systematic literature review of automatic Alzheimer's disease detection from speech and language. J Am Med Inform Assoc 2020 Nov 1;27(11):1784-97.
    PubMed
  • 68 de la Fuente Garcia S, Ritchie CW, Luz S. Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review. J Alzheimers Dis 2020;78(4):1547-74.
    PubMed
  • 69 Corcoran CM, Mittal VA, Bearden CE, E Gur R, Hitczenko K, Bilgrami Z, et al. Language as a biomarker for psychosis: A natural language processing approach. Schizophr Res 2020 Dec;226:158-66.
    PubMed
  • 70 Corcoran CM, Cecchi GA. Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders. Biol Psychiatry Cogn Neurosci Neuroimaging 2020 Aug;5(8):770-9.
    PubMed
  • 71 Ratana R, Sharifzadeh H, Krishnan J, Pang S. A Comprehensive Review of Computational Methods for Automatic Prediction of Schizophrenia With Insight Into Indigenous Populations. Front Psychiatry 2019 Sep 12;10:659.
    PubMed
  • 72 Dai HJ, Su CH, Lee YQ, Zhang YC, Wang CK, Kuo CJ, et al. Deep Learning-Based Natural Language Processing for Screening Psychiatric Patients. Front Psychiatry 2021 Jan 15;11:533949.
    PubMed
  • 73 Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, et al. RoBERTa: A robustly optimized bert pretraining approach. arXiv preprint 2019; arXiv:1907.11692.
    PubMed
  • 74 Cuthbert BN. The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry 2014 Feb;13(1):28-35.
    PubMed
  • 75 Uzuner Ö, Stubbs A, Filannino M. A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry. J Biomed Inform 2017 Nov;75S:S1-S3.
    PubMed
  • 76 Anani M, Kazi N, Kuntz M, Kahanda I. RDoC task at BioNLP-OST 2019. In: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks; 2019. p. 216-26.
    PubMed
  • 77 Elsahar Y, Hu S, Bouazza-Marouf K, Kerr D, Mansor A. Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability. Sensors (Basel) 2019 Apr 22;19(8):1911.
    PubMed
  • 78 Higginbotham DJ, Lesher GW, Moulton BJ, Roark B. The application of natural language processing to augmentative and alternative communication. Assist Technol 2011 Spring;24(1):14-24.
    PubMed
  • 79 Maritz R, Aronsky D, Prodinger B. The International Classification of Functioning, Disability and Health (ICF) in Electronic Health Records. A Systematic Literature Review. Appl Clin Inform 2017 Dec 20;8(3):964-80.
    PubMed
  • 80 Moon S, Carlson LA, Moser ED, Agnikula Kshatriya BS, Smith CY, Rocca WA, et al. Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification. J Med Internet Res 2022 Jan 28;24(1):e29015.
    PubMed
  • 81 Sterckx L, Vandewiele G, Dehaene I, Janssens O, Ongenae F, De Backere F, et al. Clinical information extraction for preterm birth risk prediction. J Biomed Inform 2020 Oct;110:103544.
    PubMed
  • 82 Barber EL, Garg R, Persenaire C, Simon M. Natural language processing with machine learning to predict outcomes after ovarian cancer surgery. Gynecol Oncol 2021 Jan;160(1):182-6.
    PubMed
  • 83 Lin WC, Chen JS, Chiang MF, Hribar MR. Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology. Transl Vis Sci Technol 2020 Feb 27;9(2):13.
    PubMed
  • 84 Connor CW. Artificial Intelligence and Machine Learning in Anesthesiology. Anesthesiology 2019 Dec;131(6):1346-59.
    PubMed
  • 85 Gaskin GL, Pershing S, Cole TS, Shah NH. Predictive modeling of risk factors and complications of cataract surgery. Eur J Ophthalmol 2016 Jun 10;26(4):328-37.
    PubMed
  • 86 Mowery DL, South BR, Christensen L, Leng J, Peltonen LM, Salanterä S, et al. Normalizing acronyms and abbreviations to aid patient understanding of clinical texts: ShARe/CLEF eHealth Challenge 2013, Task 2. J Biomed Semantics 2016 Jul 1;7:43.
    PubMed
  • 87 Moen H, Peltonen LM, Koivumäki M, Suhonen H, Salakoski T, Ginter F, et al. Improving Layman Readability of Clinical Narratives with Unsupervised Synonym Replacement. Stud Health Technol Inform 2018;247:725-9.
    PubMed
  • 88 Gopinath D, Agrawal M, Murray L, Horng S, Karger D, Sontag D. Fast, Structured clinical documentation via contextual autocomplete. In: Proceedings of the Machine Learning for Healthcare Conference; 2020. p. 842”70.
