scholarly journals Classification Tree Analysis Based On Machine Learning for Predicting Linezolid-Induced Thrombocytopenia

2021 ◽  
Vol 110 (5) ◽  
pp. 2295-2300
Author(s):  
Saki Takahashi ◽  
Yasuhiro Tsuji ◽  
Hidefumi Kasai ◽  
Chika Ogami ◽  
Hitoshi Kawasuji ◽  
...  
2015 ◽  
Vol 26 (3) ◽  
pp. 443-454 ◽  
Author(s):  
Gregory M. Dominick ◽  
Mia A. Papas ◽  
Michelle L. Rogers ◽  
William Rakowski

2021 ◽  
Author(s):  
Christian A Betancourt ◽  
Panagiota Kitsantas ◽  
Deborah G Goldberg ◽  
Beth A Hawks

ABSTRACT Introduction Military veterans continue to struggle with addiction even after receiving treatment for substance use disorders (SUDs). Identifying factors that may influence SUD relapse upon receiving treatment in veteran populations is crucial for intervention and prevention efforts. The purpose of this study was to examine risk factors that contribute to SUD relapse upon treatment completion in a sample of U.S. veterans using logistic regression and classification tree analysis. Materials and Methods Data from the 2017 Treatment Episode Data Set—Discharge (TEDS-D) included 40,909 veteran episode observations. Descriptive statistics and multivariable logistic regression analysis were conducted to determine factors associated with SUD relapse after treatment discharge. Classification trees were constructed to identify high-risk subgroups for substance use after discharge from treatment for SUDs. Results Approximately 94% of the veterans relapsed upon discharge from outpatient or residential SUD treatment. Veterans aged 18-34 years old were significantly less likely to relapse than the 35-64 age group (odds ratio [OR] 0.73, 95% confidence interval [CI]: 0.66, 0.82), while males were more likely than females to relapse (OR 1.55, 95% CI: 1.34, 1.79). Unemployed veterans (OR 1.92, 95% CI: 1.67, 2.22) or veterans not in the labor force (OR 1.29, 95% CI: 1.13, 1.47) were more likely to relapse than employed veterans. Homeless vs. independently housed veterans had 3.26 (95% CI: 2.55, 4.17) higher odds of relapse after treatment. Veterans with one arrest vs. none were more likely to relapse (OR 1.52, 95% CI: 1.19, 1.95). Treatment completion was critical to maintain sobriety, as every other type of discharge led to more than double the odds of relapse. Veterans who received care at 24-hour detox facilities were 1.49 (95% CI: 1.23, 1.80) times more likely to relapse than those at rehabilitative/residential treatment facilities. Classification tree analysis indicated that homelessness upon discharge was the most important predictor in SUD relapse among veterans. Conclusion Aside from numerous challenges that veterans face after leaving military service, SUD relapse is intensified by risk factors such as homelessness, unemployment, and insufficient SUD treatment. As treatment and preventive care for SUD relapse is an active field of study, further research on SUD relapse among homeless veterans is necessary to better understand the epidemiology of substance addiction among this vulnerable population. The findings of this study can inform healthcare policy and practices targeting veteran-tailored treatment programs to improve SUD treatment completion and lower substance use after treatment.


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