scholarly journals A Machine Learning Approach for Identifying Children at Risk of Suboptimal Adherence to Growth Hormone Therapy

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A672-A673
Author(s):  
Amalia Spataru ◽  
Paula van Dommelen ◽  
Lilian Arnaud ◽  
Quentin Le Masne ◽  
Silvia Quarteroni ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Growth Hormone ◽  
Clinical Outcomes ◽  
Growth Disorders ◽  
Learning Approach ◽  
Increased Risk ◽  
Machine Learning Approach ◽  
Suboptimal Adherence ◽  
Optimal Adherence

Abstract Background: Suboptimal adherence to recombinant human growth hormone (r-hGH) treatment can lead to suboptimal clinical outcomes. Being able to identify children who are at risk of suboptimal adherence in the near future, and take adequate measures to support adherence, may maximize clinical outcomes. Our aim was to develop a model based on data from the first 3 months of treatment to identify potential indicators of suboptimal adherence and predict adherence over the following 9 months using a machine learning approach. Methods: We assessed adherence to r-hGH treatment in children with growth disorders in their first 12 months of treatment using a connected autoinjector and e-device (easypod™), which automatically transmits adherence data via an online portal (easypod™ connect). We selected children who started the use of the device before 18 years of age and who transmitted their injection data for at least 12 months. Adherence (mg injected/mg prescribed) between 4-12 months (outcome) was categorized as optimal (≥85%) versus suboptimal (<85%). In addition to adherence over the first 3 months, comfort settings (needle speed, injection depth, injection speed, injection time), number of transmissions, number of dose changes, age at start and sex were used as potential indicators of suboptimal adherence. Several machine learning models were optimized on a class-balanced training dataset using a 5-fold cross-validation scheme. On the best performing model, machine learning interpretation techniques and chi-squared statistical tests were applied to extract the statistically significant indicators of suboptimal and optimal adherence. Results: Anonymized data were available for 10,943 children. The optimal prediction performances were achieved with the random forest algorithm. The mean adherence and the adherence standard deviation over the first 3 months were the two most important features for predicting adherence in the following 9 months. Not using the system’s features (e.g. not transmitting data often and not changing some of the comfort settings, such as the needle speed setting), as well as starting treatment at an older age were significantly associated with an increased risk of suboptimal adherence (p<0.001). When tested on first-time seen data following the same class distribution as the original data, the model achieved a sensitivity of 80% and a specificity of 81%. Conclusions: We developed a model predicting whether a child’s adherence in the following 9 months will be below or above the optimal threshold (85%) based on early data from the first 3 months of treatment and we identified the indicators of suboptimal adherence. These results can be used to identify children needing additional medical or other support to reach optimal adherence and therefore optimal clinical outcomes.

scholarly journals Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2828 ◽  
Author(s):  
Dylan Kobsar ◽  
Reed Ferber
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Movement Patterns ◽  
Movement Pattern ◽  
Human Movement ◽  
Sensor Data ◽  
Learning Approach ◽  
Wearable Sensor ◽  
Machine Learning Approach ◽  
Subject Specific

Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill walking at a preferred speed. Wearable sensor data from the 2 pre-intervention gait trials were used to define each individual’s typical movement pattern using a one-class support vector machine (OCSVM). The percentage of strides defined as outliers, based on the pre-intervention gait data and the OCSVM, were used to define the overall change in an individual’s movement pattern. The correlation between the change in movement patterns following the intervention (i.e., percentage of outliers) and improvement in self-reported clinical outcomes (e.g., pain and function) was assessed using a Spearman rank correlation. The number of outliers observed post-intervention exhibited a large association (ρ = 0.78) with improvements in self-reported clinical outcomes. These findings demonstrate a proof-of-concept and a novel methodological approach for integrating machine learning and wearable sensor data. This approach provides an objective and evidence-informed way to understand clinically important changes in human movement patterns in response to exercise therapy.

scholarly journals Machine-learning analysis identifies digital behavioral phenotypes for engagement and health outcome efficacy of mHealth interventions for obesity: post-hoc analyses of a randomized trial (Preprint)

2021 ◽  
Author(s):  
Meelim Kim ◽  
Jaeyeong Yang ◽  
Woo-Young Ahn ◽  
Hyung Jin Choi
Keyword(s):  
Machine Learning ◽  
Cognitive Behavioral Therapy ◽  
Health Outcomes ◽  
Clinical Outcomes ◽  
Weight Change ◽  
Behavioral Therapy ◽  
Cognitive Behavioral ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Learning Analysis

BACKGROUND The digital healthcare community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes. OBJECTIVE This study aimed to investigate the performance of multivariate phenotypes predicting the engagement rate and health outcomes of digital cognitive behavioral therapy (dCBT) using a machine learning approach. METHODS We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy (dCBT) for eight weeks. To discriminate the important characteristics, we conducted a machine-learning analysis. RESULTS A higher engagement rate was associated with higher weight loss at 8 weeks (r = -0.59, p < 0001) and 24 weeks (r = -0.52, p = 0001). The machine learning approach revealed distinct multivariate profiles associated with varying impacts on the outcomes. Lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, eight types of digital phenotypes predicted engagement rates (mean R2 = 0416, SD = 0006). The prediction of short-term weight change (mean R2 = 0382, SD = 0015) was associated with six different digital phenotypes. Lastly, two behavioral measures of digital phenotypes were associated with a long-term weight change (mean R2 = 0590, SD = 0011). CONCLUSIONS Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of digital intervention with the machine learning method. Our results also highlight the importance of assessing multiple aspects of motivation before and during the intervention to improve both engagement rate and clinical outcomes. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics. CLINICALTRIAL ClinicalTrials.gov NCT03465306 (Retrieved September 18, 2017, https://register.clinicaltrials.gov/NCT03465306)

scholarly journals Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4017
Author(s):  
Ghasem Akbari ◽  
Mohammad Nikkhoo ◽  
Lizhen Wang ◽  
Carl P. C. Chen ◽  
Der-Sheng Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
The Elderly ◽  
Microsoft Kinect ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approach ◽  
Kinect Sensor ◽  
Increased Risk ◽  
Machine Learning Approach

Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.

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