Dataset Information

A Machine-Learning Approach to Predicting Smoking Cessation Treatment Outcomes.

ABSTRACT: AIMS:Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thirds of individuals relapse when provided even the most robust treatments. Identifying for whom treatment is effective will improve the success of our treatments and perhaps identify strategies for improving current approaches. METHODS:Two cohorts (training: N = 90, validation: N = 71) of cigarette smokers enrolled in group cognitive-behavioral therapy (CBT). Generalized estimating equations were used to identify baseline predictors of outcome, as defined by breath carbon monoxide and urine cotinine. Significant measures were entered as candidate variables to predict quit status. The resulting decision trees were used to predict cessation outcomes in a validation cohort. RESULTS:In the training cohort, the decision trees significantly improved on chance classification of smoking status following treatment and at 6-month follow-up. The first split of all decision trees, which was delay discounting, significantly improved on chance classification rates in both the training and validation cohort. Delay discounting emerged as the single best predictor of group CBT treatment response with an average baseline discount rate of ln(k) = -7.1, correctly predicting smoking status of 80% of participants at posttreatment and 81% of participants at follow-up. CONCLUSIONS:This study provides a first step toward personalized care for smoking cessation though future work is needed to identify individuals that are likely to be successful in treatments beyond group CBT. IMPLICATIONS:This study provides a first step toward personalized care for smoking cessation. Using a novel machine-learning approach, baseline measures of clinical and executive functioning are used to predict smoking cessation outcomes following group CBT. A decision point is recommended for the single best predictor of treatment outcomes, delay discounting, to inform future research or clinical practice in an effort to better allocate patients to treatments that are likely to work.

SUBMITTER: Coughlin LN

PROVIDER: S-EPMC7297111 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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A Machine-Learning Approach to Predicting Smoking Cessation Treatment Outcomes.

Coughlin Lara N LN Tegge Allison N AN Sheffer Christine E CE Bickel Warren K WK

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 20200301 3

<h4>Aims</h4>Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thirds of individuals relapse when provided even the most robust treatments. Identifying for whom treatment is effective will improve the success of our treatments and perhaps identify strategies for improving current approaches.<h4>Methods</h4>T ...[more]

PMID: 30508122

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Project description:BackgroundActive smoking increases the risk of tuberculosis (TB) infection 2 to 2.5 times and is significantly associated with recurrent TB and TB mortality. Observational studies have shown associations between smoking and poor TB treatment outcomes such as increased loss to follow-up rate, severity of disease, drug resistance and slow smear conversion. Since most smoking-related immunologic abnormalities are reversible within six weeks of stopping smoking, smoking cessation may have substantial positive effects on TB treatment outcomes, TB relapse and future lung disease.ObjectivesTo analyse the effect of tobacco smoking cessation interventions (SCIs) on the treatment outcomes of people with adult pulmonary TB.Search methodsWe searched the Cochrane Tobacco Addiction Group Specialised Register using free-text and MeSH terms for TB and antitubercular treatment. We also searched MEDLINE and EMBASE using the same topic-related terms, combined with the search terms used to identify trials of tobacco cessation interventions from the Specialised Register. We also searched reference list of articles and reviews, the Conference Paper Index, clinicaltrials.gov and grey literature. The searches are current to 29th July 2015.Selection criteriaIndividual and cluster-randomised controlled trials (RCTs), regardless of date, language and publication status, studies of adults with pulmonary TB on first-line anti-tubercular drugs, with interventions at either an individual or a population level, delivered separately or as part of a larger tobacco control package. This included any type of behavioural or pharmaceutical intervention or both for smoking cessation.Data collection and analysisUsing the eligibility criteria, two authors independently checked the abstracts of retrieved studies for relevance, and acquired full trial reports of candidates for inclusion. The authors resolved any disagreements on eligibility by mutual consent, or by recourse to a third author. Two authors intended to independently extract study data from eligible studies into a data extraction form and compare the findings, synthesise data using risk ratios, and assess risk of bias using standard Cochrane methodologies. However, we found no eligible trials.Main resultsThere were no randomised controlled trials that met the eligibility criteria. A number of potentially eligible studies are underway, and we will assess them for inclusion in the next update of this review.Authors' conclusionsThere is a lack of high-quality evidence, i.e. RCTs, that tests the effectiveness of cessation interventions in improving TB treatment outcomes. There is a need for good-quality randomised controlled trials that assess the effect of SCIs on TB treatment outcomes in both the short and long term. Establishing such an evidence base would be an essential step towards the implementation of SCIs in TB control programmes worldwide.

