Machine learning approaches to predict rehabilitation success based on clinical and patient-reported outcome measures

Tschuggnall, Michael; Grote, Vincent; Pirchl, Michael; Holzner, Bernhard; Rumpold, Gerhard; Fischer, Michael J.

doi:10.1016/j.imu.2021.100598

Cited by 20 publications

(7 citation statements)

References 48 publications

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“…There is no doubt that their prevalence and pervasiveness in today's healthcare have stimulated rapid growth in research interest in medical and computing in recent years. In medical informatics, research related to machine learning and rehabilitation have studied such data mining applications in healthcare and related phenomena by paying attention to prediction model [2,11,12], analysis pattern of data [13 -15], decision-making [2], and health discourse [16]. To grasp the breadth of work in studying what researchers have done on using data mining for post-stroke rehabilitation, defining the scope of the data mining concept, gamification, and rehabilitation has assisted the author in better situating the range of study.…”

Section: A Defining Data Mining Gamification and Rehabilitationmentioning

confidence: 99%

“…The previous study in rehabilitation training, such as by [11], produced a prediction model using regression and classification algorithm on the success of training based on the patient's clinical report that differentiates the percentage of admission and discharge value. Using the model, they supposed that medical control and rehabilitation therapy paths might be realized and enhanced.…”

Section: ) Topic 2: Prediction Modelmentioning

confidence: 99%

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Mapping Data Mining Technique and Gamification Approach for Studying Post-Stroke Rehabilitation Training: A Systematic Literature Review

et al. 2023

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Data mining has been widely used in healthcare to provide treatment and care recommendations based on a collective prediction of individual conditions. For rehabilitation, various data mining techniques have been applied to predict and recommend suitable recovery paths and training. Also, the gamification concept was applied to rehabilitation training to motivate the patient to follow the training until the end. Researchers have conducted considerable research to investigate the validity and effectiveness of those techniques on massive patient data on specific conditions and treatment contexts. However, it is still unclear how to effectively offer customized rehabilitation training to stroke patients using gamification and data mining approaches. Thus, to understand how researchers studied them, we examined 34 peer-reviewed articles published in computer science and medical proceedings and journals between 2012 and 2022. We systematically reviewed the data mining and gamification techniques researchers had applied for post-stroke rehabilitation and related prediction models resulting from the data mining processes. As a result of the analyses, three significant contributions are identified. This article 1) identifies trends in data mining and gamification used in personalized post-stroke rehabilitation training; 2) maps trends in the study of data mining and gamification in post-stroke rehabilitation; and 3) identifies underexplored studies for future work. There is a definite need to continue developing and researching intervention strategies related to rehabilitation to address recovery problems by providing accuracy and protection of healthcare, as well as incorporating components that promote patients' motivation and engagement.

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Section: A Defining Data Mining Gamification and Rehabilitationmentioning

confidence: 99%

Section: ) Topic 2: Prediction Modelmentioning

confidence: 99%

Mapping Data Mining Technique and Gamification Approach for Studying Post-Stroke Rehabilitation Training: A Systematic Literature Review

et al. 2023

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“…ML analyzes these data to determine which factors most likely lead to better outcomes[ 45 ]. In a study on hip, knee, and foot trauma patients[ 46 ], ML was able to predict rehabilitation success. Hopefully, by applying these findings in the future, trauma patients will receive individualized treatments that provide the optimal outcome of healthier and happier patients.…”

Section: Predictive Analysismentioning

confidence: 99%

Application of artificial intelligence in trauma orthopedics: Limitation and prospects

Salimi,

Parry,

Shahrokhi

et al. 2023

World J Clin Cases

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The varieties and capabilities of artificial intelligence and machine learning in orthopedic surgery are extensively expanding. One promising method is neural networks, emphasizing big data and computer-based learning systems to develop a statistical fracture-detecting model. It derives patterns and rules from outstanding amounts of data to analyze the probabilities of different outcomes using new sets of similar data. The sensitivity and specificity of machine learning in detecting fractures vary from previous studies. AI may be most promising in the diagnosis of less-obvious fractures that are more commonly missed. Future studies are necessary to develop more accurate and effective detection models that can be used clinically.

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“…Other works have focused on literature related to specific stages of cancer disease, regardless of the cancer type, such as cancer rehabilitation (Tschuggnall et al. 2021 ) and cancer survival prediction (Deepa and Gunavathi 2022 ). There also exist insightful reviews on the most popular ML techniques for cancer research (see Maurya et al.…”

Section: Introductionmentioning

confidence: 99%

Bibliometric analysis of the global scientific production on machine learning applied to different cancer types

Ruiz-Fresneda

Gijón

Morales-Álvarez

2023

Environ Sci Pollut Res

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Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.

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Machine learning approaches to predict rehabilitation success based on clinical and patient-reported outcome measures

Cited by 20 publications

References 48 publications

Mapping Data Mining Technique and Gamification Approach for Studying Post-Stroke Rehabilitation Training: A Systematic Literature Review

Mapping Data Mining Technique and Gamification Approach for Studying Post-Stroke Rehabilitation Training: A Systematic Literature Review

Application of artificial intelligence in trauma orthopedics: Limitation and prospects

Bibliometric analysis of the global scientific production on machine learning applied to different cancer types

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