    PubMed
  • 89 Moen H, Hakala K, Peltonen LM, Matinolli HM, Suhonen H, Terho K, et al. Assisting nurses in care documentation: from automated sentence classification to coherent document structures with subject headings. J Biomed Semantics 2020 Sep 1;11(1):10.
    PubMed
  • 90 Krishna K, Khosla S, Bigham JP, Lipton ZC. Generating SOAP notes from doctor-patient conversations using modular summarization techniques. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics; 2021. p. 4958”72.
    PubMed
  • 91 Zhang L, Negrinho R, Ghosh A, Jagannathan V, Hassanzadeh HR, Schaaf T, et al. Leveraging pretrained models for automatic summarization of doctor-patient conversations. In: Findings of the Association for Computational Linguistics: EMNLP 2021; 2021. p. 3693”712.
    PubMed
  • 92 Blackley SV, Huynh J, Wang L, Korach Z, Zhou L. Speech recognition for clinical documentation from 1990 to 2018: a systematic review. J Am Med Inform Assoc 2019 Apr 1;26(4):324-38.
    PubMed
  • 93 Coiera E, Kocaballi B, Halamka J, Laranjo L. The digital scribe. NPJ Digit Med 2018 Oct 16;1:58.
    PubMed
  • 94 van Buchem MM, Boosman H, Bauer MP, Kant IMJ, Cammel SA, Steyerberg EW. The digital scribe in clinical practice: a scoping review and research agenda. NPJ Digit Med 2021 Mar 26;4(1):57.
    PubMed
  • 95 Wang J, Lavender M, Hoque E, Brophy P, Kautz H. A patient-centered digital scribe for automatic medical documentation. JAMIA Open 2021 Feb 17;4(1):ooab003.
    PubMed
  • 96 Yada S, Joh A, Tanaka R, Cheng F, Aramaki E, Kurohashi S. Towards a versatile medical-annotation guideline feasible without heavy medical knowledge: starting from critical lung diseases. In: Proceedings of the 12th Language Resources and Evaluation Conference; 2020. p. 4565-72.
    PubMed
  • 97 Roberts A, Gaizauskas R, Hepple M, Demetriou G, Guo Y, Roberts I, et al. Building a semantically annotated corpus of clinical texts. J Biomed Inform 2009 Oct;42(5):950-66.
    PubMed
  • 98 Patel P, Davey D, Panchal V, Pathak P. Annotation of a large clinical entity corpus. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing; 2018. p. 2033-42.
    PubMed
  • 99 Campillos L, Deléger L, Grouin C, Hamon T, Ligozat AL, Névéol A. A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT). Language Resources and Evaluation 2018;52(2):571-601.
    PubMed
  • 100 Yada S, Aramaki E, Tanaka R, Cheng F, Kurohashi S. Medical/Clinical text annotation guidelines. figshare. Book; 2021. Available from: https://doi.org/10.6084/m9.figshare.16418811.v2
    PubMed
  • 101 Styler WF, Bethard S, Finan S, Palmer M, Pradhan S, De Groen PC, et al. Temporal annotation in the clinical domain. Transactions of the association for computational linguistics 2014;2:143-54.
    PubMed
  • 102 Karimi S, Metke-Jimenez A, Kemp M, Wang C. Cadec: A corpus of adverse drug event annotations. J Biomed Inform 2015 Jun;55:73-81.
    PubMed
  • 103 Roberts K, Demner-Fushman D, Tonning JM. Overview of the TAC 2017 Adverse Reaction Extraction from Drug Labels Track. In: Proceedings of the Tenth Text Analysis Conference 2017.
    PubMed
  • 104 Henry S, Buchan K, Filannino M, Stubbs A, Uzuner Ö. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. J Am Med Inform Assoc 2020 Jan 1;27(1):3-12.
    PubMed
  • 105 Aramaki E, Yano K, Wakamiya S. MedEx/J: A One-Scan Simple and Fast NLP Tool for Japanese Clinical Texts. Stud Health Technol Inform 2017;245:285-8.
    PubMed
  • 106 Huang Y, Lowe HJ. A novel hybrid approach to automated negation detection in clinical radiology reports. J Am Med Inform Assoc 2007 May-Jun;14(3):304-11.
    PubMed
  • 107 Jagannatha A, Liu F, Liu W, Yu H. Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0). Drug Saf 2019 Jan;42(1):99-111.
    PubMed
  • 108 Wei CH, Peng Y, Leaman R, Davis AP, Mattingly CJ, Li J, et al. Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task. Database (Oxford) 2016 Mar 19;2016:baw032.
    PubMed
  • 109 Bayer S, Clark C, Dang O, Aberdeen J, Brajovic S, Swank K, et al. ADE Eval: An Evaluation of Text Processing Systems for Adverse Event Extraction from Drug Labels for Pharmacovigilance. Drug Saf 2021 Jan;44(1):83-94.