Project description:BackgroundThe Centers for Medicare and Medicaid Services projects that health care costs will continue to grow over the next few years. Rising readmission costs contribute significantly to increasing health care costs. Multiple areas of health care, including readmissions, have benefited from the application of various machine learning algorithms in several ways.ObjectiveWe aimed to identify suitable models for predicting readmission charges billed by hospitals. Our literature review revealed that this application of machine learning is underexplored. We used various predictive methods, ranging from glass-box models (such as regularization techniques) to black-box models (such as deep learning-based models).MethodsWe defined readmissions as readmission with the same major diagnostic category (RSDC) and all-cause readmission category (RADC). For these readmission categories, 576,701 and 1,091,580 individuals, respectively, were identified from the Nationwide Readmission Database of the Healthcare Cost and Utilization Project by the Agency for Healthcare Research and Quality for 2013. Linear regression, lasso regression, elastic net, ridge regression, eXtreme gradient boosting (XGBoost), and a deep learning model based on multilayer perceptron (MLP) were the 6 machine learning algorithms we tested for RSDC and RADC through 10-fold cross-validation.ResultsOur preliminary analysis using a data-driven approach revealed that within RADC, the subsequent readmission charge billed per patient was higher than the previous charge for 541,090 individuals, and this number was 319,233 for RSDC. The top 3 major diagnostic categories (MDCs) for such instances were the same for RADC and RSDC. The average readmission charge billed was higher than the previous charge for 21 of the MDCs in the case of RSDC, whereas it was only for 13 of the MDCs in RADC. We recommend XGBoost and the deep learning model based on MLP for predicting readmission charges. The following performance metrics were obtained for XGBoost: (1) RADC (mean absolute percentage error [MAPE]=3.121%; root mean squared error [RMSE]=0.414; mean absolute error [MAE]=0.317; root relative squared error [RRSE]=0.410; relative absolute error [RAE]=0.399; normalized RMSE [NRMSE]=0.040; mean absolute deviation [MAD]=0.031) and (2) RSDC (MAPE=3.171%; RMSE=0.421; MAE=0.321; RRSE=0.407; RAE=0.393; NRMSE=0.041; MAD=0.031). The performance obtained for MLP-based deep neural networks are as follows: (1) RADC (MAPE=3.103%; RMSE=0.413; MAE=0.316; RRSE=0.410; RAE=0.397; NRMSE=0.040; MAD=0.031) and (2) RSDC (MAPE=3.202%; RMSE=0.427; MAE=0.326; RRSE=0.413; RAE=0.399; NRMSE=0.041; MAD=0.032). Repeated measures ANOVA revealed that the mean RMSE differed significantly across models with P<.001. Post hoc tests using the Bonferroni correction method indicated that the mean RMSE of the deep learning/XGBoost models was statistically significantly (P<.001) lower than that of all other models, namely linear regression/elastic net/lasso/ridge regression.ConclusionsModels built using XGBoost and MLP are suitable for predicting readmission charges billed by hospitals. The MDCs allow models to accurately predict hospital readmission charges.

Dataset Information

A Machine-Learning Approach to Predicting Smoking Cessation Treatment Outcomes.

Publications

A Machine-Learning Approach to Predicting Smoking Cessation Treatment Outcomes.

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