AI in Rehabilitation Medicine: Opportunities and Challenges

키워드

참고문헌 (104)

1. Korteling JEH, "Human-versus Artificial Intelligence", Front Artif Intell, 4, pp.622364~ (2021)
2. Yu KH, "Artificial intelligence in healthcare", Nat Biomed Eng, 2, pp.719~731 (2018)
3. van der Schaar M, "Annual report of the chief medical officer, 2018. Health 2040 – better health within reach" (2018), Department of Health and Social Care
4. French MA, "Precision rehabilitation : optimizing function, adding value to health care", Arch Phys Med Rehabil, 103, pp.1233~1239 (2022)
5. Agrawal R, "Big data in digital healthcare : lessons learnt and recommendations for general practice", Heredity(Edinb), 124, pp.525~534 (2020)
6. Reda R, "Towards consistent data representation in the IoT healthcare landscape", Paper presented at: DH '18: Proceedings of the 2018 International Conference on Digital Health, pp.5~10 (2018)
7. Bellazzi R, "Predictive data mining in clinical medicine : current issues and guidelines", Int J Med Inform, 77, pp.81~97 (2008)
8. Deo RC, "Machine learning in medicine", Circulation, 132, pp.1920~1930 (2015)
9. Landi I, "Deep representation learning of electronic health records to unlock patient stratification at scale", NPJ Digit Med, 3, pp.96~ (2020)
10. Jensen PB, "Mining electronic health records : towards better research applications and clinical care", Nat Rev Genet, 13, pp.395~405 (2012)
11. Xu S, "Translational gaps and opportunities for medical wearables in digital health", Sci Transl Med, 14, pp.eabn6036~ (2022)
12. Brom H, "Leveraging electronic health records and machine learning to tailor nursing care for patients at high risk for readmissions", J Nurs Care Qual, 35, pp.27~33 (2020)
13. Delahanty RJ, "Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis", Ann Emerg Med, 73, pp.334~344 (2019)
14. Engelhard MM, "Predictive value of early autism detection models based on electronic health record data collected before age 1 year", JAMA Netw Open, 6, pp.e2254303~ (2023)
15. Flores AM, "Leveraging machine learning and artificial intelligence to improve peripheral artery disease detection, treatment, and outcomes", Circ Res, 128, pp.1833~1850 (2021)
16. Ye C, "Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm", Int J Med Inform, 137, pp.104105~ (2020)
17. Cramer EM, "Predicting the incidence of pressure ulcers in the intensive care unit using machine learning", EGEMS(Wash DC), 7, pp.49~ (2019)
18. Kucukboyaci NE, "Cluster analysis of vulnerable groups in acute traumatic brain injury rehabilitation", Arch Phys Med Rehabil, 99, pp.2365~2369 (2018)
19. Custer MG, "Identifying homogeneous outcome groups in adult rehabilitation using cluster analysis", Am J Occup Ther, 73, pp.7305205050p1~7305205050p9 (2019)
20. Bland MD, "Prediction of discharge walking ability from initial assessment in a stroke inpatient rehabilitation facility population", Arch Phys Med Rehabil, 93, pp.1441~1447 (2012)
21. Harari Y, "Inpatient stroke rehabilitation : prediction of clinical outcomes using a machine-learning approach", J Neuroeng Rehabil, 17, pp.71~ (2020)
22. Henderson CE, "Predicting discharge walking function with high-intensity stepping training during inpatient rehabilitation in nonambulatory patients poststroke", Arch Phys Med Rehabil, 103(7S), pp.S189~S196 (2022)
23. Scrutinio D, "Development and validation of a predictive model for functional outcome after stroke rehabilitation : the Maugeri model", Stroke, 48, pp.3308~3315 (2017)
24. Stinear CM, "Prediction tools for stroke rehabilitation", Stroke, 50, pp.3314~3322 (2019)
25. Smith MC, "The TWIST tool predicts when patients will recover independent walking after stroke : an observational study", Neurorehabil Neural Repair, 36, pp.461~471 (2022)
26. Lundquist CB, "Accuracy of the upper limb prediction algorithm PREP2 applied 2 weeks poststroke : a prospective longitudinal study", Neurorehabil Neural Repair, 35, pp.68~78 (2021)
