scholarly journals Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0213258 ◽  
Author(s):  
Hong J. Kan ◽  
Hadi Kharrazi ◽  
Hsien-Yen Chang ◽  
Dave Bodycombe ◽  
Klaus Lemke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Health Care ◽  
Linear Regression ◽  
Health Care Costs ◽  
Risk Adjustment ◽  
Regression Models ◽  
Linear Regression Models ◽  
Care Costs

Associations between perceptions of care experiences and receipt of mental health care among older adults with cancer.

2020 ◽  
Vol 38 (29_suppl) ◽  
pp. 175-175
Author(s):  
Lisa M Lines ◽  
Daniel H Barch ◽  
Diana Zabala ◽  
Michael T. Halpern ◽  
Paul Jacobsen ◽  
...  
Keyword(s):  
Mental Health ◽  
Older Adults ◽  
Health Care ◽  
Linear Regression ◽  
Mental Health Care ◽  
Mood Disorders ◽  
Regression Models ◽  
Case Mix ◽  
Linear Regression Models ◽  
Fair Poor

175 Background: Older adults with cancer and worse self-rated mental health report worse care experiences. We hypothesized that, controlling for health and demographic characteristics, older adults with cancer who received care for anxiety or mood disorders would report better care experiences. Methods: We used SEER-CAHPS data to identify Medicare beneficiaries, aged 66 and over, diagnosed from August 2006 through December 2013 with one of the 10 most prevalent solid tumor malignancies. To identify utilization for anxiety or mood disorders (screening, diagnosis, or treatment), we analyzed inpatient, outpatient, home health, physician, and prescription drug claims from 12 months before through up to 5 years after cancer diagnosis. Outcomes of interest were global care experience ratings (Overall Care, Personal Doctor, and Specialist; rated on a 0-10 scale) and composite measures (Getting Needed Care, Getting Care Quickly, and Doctor Communication; scored from 0-100). We estimated linear regression models and also used a Bayesian Model Averaging approach, adjusting for standard case-mix adjustors (including sociodemographics and self-reported general health and mental health status [MHS]) and other characteristics, including cancer site and stage at diagnosis. We also included interaction terms between mental health care utilization and MHS. Results: Approximately 22% of the overall sample (n = 4,998) had both cancer and a claim for an anxiety or mood disorder, and of those individuals, 18% reported fair/poor MHS. Only 7% of those in the cancer-only cohort reported fair/poor MHS. Before adjusting for mental health utilization, worse MHS was significantly associated with worse experience of care. After accounting for anxiety/mood disorder-related utilization, linear regression models showed no significant associations between fair/poor MHS and worse care experiences, while Bayesian models found that reliable associations remained between worse MHS and lower global ratings of Overall Care and Specialist. Conclusions: Utilization for anxiety/mood disorders mediates the association between fair/poor MHS and worse care experiences. Although MHS is a case-mix adjustor for CAHPS public reporting, it is important to recognize that care for anxiety or mood disorders may improve care experiences among seniors with cancer.

scholarly journals Application of Machine Learning Techniques to Predict Mechanical Properties for Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for production of end-use parts. Prediction of mechanical properties can be one of the possible ways to improve the repeatability of the results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress and elongation at break for polyamide 2200 (also known as PA12). EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models have prediction accuracy higher than 80% only for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about material properties, these models need to be improved in the future based on additional experimental work.

Longitudinal association between panic disorder and health care costs in older adults

2019 ◽  
Vol 36 (12) ◽  
pp. 1135-1142
Author(s):  
Johanna Katharina Hohls ◽  
Hans‐Helmut König ◽  
Dirk Heider ◽  
Hermann Brenner ◽  
Friederike Böhlen ◽  
...  
Keyword(s):  
Older Adults ◽  
Health Care ◽  
Panic Disorder ◽  
Health Care Costs ◽  
Longitudinal Association ◽  
Care Costs

scholarly journals Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Vol 9 (6) ◽  
pp. 1060
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for the manufacturing industry due to flexibility in its design and functionality, but inconsistency in quality is one of the major limitations preventing utilizing this technology for the production of end-use parts. The prediction of mechanical properties can be one of the possible ways to improve the repeatability of results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress, and elongation at break for polyamide 2200 (also known as PA12). An EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models only have prediction accuracy higher than 80% for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about the material properties, these models need to be improved in the future based on additional experimental work.

scholarly journals The Influence of Sitting, Standing, and Stepping Bouts on Cardiometabolic Health Markers in Older Adults

2021 ◽  
pp. 1-9
Author(s):  
Simone J.J.M. Verswijveren ◽  
Cormac Powell ◽  
Stephanie E. Chappel ◽  
Nicola D. Ridgers ◽  
Brian P. Carson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Body Composition ◽  
Linear Regression ◽  
Regression Models ◽  
Fasting Glucose ◽  
Cardiometabolic Health ◽  
Linear Regression Models ◽  
Cross Sectional

Aside from total time spent in physical activity behaviors, how time is accumulated is important for health. This study examined associations between sitting, standing, and stepping bouts, with cardiometabolic health markers in older adults. Participants from the Mitchelstown Cohort Rescreen Study (N = 221) provided cross-sectional data on activity behaviors (assessed via an activPAL3 Micro) and cardiometabolic health. Bouts of ≥10-, ≥30-, and ≥60-min sitting, standing, and stepping were calculated. Linear regression models were fitted to examine the associations between bouts and cardiometabolic health markers. Sitting (≥10, ≥30, and ≥60 min) and standing (≥10 and ≥30 min) bouts were detrimentally associated with body composition measures, lipid markers, and fasting glucose. The effect for time spent in ≥60-min sitting and ≥30-min standing bouts was larger than shorter bouts. Fragmenting sitting with bouts of stepping may be targeted to benefit cardiometabolic health. Further insights for the role of standing need to be elicited.

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