Estimating the Efficacy of Common Treatments in Children and Young Adults Diagnosed with Cerebral Palsy Using Three Machine Learning Algorithms

Schwartz, Michael H.; Ries, Andrew J.; Georgiadis, Andrew

doi:10.1101/2021.10.06.21264624

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…We are specifically thinking of variables like spasticity (ICF category b750 –Motor reflex functions and b735 –Muscle tone functions), motor control (b760 –Control of voluntary movement functions), strength (b730 –Muscle power), range-of-motion, bony alignment, muscle contracture, and joint health (s740 –Structure of pelvic region, s750 –Structure of lower extremity, s760 –Structure of the trunk, and s770 –Additional musculoskeletal structures related to movement). The Structure and Function node is the core of the proposed causal model since elements of this node are the direct targets of treatments, and treatment effects are largest at this level [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

A model for understanding the causes and consequences of walking impairments

et al. 2022

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Walking is an important skill with positive impacts on health, function, and well-being. Many disorders impair walking and its positive impacts through a variety of complex and interrelated mechanisms. Any attempt to understand walking impairments, or the effects of interventions intended to treat these impairments, must respect this complexity. Therefore, our main objectives in conducting this study were to (1) propose a comprehensive model for quantifying the causes and consequences of walking impairments and (2) demonstrate the potential utility of the model for supporting clinical care and addressing basic scientific questions related to walking. To achieve these goals, we introduced a model, described by a directed acyclic graph, consisting of 10 nodes and 23 primary causal paths. We gave detailed descriptions of each node and path based on domain knowledge. We then demonstrated the model’s utility using a large sample of gait data (N = 9504) acquired as part of routine care at a regional referral center. We analyzed five relevant examples that involved many of the model’s nodes and paths. We computed causal effect magnitudes as Shapley values and displayed the overall importance of variables (mean absolute Shapley value), the variation of Shapley values with respect to underlying variables, and Shapley values for individual observations (case studies). We showed that the model was plausible, captured some well-known cause-effect relationships, provided new insights into others, and generated novel hypotheses requiring further testing through simulation or experiment. To aid in transparency, reproducibility, and future enhancements we have included an extensively commented Rmarkdown file and a deidentified data set.

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Section: Methodsmentioning

confidence: 99%

A model for understanding the causes and consequences of walking impairments

et al. 2022

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“…We are specifically thinking of variables like spasticity (ICF category b750 -Motor reflex functions and b735 -Muscle tone functions), motor control (b760 -Control of voluntary movement functions), strength (b730 -Muscle power), range-of-motion, bony alignment, muscle contracture, and joint health (s740 -Structure of pelvic region, s750 -Structure of lower extremity, s760 -Structure of the trunk, and s770 -Additional musculoskeletal structures related to movement). The Structure and Function node is the core of the proposed causal model since elements of this node are the direct targets of treatments, and treatment effects are largest at this level (12). Gait Mechanics are the elements of dynamics pertaining to the motion of segments and joints during gait (usually walking, but running, skipping, and other gaits could be considered).…”

Section: Model Nodesmentioning

confidence: 99%

A Model for Understanding the Causes and Consequences of Walking Impairments

Schwartz

Steele

Ries

et al. 2022

Preprint

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Walking is an important skill with positive impacts on health, function, and well-being. Many disorders impair walking and its positive impacts through a variety of complex and interrelated mechanisms. Any attempt to understand walking impairments, or the effects of interventions intended to treat these impairments, must respect this complexity. Therefore, our main objectives in conducting this study were to (1) propose a comprehensive model for quantifying the causes and consequences of walking impairments and (2) demonstrate the potential utility of the model for supporting clinical care and addressing basic scientific questions related to walking. To achieve these goals, we introduced a model, described by a directed acyclic graph, consisting of 10 nodes and 23 primary causal paths. We gave detailed descriptions of each node and path based on domain knowledge. We then demonstrated the model's utility using a large sample of gait data (N = 9504) acquired as part of routine care at a regional referral center. We analyzed five relevant examples that involved many of the model's nodes and paths. We computed causal effect magnitudes as Shapley values and displayed the overall importance of variables (mean absolute Shapley value), the variation of Shapley values with respect to underlying variables, and Shapley values for individual observations (case studies). We showed that the model was plausible, captured some well-known cause-effect relationships, provided new insights into others, and generated novel hypotheses requiring further testing through simulation or experiment. To aid in transparency, reproducibility, and future enhancements we have included an extensively commented Rmarkdown file and a deidentified data set.

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A systematic review on machine learning approaches in cerebral palsy research

Nahar,

Paul,

Saikia

2024

PeerJ

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Background This review aims to explore advances in the field of cerebral palsy (CP) focusing on machine learning (ML) models. The objectives of this study is to analyze the advances in the application of ML models in the field of CP and to compare the performance of different ML algorithms in terms of their effectiveness in CP identification, classifying CP into its subtypes, prediction of abnormalities in CP, and its management. These objectives guide the review in examining how ML techniques are applied to CP and their potential impact on improving outcomes in CP research and treatment. Methodology A total of 20 studies were identified on ML for CP from 2013 to 2023. Search Engines used during the review included electronic databases like PubMed for accessing biomedical and life sciences, IEEE Xplore for technical literature in computer, Google Scholar for a broad range of academic publications, Scopus and Web of Science for multidisciplinary high impact journals. Inclusion criteria included articles containing keywords such as cerebral palsy, machine learning approaches, outcome response, identification, classification, diagnosis, and treatment prediction. Studies were included if they reported the application of ML techniques for CP patients. Peer reviewed articles from 2013 to 2023 were only included for the review. We selected full-text articles, clinical trials, randomized control trial, systematic reviews, narrative reviews, and meta-analyses published in English. Exclusion criteria for the review included studies not directly related to CP. Editorials, opinion pieces, and non-peer-reviewed articles were also excluded. To ensure the validity and reliability of the findings in this review, we thoroughly examined the study designs, focusing on the appropriateness of their methodologies and sample sizes. To synthesize and present the results, data were extracted and organized into tables for easy comparison. The results were presented through a combination of text, tables, and figures, with key findings emphasized in summary tables and relevant graphs. Results Random forest (RF) is mainly used for classifying movements and deformities due to CP. Support vector machine (SVM), decision tree (DT), RF, and K-nearest neighbors (KNN) show 100% accuracy in exercise evaluation. RF and DT show 94% accuracy in the classification of gait patterns, multilayer perceptron (MLP) shows 84% accuracy in the classification of CP children, Bayesian causal forests (BCF) have 74% accuracy in predicting the average treatment effect on various orthopedic and neurological conditions. Neural networks are 94.17% accurate in diagnosing CP using eye images. However, the studies varied significantly in their design, sample size, and quality of data, which limits the generalizability of the findings. Conclusion Clinical data are primarily used in ML models in the CP field, accounting for almost 47%. With the rise in popularity of machine learning techniques, there has been a rise in interest in developing automated and data-driven approaches to explore the use of ML in CP.

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Estimating the Efficacy of Common Treatments in Children and Young Adults Diagnosed with Cerebral Palsy Using Three Machine Learning Algorithms

Cited by 3 publications

References 37 publications

A model for understanding the causes and consequences of walking impairments

A model for understanding the causes and consequences of walking impairments

A Model for Understanding the Causes and Consequences of Walking Impairments

A systematic review on machine learning approaches in cerebral palsy research

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