27. Ehteshami Bejnordi B, "Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer", JAMA, 318, pp.2199~2210 (2017)
28. Litjens G, "Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis", Sci Rep, 6, pp.26286~ (2016)
29. Poplin R, "Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning", Nat Biomed Eng, 2, pp.158~164 (2018)
30. Panwar N, "Fundus photography in the 21st century--a review of recent technological advances and their implications for worldwide healthcare", Telemed J E Health, 22, pp.198~208 (2016)
31. Solana-Lavalle G, "Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of Parkinson’s disease", Comput Methods Programs Biomed, 198, pp.105793~ (2021)
32. Liew SL, "Association of brain age, lesion volume, and functional outcome in patients with stroke", Neurology, 100, pp.e2103~e2113 (2023)
33. Warmerdam E, "Long-term unsupervised mobility assessment in movement disorders", Lancet Neurol, 19, pp.462~470 (2020)
34. Adans-Dester CP, "Neurorehabilitation technology", pp.467~507 (2022), Springer
35. O’Brien MK, "Wearable sensors improve prediction of post-stroke walking function following inpatient rehabilitation", IEEE J Transl Eng Health Med, 10, pp.2100711~ (2022)
36. Liuzzi P, "Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors", Sci Rep, 13, pp.8640~ (2023)
37. Adans-Dester C, "Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery", NPJ Digit Med, 3, pp.121~ (2020)
38. Lonini L, "Wearable sensors for Parkinson’s disease : which data are worth collecting for training symptom detection models", NPJ Digit Med, 1, pp.64~ (2018)
39. Shawen N, "Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors", J Neuroeng Rehabil, 17, pp.52~ (2020)
40. O’Brien MK, "Advanced machine learning tools to monitor biomarkers of dysphagia : a wearable sensor proof-of-concept study", Digit Biomark, 5, pp.167~175 (2021)
41. Hafer JF, "Challenges and advances in the use of wearable sensors for lower extremity biomechanics", J Biomech, 157, pp.111714~ (2023)
42. Albert MV, "Monitoring daily function in persons with transfemoral amputations using a commercial activity monitor : a feasibility study", PM R, 6, pp.1120~1127 (2014)
43. Kim J, "Wearable sensors quantify mobility in people with lower limb amputation during daily life", IEEE Trans Neural Syst Rehabil Eng, 28, pp.1282~1291 (2020)
44. Moshawrab M, "Smart wearables for the detection of cardiovascular diseases : a systematic literature review", Sensors(Basel), 23, pp.828~ (2023)
45. Boe AJ, "Automating sleep stage classification using wireless, wearable sensors", NPJ Digit Med, 2, pp.131~ (2019)
46. Hsieh KL, "Mobile technology for falls prevention in older adults", J Gerontol A Biol Sci Med Sci, 78, pp.861~868 (2023)
47. Ghomrawi HMK, "Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy", NPJ Digit Med, 6, pp.148~ (2023)
48. O’Brien MK, "Activity recognition for persons with stroke using mobile phone technology : toward improved performance in a home setting", J Med Internet Res, 19, pp.e184~ (2017)
49. Adib F, "Smart homes that monitor breathing and heart rate", Paper presented at : CHI '15 : Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp.837~846 (2015)
50. Zhang G, "Contactless in-home monitoring of the long-term respiratory and behavioral phenotypes in older adults with COVID-19 : a case series", Front Psychiatry, 12, pp.754169~ (2021)
51. Yang Y, "Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals", Nat Med, 28, pp.2207~2215 (2022)
52. Haque A, "Illuminating the dark spaces of healthcare with ambient intelligence", Nature, 585, pp.193~202 (2020)
53. Kim WS, "Upper extremity functional evaluation by Fugl-Meyer Assessment scoring using depth-sensing camera in hemiplegic stroke patients", PLoS One, 11, pp.e0158640~ (2016)