    PubMed
  • 110 Negi K, Pavuri A, Patel L, Jain C. A novel method for drug-adverse event extraction using machine learning. Informatics in Medicine Unlocked 2019;17:100190.
    PubMed
  • 111 Ujiie S, Yada S, Wakamiya S, Aramaki E. Identification of Adverse Drug Event-Related Japanese Articles: Natural Language Processing Analysis. JMIR Med Inform 2020 Nov 27;8(11):e22661.
    PubMed
  • 112 Wen A, Fu S, Moon S, El Wazir M, Rosenbaum A, Kaggal VC, et al. Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation. NPJ Digit Med 2019 Dec 17;2:130.
    PubMed
  • 113 Rouzfarakh M, Deldar K, Froutan R, Ahmadabadi A, Mazlom SR. The effect of rehabilitation education through social media on the quality of life in burn patients: a randomized, controlled, clinical trial. BMC Med Inform Decis Mak 2021 Feb 22;21(1):70.
    PubMed
  • 114 Zhang QL, Xu N, Huang ST, Chen Q, Cao H. WeChat-Assisted Preoperative Health Education Reduces Burden of Care on Parents of Children with Simple Congenital Heart Disease: a Prospective Randomized Controlled Study. Braz J Cardiovasc Surg 2021 Oct 17;36(5):663-9.
    PubMed
  • 115 Yonek JC, Meacham MC, Ramo D, Delucchi K, Tolou-Shams M, Satre DD. The Relationship of E-Cigarette Use to Tobacco Use Outcomes Among Young Adults Who Smoke and Use Alcohol. J Addict Med 2021 Sep-Oct 01;15(5):421-4.
    PubMed
  • 116 Yang B, Liu JF, Xie WP, Cao H, Chen Q. The effects of WeChat follow-up management to improve the parents' mental status and the quality of life of premature newborns with patent ductus arteriosus. J Cardiothorac Surg 2021 Aug 21;16(1):235.
    PubMed
  • 117 Abd-Alrazaq AA, Alajlani M, Alalwan AA, Bewick BM, Gardner P, Househ M. An overview of the features of chatbots in mental health: A scoping review. Int J Med Inform 2019 Dec;132:103978.
    PubMed
  • 118 Almusharraf F, Rose J, Selby P. Engaging Unmotivated Smokers to Move Toward Quitting: Design of Motivational Interviewing-Based Chatbot Through Iterative Interactions. J Med Internet Res 2020 Nov 3;22(11):e20251.
    PubMed
  • 119 Zhang Y, Sun S, Galley M, Chen Y-C, Brockett C, Gao X, et al. DialoGPT: Large-scale generative pre-training for conversational response generation. In: Proceedings of the 58th annual meeting of the association for computational linguistics: System demonstrations. Association for Computational Linguistics; 2020. p. 270”8.
    PubMed
  • 120 Cooper A, Ireland D. Designing a Chat-Bot for Non-Verbal Children on the Autism Spectrum. Stud Health Technol Inform 2018;252:63-8.
    PubMed
  • 121 Zand A, Sharma A, Stokes Z, Reynolds C, Montilla A, Sauk J, Hommes D. An Exploration Into the Use of a Chatbot for Patients With Inflammatory Bowel Diseases: Retrospective Cohort Study. J Med Internet Res 2020 May 26;22(5):e15589.
    PubMed
  • 122 Glowacki EM, Bernhardt JM, McGlone MS. Tailored texts: An application of regulatory fit to text messages designed to reduce high-risk drinking. Health Informatics J 2020 Sep;26(3):1742763.
    PubMed
  • 123 Glass JE, McKay JR, Gustafson DH, Kornfield R, Rathouz PJ, McTavish FM, et al. Treatment seeking as a mechanism of change in a randomized controlled trial of a mobile health intervention to support recovery from alcohol use disorders. J Subst Abuse Treat 2017 Jun;77:57-66.
    PubMed
  • 124 Reiswich A, Haag M. Evaluation of Chatbot Prototypes for Taking the Virtual Patient's History. Stud Health Technol Inform 2019;260:73-80.
    PubMed
  • 125 Shorey S, Ang E, Yap J, Ng ED, Lau ST, Chui CK. A Virtual Counseling Application Using Artificial Intelligence for Communication Skills Training in Nursing Education: Development Study. J Med Internet Res 2019 Oct 29;21(10):e14658. Erratum in: J Med Internet Res 2019 Nov 26;21(11):e17064.
    PubMed
  • 126 Lee H, Kang J, Yeo J. Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment. J Med Internet Res 2021 May 6;23(5):e27460.
    PubMed
  • 127 Chu ET, Huang ZZ. DBOS: A Dialog-Based Object Query System for Hospital Nurses. Sensors (Basel) 2020 Nov 19;20(22):6639.
    PubMed