Natural Language Processing: from Bedside to Everywhere

Overview

Journal Yearb Med Inform

Publisher Thieme

Specialty Medical Informatics

Date 2022 Jun 2

PMID 35654422

Authors

Eiji Aramaki

Shoko Wakamiya

Shuntaro Yada

Yuta Nakamura

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Usefulness of Generative Artificial Intelligence (AI) Tools in Pediatric Dentistry.

Kusaka S, Akitomo T, Hamada M, Asao Y, Iwamoto Y, Tachikake M Diagnostics (Basel). 2025; 14(24.

PMID: 39767179 PMC: 11674453. DOI: 10.3390/diagnostics14242818.

Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing.

Lee J, Zepeda A, Arbour G, Isaac K, Ng R, Nichol A JCO Clin Cancer Inform. 2024; 8:e2400107.

PMID: 39705642 PMC: 11670918. DOI: 10.1200/CCI.24.00107.

Identifying Medical Concepts and Semantic Types in Lay Vocabularies of Health Consumers Who are Concerned with Diabetes on Social Media Using the UMLS and NLP.

Anik A, Upama P, Rabbani M, Tian S, Park M, Ahamed S Proc COMPSAC. 2024; 2024:862-869.

PMID: 39640180 PMC: 11619756. DOI: 10.1109/compsac61105.2024.00119.

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.

Pham T, Teh M, Chatzopoulou D, Holmes S, Coulthard P Curr Oncol. 2024; 31(9):5255-5290.

PMID: 39330017 PMC: 11430806. DOI: 10.3390/curroncol31090389.

Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs.

Yada S, Nishiyama T, Wakamiya S, Kawazoe Y, Imai S, Hori S PLoS One. 2024; 19(9):e0310432.

PMID: 39259727 PMC: 11389901. DOI: 10.1371/journal.pone.0310432.

References

Abd-Alrazaq A, Alajlani M, Alalwan A, Bewick B, Gardner P, Househ M . An overview of the features of chatbots in mental health: A scoping review. Int J Med Inform. 2019; 132:103978. DOI: 10.1016/j.ijmedinf.2019.103978. View

Rouzfarakh M, Deldar K, Froutan R, Ahmadabadi A, Mazlom S . 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; 21(1):70. PMC: 7901117. DOI: 10.1186/s12911-021-01421-0. View

DeSouza D, Robin J, Gumus M, Yeung A . Natural Language Processing as an Emerging Tool to Detect Late-Life Depression. Front Psychiatry. 2021; 12:719125. PMC: 8450440. DOI: 10.3389/fpsyt.2021.719125. View

Monshi M, Poon J, Chung V . Deep learning in generating radiology reports: A survey. Artif Intell Med. 2020; 106:101878. PMC: 7227610. DOI: 10.1016/j.artmed.2020.101878. View

Shiner B, Neily J, Mills P, Watts B . Identification of Inpatient Falls Using Automated Review of Text-Based Medical Records. J Patient Saf. 2016; 16(3):e174-e178. DOI: 10.1097/PTS.0000000000000275. View

Datta S, Bernstam E, Roberts K . A frame semantic overview of NLP-based information extraction for cancer-related EHR notes. J Biomed Inform. 2019; 100:103301. PMC: 11771512. DOI: 10.1016/j.jbi.2019.103301. View

Li J, Chen H, Wang Y, Chen M, Liang H . Next-Generation Analytics for Omics Data. Cancer Cell. 2020; 39(1):3-6. PMC: 8336660. DOI: 10.1016/j.ccell.2020.09.002. View

Boye M, Grabar N, Tran M . Contrastive conversational analysis of language production by Alzheimer's and control people. Stud Health Technol Inform. 2014; 205:682-6. View

van Buchem M, Boosman H, Bauer M, Kant I, Cammel S, Steyerberg E . The digital scribe in clinical practice: a scoping review and research agenda. NPJ Digit Med. 2021; 4(1):57. PMC: 7997964. DOI: 10.1038/s41746-021-00432-5. View

10.

Connor C . Artificial Intelligence and Machine Learning in Anesthesiology. Anesthesiology. 2019; 131(6):1346-1359. PMC: 6778496. DOI: 10.1097/ALN.0000000000002694. View

11.

Sun W, Rumshisky A, Uzuner O . Evaluating temporal relations in clinical text: 2012 i2b2 Challenge. J Am Med Inform Assoc. 2013; 20(5):806-13. PMC: 3756273. DOI: 10.1136/amiajnl-2013-001628. View

12.

Chase H, Mitrani L, Lu G, Fulgieri D . Early recognition of multiple sclerosis using natural language processing of the electronic health record. BMC Med Inform Decis Mak. 2017; 17(1):24. PMC: 5329909. DOI: 10.1186/s12911-017-0418-4. View

13.

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; 27(11):1784-1797. PMC: 7671617. DOI: 10.1093/jamia/ocaa174. View

14.

Buchan K, Filannino M, Uzuner O . Automatic prediction of coronary artery disease from clinical narratives. J Biomed Inform. 2017; 72:23-32. PMC: 5592829. DOI: 10.1016/j.jbi.2017.06.019. View

15.

Chapman W, Nadkarni P, Hirschman L, DAvolio L, Savova G, Uzuner O . 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; 18(5):540-3. PMC: 3168329. DOI: 10.1136/amiajnl-2011-000465. View

16.

Ujiie S, Yada S, Wakamiya S, Aramaki E . Identification of Adverse Drug Event-Related Japanese Articles: Natural Language Processing Analysis. JMIR Med Inform. 2020; 8(11):e22661. PMC: 7732716. DOI: 10.2196/22661. View

17.

Aronson A . Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp. 2002; :17-21. PMC: 2243666. View

18.

Letourneau-Guillon L, Camirand D, Guilbert F, Forghani R . Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics. Neuroimaging Clin N Am. 2020; 30(4):e1-e15. DOI: 10.1016/j.nic.2020.08.008. View

19.

Karimi S, Metke-Jimenez A, Kemp M, Wang C . Cadec: A corpus of adverse drug event annotations. J Biomed Inform. 2015; 55:73-81. DOI: 10.1016/j.jbi.2015.03.010. View

20.

Cuthbert B . The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014; 13(1):28-35. PMC: 3918011. DOI: 10.1002/wps.20087. View