본문 바로가기
KERD
논문KCI 등재

Use of Machine Learning in Stroke Rehabilitation: A Narrative Review

1

조회

0

다운로드

0

인용

0

참고문헌

키워드

초록

A narrative review was conducted of machine learning applications and research in the field of stroke rehabilitation. The machine learning models commonly used in medical research include random forest, logistic regression, and deep neural networks. Convolutional neural networks (CNNs), a type of deep neural network, are typically used for image analysis. Machine learning has been used in stroke rehabilitation to predict recover y of motor function using a large amount of clinical data as input. Recent studies on predicting motor function have trained CNN models using magnetic resonance images as input data together with clinical data to increase the accuracy of motor function prediction models. Additionally, a model interpreting videofluoroscopic swallowing studies was developed and investigated. In the future, we anticipate that machine learning will be actively used to treat stroke patients, such as predicting the occurrence of depression and the recover y of language, cognitive, and sensor y function, as well as prescribing appropriate rehabilitation treatments

Abstract

A narrative review was conducted of machine learning applications and research in the field of stroke rehabilitation. The machine learning models commonly used in medical research include random forest, logistic regression, and deep neural networks. Convolutional neural networks (CNNs), a type of deep neural network, are typically used for image analysis. Machine learning has been used in stroke rehabilitation to predict recover y of motor function using a large amount of clinical data as input. Recent studies on predicting motor function have trained CNN models using magnetic resonance images as input data together with clinical data to increase the accuracy of motor function prediction models. Additionally, a model interpreting videofluoroscopic swallowing studies was developed and investigated. In the future, we anticipate that machine learning will be actively used to treat stroke patients, such as predicting the occurrence of depression and the recover y of language, cognitive, and sensor y function, as well as prescribing appropriate rehabilitation treatments

참고문헌 (53)

