scholarly journals Identifying the Most Appropriate Intervention Targets Using Prediction Model Based on a Machine-Learning Method: A Retrospective Analysis of a Health Promotion Program for Improving Participation in General Health Check-Up

2018 ◽  
Vol 14 (4) ◽  
Author(s):  
Akihiro Shimoda ◽  
Yoshiyuki Saito ◽  
Chieko Ooe ◽  
Daisuke Ichikawa ◽  
Ataru Igarashi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Promotion ◽  
Prediction Model ◽  
Retrospective Analysis ◽  
General Health ◽  
Health Check ◽  
Health Promotion Program ◽  
Machine Learning Method ◽  
Learning Method ◽  
Promotion Program

scholarly journals Bilateral Globus Pallidus Internus Deep Brain Stimulation for Parkinson’s Disease: Therapeutic Effects and Motor Outcomes Prediction

2020 ◽  
Author(s):  
Dingding Shen ◽  
Linhao Cao ◽  
Yun Ling ◽  
Dianyou Li ◽  
Kang Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Prediction Model ◽  
Machine Learning Method ◽  
Learning Method ◽  
One Year ◽  
Deep Brain

Abstract Background: Deep brain stimulation (DBS) has emerged as a highly effective surgical treatment for advanced Parkinson’s disease (PD). Good response in levodopa challenge test has suggested as criterion to identify optimal candidates for surgery. However, the response to levodopa and DBS is not always congruent, and predictive value of the levodopa test remains controversial. This study was set out to identify predictors of response to DBS and develop a novel prediction model evaluating DBS candidacy. Methods: Herein, we retrospectively analyzed 62 consecutive PD patients who underwent bilateral globus pallidus interna (GPi) DBS from 2016 to 2019.  The changes in UPDRS-III (Unified Parkinson’s Disease Rating Scale part III) total and subscores after surgery at one-year follow-up were evaluated and potential predictor variables were also collected. In the training cohort of 29 patients, we developed a novel machine learning method with 5-fold cross validations implementing on these variables to predict GPi DBS treatment outcomes in a multivariate linear analysis. Furthermore, the machine learning model was externally validated with another cohort of 33 GPi DBS PD patients.Results: GPi DBS significantly improved postoperative motor function of PD patients. The overall UPDRS-III scores improved by 30.4%, with highest improvement in tremor (75.0%), followed by limb bradykinesia (27.5%), rigidity (27.3%) and axial bradykinesia (22.4%). Most intriguingly, improvement in tremor can be predicted with high accuracy using this prediction model (adjusted R2= 0.82 for absolute improvement, and adjusted R2 = 0.76 for relative improvement), in which off medication tremor subscore was identified as the most powerful preoperative predictor. In the external validation cohort, the machine learning method showed good predictive performance.Conclusions: We confirmed the effects of bilateral GPi-DBS with a one-year follow-up. The good performance of the present prediction model demonstrated the utility of machine-learning based motor response prediction after GPi DBS, based on clinical preoperative variables.

Better with age: A health promotion program for men at midlife.

2017 ◽  
Vol 18 (1) ◽  
pp. 40-49 ◽  
Author(s):  
David Mellor ◽  
Catherine Connaughton ◽  
Marita P. McCabe ◽  
Gemma Tatangelo
Keyword(s):  
Health Promotion ◽  
Health Promotion Program ◽  
Promotion Program

Effectiveness of Tailored Health Promotion Program for Reducing Cardiovascular Risk Factors in Subway Workers

2006 ◽  
Vol 18 (1) ◽  
pp. 15 ◽  
Author(s):  
Seon Young Lee ◽  
Kang Sook Lee ◽  
Jung Wan Koo ◽  
Hyun Woo Yim ◽  
Hyoung Ryoul Kim ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Promotion ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Health Promotion Program ◽  
Promotion Program
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