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Review of Stroke-specific Natural Language Processing (NLP) and Machine Learning (ML) Applications with Unstructured Data
by
Tahani Daghistani
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 3 |
| Year of Publication: 2024 |
| Authors: Tahani Daghistani |
10.5120/ijca2024923373
|
Tahani Daghistani . Review of Stroke-specific Natural Language Processing (NLP) and Machine Learning (ML) Applications with Unstructured Data. International Journal of Computer Applications. 186, 3 ( Jan 2024), 37-40. DOI=10.5120/ijca2024923373
@article{
10.5120/ijca2024923373,
author = {
Tahani Daghistani
},
title = { Review of Stroke-specific Natural Language Processing (NLP) and Machine Learning (ML) Applications with Unstructured Data },
journal = {
International Journal of Computer Applications
},
issue_date = { Jan 2024 },
volume = { 186 },
number = { 3 },
month = { Jan },
year = { 2024 },
issn = { 0975-8887 },
pages = {
37-40
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume186/number3/33055-2024923373/
},
doi = { 10.5120/ijca2024923373 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:29:38.826043+05:30
%A Tahani Daghistani
%T Review of Stroke-specific Natural Language Processing (NLP) and Machine Learning (ML) Applications with Unstructured Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 3
%P 37-40
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The interest in Neutral Language Processing (NLP) and Machine Learning (ML) applications, in particular to stroke using unstructured data, has markedly increased in recent years. In this rapidly evolving context, it is necessary to learn and understand the novel approaches that complement and even exceed accumulated experience in the medical field. Various studies over the years conducted to demonstrate such applications with their ability to process large amounts of data, as one of the proposed approaches to support the neuroradiologists and improve the care of stroke. This study evaluates NLP/ML-based models with respect to (1) Application purpose, (2) the outcome considered, and (3) the approaches applied.
References
- Houssein, E.H., Mohamed, R.E. and Ali, A.A., 2021. Machine learning techniques for biomedical natural language processing: a comprehensive review. IEEE Access, 9, pp.140628-140653.
- Malmasi, S., Hosomura, N., Chang, L.S., Brown, C.J., Skentzos, S. and Turchin, A., 2017. Extracting healthcare quality information from unstructured data. In AMIA Annual Symposium Proceedings (Vol. 2017, p. 1243). American Medical Informatics Association.
- De Rosario, H., Pitarch-Corresa, S., Pedrosa, I., Vidal-Pedrós, M., de Otto-López, B., García-Mieres, H. and Álvarez-Rodríguez, L., 2023. Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review. JMIR Medical Informatics, 11, p.e48693.
- Elkins JS, Friedman C, Boden-Albala B, Sacco RL, Hripcsak G. Coding neuroradiology reports for the Northern Manhattan Stroke Study: a comparison of natural language processing and manual review. Comput Biomed Res. 2000 Feb;33(1):1-10. doi: 10.1006/cbmr.1999.1535. PMID: 10772780.
- Kim C, Zhu V, Obeid J, Lenert L. Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke. PLoS One. 2019 Feb 28;14(2):e0212778. doi: 10.1371/journal.pone.0212778. PMID: 30818342; PMCID: PMC6394972.
- Garg R, Oh E, Naidech A, Kording K, Prabhakaran S. Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing. J Stroke Cerebrovasc Dis. 2019 Jul;28(7):2045-2051. doi: 10.1016/j.jstrokecerebrovasdis.2019.02.004. Epub 2019 May 15. PMID: 31103549.
- Ong, C.J., Orfanoudaki, A., Zhang, R., Caprasse, F.P.M., Hutch, M., Ma, L., Fard, D., Balogun, O., Miller, M.I., Minnig, M. and Saglam, H., 2020. Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports. PloS one, 15(6), p.e0234908.
- Kogan E, Twyman K, Heap J, Milentijevic D, Lin JH, Alberts M. Assessing stroke severity using electronic health record data: a machine learning approach. BMC Med Inform Decis Mak. 2020 Jan 8;20(1):8. doi: 10.1186/s12911-019-1010-x. PMID: 31914991; PMCID: PMC6950922.