  1. Janiesch C, "Machine learning and deep learning", Electron Mark, 31, pp.685~695 (2021)
  2. Jovel J, "An introduction to machine learning approaches for biomedical research", Front Med(Lausanne), 8, pp.771607~ (2021)
  3. Exarchos I, "Supervised and unsupervised machine learning for automated scoring of sleep-wake and cataplexy in a mouse model of narcolepsy", Sleep (Basel), 43, pp.zsz272~ (2020)
  4. Pan W, "Cluster analysis : unsupervised learning via supervised learning with a non-convex penalty", J Mach Learn Res, 14, pp.1865~ (2013)
  5. Wagner M, "The importance of machine learning in autonomous actions for surgical decision making", Art Int Surg, 2, pp.64~79 (2022)
  6. Sarker IH, "Deep learning : a comprehensive overview on techniques, taxonomy, applications and research directions", SN Comput Sci, 2, pp.420~ (2021)
  7. LeCun Y, "Deep learning", Nature, 521, pp.436~444 (2015)
  8. Rajula HS, "Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment", Medicina(Kaunas), 56, pp.455~ (2020)
  9. González RG, "Clinical MRI of acute ischemic stroke", J Magn Reson Imaging, 36, pp.259~271 (2012)
  10. Gupta VP, "Prognosticating functional outcome after intracerebral hemorrhage : the ICHOP score", World Neurosurg, 101, pp.577~583 (2017)
  11. Lin WY, "Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation", Int J Med Inform, 111, pp.159~164 (2018)
  12. Kim Jeoung Kun, "Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study", Journal of Korean Medical Science, 37(6), pp.1~8 (2022), 대한의학회[DOI]
  13. Kim JK, "Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning", Sci Rep, 11, pp.7989~ (2021)
  14. Svetnik V, "Random forest : a classification and regression tool for compound classification and QSAR modeling", J Chem Inf Comput Sci, 43, pp.1947~1958 (2003)
  15. Denisko D, "Classification and interaction in random forests", Proc Natl Acad Sci U S A, 115, pp.1690~1692 (2018)
  16. Chen X, "Random forests for genomic data analysis", Genomics, 99, pp.323~329 (2012)
  17. Mazumdar D, "Random forest similarity maps: a scalable visual representation for global and local interpretation", Electronics(Basel), 10, pp.2862~ (2021)
  18. Pirneskoski J, "Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality : a retrospective study", Resusc Plus, 4, pp.100046~ (2020)
  19. Odegua R, "An empirical study of ensemble techniques (bagging, boosting and stacking)", Proceedings of the conference “Deep Learning IndabaX”, pp.1~10 (2019), University of Lagos
  20. Chen T, "Xgboost: a scalable tree boosting system", Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp.785~794 (2016), Association for Computing Machinery
  21. Ke G, "Lightgbm : a highly efficient gradient boosting decision tree", Adv Neural Inf Process Syst, 30, pp.3146~3154 (2017)
  22. Hancock JT, "CatBoost for big data : an interdisciplinary review", J Big Data, 7, pp.94~ (2020)
  23. Weis DC, "Data mining PubChem using a support vector machine with the Signature molecular descriptor : classification of factor XIa inhibitors", J Mol Graph Model, 27, pp.466~475 (2008)
  24. Winters-Hilt S, "SVM clustering", BMC Bioinformatics, 8(Suppl 7), pp.S18~ (2007)
  25. Awad M, "Efficient learning machines" (2015), Apress
  26. Huang S, "Applications of support vector machine(SVM)learning in cancer genomics", Cancer Genomics Proteomics, 15, pp.41~51 (2018)
  27. Choi RY, "Introduction to machine learning, neural networks, and deep learning", Transl Vis Sci Technol, 9, pp.14~ (2020)
  28. Sperandei S, "Understanding logistic regression analysis", Biochem Med(Zagreb), 24, pp.12~18 (2014)
  29. Nadkarni P, "Clinical research computing", pp.85~114 (2016), Academic Press
  30. Chang AC, "Intelligence-based medicine", pp.67~140 (2020), Academic Press
  31. Kriegeskorte N, "Neural network models and deep learning", Curr Biol, 29, pp.R231~R236 (2019)
  32. Sarker IH, "Deep learning : a comprehensive overview on techniques, taxonomy, applications and research directions", SN Comput Sci, 2, pp.420~ (2021)
  33. Shin H, "Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image", BMC Musculoskelet Disord, 23, pp.510~ (2022)
  34. 이하경, "Introduction to convolutional neural network using Keras; an understanding from a statistician", Communications for Statistical Applications and Methods, 26(6), pp.591~610 (2019), 한국통계학회
  35. Vaswani A, "Attention is all you need", Adv Neural Inf Process Syst, 30, pp.1~11 (2017)
  36. Sulubacak U, "Multimodal machine translation through visuals and speech", Mach Transl, 34, pp.97~147 (2020)
  37. Roshanzamir A, "Transformer-based deep neural network language models for Alzheimer’s disease risk assessment from targeted speech", BMC Med Inform Decis Mak, 21, pp.92~ (2021)
  38. Giulia A, "extending association rule mining to microbiome pattern analysis : tools and guidelines to support real applications", Front Bioinform, 1, pp.794547~ (2022)
  39. Mendez KM, "Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing", Metabolomics, 15, pp.125~ (2019)
  40. Chicco D, "The Matthews correlation coefficient(MCC)is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation", BioData Min, 14, pp.13~ (2021)
  41. Chicco D, "The advantages of the Matthews correlation coefficient(MCC)over F1 score and accuracy in binary classification evaluation", BMC Genomics, 21, pp.6~ (2020)
  42. Keilwagen J, "Area under precision-recall curves for weighted and unweighted data", PLoS One, 9, pp.e92209~ (2014)
  43. Heo J, "Machine learning-based model for prediction of outcomes in acute stroke", Stroke, 50, pp.1263~1265 (2019)
  44. Kim JK, "Practical machine learning model to predict the recovery of motor function in patients with stroke", Eur Neurol, 85, pp.273~279 (2022)
  45. Choo YJ, "Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke", Sci Rep, 11, pp.8499~ (2021)
  46. Liang X, "Predicting post-stroke somatosensory function from resting-state functional connectivity : a feasibility study", Brain Sci, 11, pp.1388~ (2021)
  47. Kim JK, "Deep learning algorithm trained on brain magnetic resonance images and clinical data to predict motor outcomes of patients with corona radiata infarct", Front Neurosci, 15, pp.795553~ (2022)
  48. Meng Y, "Prediction model of hemorrhage transformation in patient with acute ischemic stroke based on multiparametric MRI radiomics and machine learning", Brain Sci, 12, pp.858~ (2022)
  49. Mutke MA, "Comparing poor and favorable outcome prediction with machine learning after mechanical thrombectomy in acute ischemic stroke", Front Neurol, 13, pp.737667~ (2022)
  50. Shin H, "Prediction of motor outcome of stroke patients using a deep learning algorithm with brain MRI as input data", Eur Neurol (2022)
  51. Feng MC, "The mortality and the risk of aspiration pneumonia related with dysphagia in stroke patients", J Stroke Cerebrovasc Dis, 28, pp.1381~1387 (2019)
  52. Gates ED, "An efficient magnetic resonance image data quality screening dashboard", J Appl Clin Med Phys, 23, pp.e13557~ (2022)
  53. Saeed SU, "Image quality assessment for machine learning tasks using meta-reinforcement learning", Med Image Anal, 78, pp.102427~ (2022)

관련 논문AI추천

이 간행물의 다른 논문 (10건)