54. Stenum J, "Two-dimensional video-based analysis of human gait using pose estimation", PLoS Comput Biol, 17, pp.e1008935~ (2021)
55. Sato K, "Quantifying normal and parkinsonian gait features from home movies : practical application of a deep learning-based 2D pose estimator", PLoS One, 14, pp.e0223549~ (2019)
56. Lonini L, "Video-based pose estimation for gait analysis in stroke survivors during clinical assessments : a proof-of-concept study", Digit Biomark, 6, pp.9~18 (2022)
57. Lam WWT, "A systematic review of the applications of markerless motion capture(MMC)technology for clinical measurement in rehabilitation", J Neuroeng Rehabil, 20, pp.57~ (2023)
58. Sibley KG, "Video-based analyses of Parkinson’s disease severity : a brief review", J Parkinsons Dis, 11(S1), pp.S83~S93 (2021)
59. Adde L, "Early prediction of cerebral palsy by computer-based video analysis of general movements : a feasibility study", Dev Med Child Neurol, 52, pp.773~778 (2010)
60. Chen PC, "How to develop machine learning models for healthcare", Nat Mater, 18, pp.410~414 (2019)
61. Alaa AM, "Demystifying black-box models with symbolic metamodels", Paper presented at: Advances in Neural Information Processing Systems 32 (NeurIPS 2019), pp.32~ (2019)
62. Ehrmann DE, "Making machine learning matter to clinicians : model actionability in medical decision-making", NPJ Digit Med, 6, pp.7~ (2023)
63. Parimbelli E, "Why did AI get this one wrong?-tree-based explanations of machine learning model predictions", Artif Intell Med, 135, pp.102471~ (2023)
64. Busch Tde A, "Factors associated with lower gait speed among the elderly living in a developing country : a cross-sectional population-based study", BMC Geriatr, 15, pp.35~ (2015)
65. Pacini Panebianco G, "Analysis of the performance of 17 algorithms from a systematic review : influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements", Gait Posture, 66, pp.76~82 (2018)
66. Atallah L, "Sensor positioning for activity recognition using wearable accelerometers", IEEE Trans Biomed Circuits Syst, 5, pp.320~329 (2011)
67. Rantz M, "Randomized trial of intelligent sensor system for early illness alerts in senior housing", J Am Med Dir Assoc, 18, pp.860~870 (2017)
68. Luo Z, "Computer vision-based descriptive analytics of seniors’ daily activities for long-term health monitoring", Proc Mach Learn Res, 85, pp.1~18 (2018)
69. Gold R, "Developing a new reference standard : is validation necessary?", Acad Radiol, 17, pp.1079~1082 (2010)
70. Fusca M, "Validation of a wearable IMU system for gait analysis : protocol and application to a new system", Appl Sci, 8, pp.1167~ (2018)
71. van Lier HG, "A standardized validity assessment protocol for physiological signals from wearable technology : methodological underpinnings and an application to the E4 biosensor", Behav Res Methods, 52, pp.607~629 (2020)
72. Lonini L, "Activity recognition in individuals walking with assistive devices : the benefits of device-specific models", JMIR Rehabil Assist Technol, 4, pp.e8~ (2017)
73. Lanotte F, "Validity and reliability of a commercial wearable sensor system for measuring spatiotemporal gait parameters in a post-stroke population : the effects of walking speed and asymmetry", Physiol Meas, 44, pp.085005~ (2023)
74. Williams DR, "Race, socioeconomic status, and health : complexities, ongoing challenges, and research opportunities", Ann N Y Acad Sci, 1186, pp.69~101 (2010)
75. Prates MOR, "Assessing gender bias in machine translation : a case study with Google Translate", Neural Comput Appl, 32, pp.6363~6381 (2020)
76. Kohane IS, "What every reader should know about studies using electronic health record data but may be afraid to ask", J Med Internet Res, 23, pp.e22219~ (2021)
77. Zijlstra W, "Assessment of spatio-temporal gait parameters from trunk accelerations during human walking", Gait Posture, 18, pp.1~10 (2003)
