Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb

Iwamoto, Yoshiki; Imura, Takeshi; Tanaka, Ryo; Mitsutake, Tsubasa; Jung, Hungu; Suzukawa, Takahiro; Taki, Shingo; Imada, Naoki; Inagawa, Tetsuji; Araki, Hayato; Araki, Osamu

doi:10.1016/j.jstrokecerebrovasdis.2022.106517

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…Iwamoto et al [ 36 ] found that FMA-UE scores at the initiation of inpatient rehabilitation were the most important predictor for identifying participants that would achieve an MCID on the FMA-UE 30 days after treatment. Harari et al [ 4 ] built ML models to predict discharge scores of FIM, Ten-Meter Walk Test, Six-Minute Walk Test, and the Berg Balance Scale after inpatient rehabilitation stay; they found that the most important predictors for these scores were their own scores at admission.…”

Section: Discussionmentioning

confidence: 99%

“…In chronic stroke, baseline FMA-UE was found to predict postintervention UL motor function in two studies using ML [ 13 , 14 ]. For studies using traditional statistical analysis, baseline BBT was found to predict postintervention outcomes of activities and participation [ 37 , 38 ], and FMA-UE was found to predict both UL motor function and activities and participation [ 25 , 36 , 39 ]. Our findings further supported the predictive value of UL motor function for postintervention achievement of MCID in the PROMs of activities and participation in chronic stroke.…”

Section: Discussionmentioning

confidence: 99%

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Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study

Chen

Lin

et al. 2023

J NeuroEngineering Rehabil

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Background Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient-centered care. However, predictors for patient-centered outcome measures for activities and participation in stroke rehabilitation requires further investigation. Methods This study retrospectively analyzed data collected for our previous studies from 124 participants. Machine Learning models were built to predict postintervention improvement of patient-reported outcome measures of daily activities (i.e, the Motor Activity Log and the Nottingham Extended Activities of Daily Living) and participation (i.e, the Activities of Daily Living domain of the Stroke Impact Scale). Three groups of 18 potential predictors were included: patient demographics, stroke characteristics, and baseline assessment scores that encompass all three domains under the framework of International Classification of Functioning, Disability and Health. For each target variable, classification models were built with four algorithms, logistic regression, k-nearest neighbors, support vector machine, and random forest, and with all 18 potential predictors and the most important predictors identified by feature selection. Results Predictors for the four target variables partially overlapped. For all target variables, their own baseline scores were among the most important predictors. Upper-limb motor function and selected demographic and stroke characteristics were also among the important predictors across the target variables. For the four target variables, prediction accuracies of the best-performing models with 18 features ranged between 0.72 and 0.96. Those of the best-performing models with fewer features ranged between 0.72 and 0.84. Conclusions Our findings support the feasibility of using Machine Learning for the prediction of stroke rehabilitation outcomes. The study was the first to use Machine Learning to identify important predictors for postintervention improvement on four patient-reported outcome measures of activities and participation in chronic stroke. The study contributes to precision rehabilitation and patient-centered care, and the findings may provide insights into the identification of patients that are likely to benefit from stroke rehabilitation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study

Chen

Lin

et al. 2023

J NeuroEngineering Rehabil

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“…Likewise, the proposed study determined the impact of the stroke using MBI. Most studies (Shah et al, 1989;Lee et al, 2016;Sale et al, 2018;Thakkar et al, 2020;Jiang et al, 2021;Sohn et al, 2021;Iwamoto et al, 2022) applied RF to identify the functional recovery and possibilities of readmission of SPs. RF and ANN achieved an effective outcome due to their internal structure that supports decision-making.…”

Section: Discussionmentioning

confidence: 99%

“…Acute ischemic stroke (AIS) risk increases globally, presenting severe concerns for healthcare systems (Sale et al, 2018). In recent years, the healthcare industry has made significant progress toward a data-driven approach to advance precision medicine to a greater level (Shah et al, 1989;Lee et al, 2016;Iwamoto et al, 2022). The systematic development and utilization of risk prediction tools may enhance the implementation of preventative measures.…”

Section: Journal Of Disability Research 2023mentioning

confidence: 99%

“…Stroke prognosis models demand enormous computational resources that challenge the healthcare centers in implementing the prediction. Kim et al (2021) DL is a class of ML algorithms that attempts to generalize to more significant abstractions by employing a series of nonlinear transformations (Shah et al, 1989;Sale et al, 2018;Iwamoto et al, 2022). DL techniques can handle newly acquired information in computer vision, speech recognition, natural language processing, and voice/signal processing.…”

Section: Related Studiesmentioning

confidence: 99%

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Predicting Functional Recovery of Stroke Rehabilitation Using a Deep Learning Technique

Ali Aljarallah,

Dutta,

Wahab Sait

et al. 2023

Journal of Disability Research

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Stroke survivors have access to a wide range of drug and non-drug treatments for the resulting physiological and functional problems. However, comprehensive therapies typically fail to meet the demands of a large percentage of patients. The recent clinical studies to improve protocol scientific evidence have resulted in a new development phase for rehabilitation medicine. Stroke rehabilitation supports individuals to lead a normal life. It assists the physicians in offering an effective environment to the patients. The evaluation of a patient’s progress in rehabilitation is based on the clinician’s subjective observations and the patient’s self-reported data. Deep learning techniques offer novel forms of individualized treatment. Nonetheless, missing data is one of the crucial factors that reduces the performance of data classification techniques. Thus, there is a demand for functional recovery prediction models for supporting stroke patients (SPs) to improve their quality of life. In this study, the researchers intend to build a framework for predicting functional outcomes using the electronic health record data of SPs. An attention-based bidirectional gated recurrent unit is used for developing the data imputation model. In addition, a shallow-convolutional neural network is employed for predicting the functional outcomes based on the modified Barthel Index. Data from 356 SPs were utilized for evaluating the performance of the proposed framework with the benchmark metrics and baseline models. The findings reveal that the proposed framework outperforms the state-of-the-art classification by achieving an average accuracy, precision, recall, F1-measure, specificity, and sensitivity of 98.18, 97.48, 98, 97.74, 96.74, and 97.24, respectively. The proposed framework can be implemented in real time to support SPs.

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Development and External Validation of a Motor Intention–Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke

Hu,

Ti,

Shi

et al. 2024

Archives of Physical Medicine and Rehabilitation

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Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb

Cited by 7 publications

References 20 publications

Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study

Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study

Predicting Functional Recovery of Stroke Rehabilitation Using a Deep Learning Technique

Development and External Validation of a Motor Intention–Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke

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