- Yu, A.Y., Liu, Z.A., Pou-Prom, C., Lopes, K., Kapral, M.K., Aviv, R.I. and Mamdani, M., 2021. Automating Stroke Data extraction from free-text radiology reports using natural language processing: instrument validation study. JMIR Medical Informatics, 9(5), p.e24381..
- Li MD, Lang M, Deng F, Chang K, Buch K, Rincon S, Mehan WA, Leslie-Mazwi TM, Kalpathy-Cramer J. Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports. AJNR Am J Neuroradiol. 2021 Mar;42(3):429-434. doi: 10.3174/ajnr.A6961. Epub 2020 Dec 17. PMID: 33334851; PMCID: PMC7959438.
- Gunter, D., Puac-Polanco, P., Miguel, O., Thornhill, R.E., Yu, A.Y., Liu, Z.A., Mamdani, M., Pou-Prom, C. and Aviv, R.I., 2022. Rule-based natural language processing for automation of stroke data extraction: a validation study. Neuroradiology, 64(12), pp.2357-2362.
- Prevedello LM, Farkas C, Ip IK, Cohen AB, Mukundan S, Sodickson AD, Khorasani R. Large-scale automated assessment of radiologist adherence to the Physician Quality Reporting System for stroke. J Am Coll Radiol. 2012 Jun;9(6):414-20. doi: 10.1016/j.jacr.2012.01.014. PMID: 22632668.
- Mowery, D.L., Chapman, B.E., Conway, M., South, B.R., Madden, E., Keyhani, S. and Chapman, W.W., 2016. Extracting a stroke phenotype risk factor from Veteran Health Administration clinical reports: an information content analysis. Journal of Biomedical Semantics, 7(1), pp.1-12.
- Wheater, E., Mair, G., Sudlow, C., Alex, B., Grover, C. and Whiteley, W., 2019. A validated natural language processing algorithm for brain imaging phenotypes from radiology reports in UK electronic health records. BMC Medical Informatics and Decision Making, 19(1), pp.1-11.
- Alex B, Grover C, Tobin R, Sudlow C, Mair G, Whiteley W. Text mining brain imaging reports. J Biomed Semantics. 2019 Nov 12;10(Suppl 1):23. doi: 10.1186/s13326-019-0211-7. PMID: 31711539; PMCID: PMC6849161.
- Mayampurath A, Parnianpour Z, Richards CT, Meurer WJ, Lee J, Ankenman B, Perry O, Mendelson SJ, Holl JL, Prabhakaran S. Improving Prehospital Stroke Diagnosis Using Natural Language Processing of Paramedic Reports. Stroke. 2021 Aug;52(8):2676-2679. doi: 10.1161/STROKEAHA.120.033580. Epub 2021 Jun 24. PMID: 34162217; PMCID: PMC8514137.
- Zhao Y, Fu S, Bielinski SJ, Decker PA, Chamberlain AM, Roger VL, Liu H, Larson NB. Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation. J Med Internet Res. 2021 Mar 8;23(3):e22951. doi: 10.2196/22951. PMID: 33683212; PMCID: PMC7985804.
- Sung, S.F., Chen, K., Wu, D.P., Hung, L.C., Su, Y.H. and Hu, Y.H., 2018. Applying natural language processing techniques to develop a task-specific EMR interface for timely stroke thrombolysis: a feasibility study. International journal of medical informatics, 112, pp.149-157.
- Miller, M.I., Orfanoudaki, A., Cronin, M., Saglam, H., So Yeon Kim, I., Balogun, O., Tzalidi, M., Vasilopoulos, K., Fanaropoulou, G., Fanaropoulou, N.M. and Kalin, J., 2022. Natural Language Processing of Radiology Reports to Detect Complications of Ischemic Stroke. Neurocritical Care, pp.1-12.
- Huang, R., Liu, J., Wan, T.K., Siriwanna, D., Woo, Y.M.P., Vodencarevic, A., Wong, C.W. and Chan, K.H.K., 2023. Stroke mortality prediction based on ensemble learning and the combination of structured and textual data. Computers in Biology and Medicine, 155, p.106176
Index Terms
Computer Science
Information Sciences
Keywords