78. Jasiewicz JM, "Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals", Gait Posture, 24, pp.502~509 (2006)
79. Nazmi N, "A review of classification techniques of EMG signals during isotonic and isometric contractions", Sensors(Basel), 16, pp.1304~ (2016)
80. Herrero JG, "Contextual and human factors in information fusion", pp.79~79 (2010), IOS Press Ebooks
81. Yu L, "Missing data preprocessing in credit classification : one-hot encoding or imputation?", Emerg Markets Financ Trade, 58, pp.472~482 (2020)
82. Guyon I, "An introduction to variable and feature selection", J Mach Learn Res, 3, pp.1157~1182 (2003)
83. Baxter J, "A model of inductive bias learning", J Artif Intell Res, 12, pp.149~198 (2000)
84. Vranas KC, "Identifying distinct subgroups of ICU patients : a machine learning approach", Crit Care Med, 45, pp.1607~1615 (2017)
85. Turgeman L, "Insights from a machine learning model for predicting the hospital Length of Stay(LOS)at the time of admission", Expert Syst Appl, 78, pp.376~385 (2017)
86. Lonini L, "Automatic detection of spasticity from flexible wearable sensors", Paper presented at : UbiComp ‘17 : Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, pp.133~136 (2017)
87. Lonini L, "Rapid screening of physiological changes associated with COVID-19 using soft-wearables and structured activities : a pilot study", IEEE J Transl Eng Health Med, 9, pp.4900311~ (2021)
88. Bacciu D, "A learning system for automatic Berg Balance Scale score estimation", Eng Appl Artif Intell, 66, pp.60~74 (2017)
89. Krishnan R, "Self-supervised learning in medicine and healthcare", Nat Biomed Eng, 6, pp.1346~1352 (2022)
90. Yoon J, "Anonymization through data synthesis using generative adversarial networks(ADS-GAN)", IEEE J Biomed Health Inform, 24, pp.2378~2388 (2020)
91. Yang X, "A large language model for electronic health records", NPJ Digit Med, 5, pp.194~ (2022)
92. Saeb S, "The need to approximate the use-case in clinical machine learning", Gigascience, 6, pp.1~9 (2017)
93. Ramspek CL, "External validation of prognostic models : what, why, how, when and where?", Clin Kidney J, 14, pp.49~58 (2020)
94. Gautam A, "MyoNet : a transfer-learning-based LRCN for lower limb movement recognition and knee joint angle prediction for remote monitoring of rehabilitation progress from sEMG", IEEE J Transl Eng Health Med, 8, pp.2100310~ (2020)
95. Du C, "Assessing physical rehabilitation exercises using graph convolutional network with self-supervised regularization", Annu Int Conf IEEE Eng Med Biol Soc, 2021, pp.281~285 (2021)
96. Chen RJ, "Synthetic data in machine learning for medicine and healthcare", Nat Biomed Eng, 5, pp.493~497 (2021)
97. Che Z, "Boosting deep learning risk prediction with generative adversarial networks for electronic health records", Paper presented at : 2017 IEEE International Conference on Data Mining(ICDM), pp.787~792 (2017)
98. Shen C, "Learning for dose allocation in adaptive clinical trials with safety constraints", Paper presented at : ICML'20 : Proceedings of the 37th International Conference on Machine Learning, pp.8730~8740 (2020)
99. Arora A, "The promise of large language models in health care", Lancet, 401, pp.641~ (2023)
100. Murphy C, "Generative AI in spinal cord injury research and care : opportunities and challenges ahead", J Spinal Cord Med, 46, pp.341~342 (2023)
101. Thirunavukarasu AJ, "Large language models will not replace healthcare professionals : curbing popular fears and hype", J R Soc Med, 116, pp.181~182 (2023)
102. Singhal K, "Large language models encode clinical knowledge", Nature, 620, pp.E19~ (2023)
103. Vasey B, "Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence : DECIDE-AI", Nat Med, 28, pp.924~933 (2022)
104. Sahiner B, "Data drift in medical machine learning : implications and potential remedies", Br J Radiol, 96, pp.20220878~ (2023)

관련 논문AI추천