scholarly journals Machine-Learning Prediction of Comorbid Substance Use Disorders in ADHD Youth Using Swedish Registry Data

2019 ◽  
Author(s):  
Yanli Zhang-James ◽  
Qi Chen ◽  
Ralf Kuja-Halkola ◽  
Paul Lichtenstein ◽  
Henrik Larsson ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Registry Data ◽  
Longitudinal Models ◽  
Cross Sectional ◽  
Using Data

AbstractBackgroundChildren with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.MethodsPsychiatric and somatic diagnoses, family history of these disorders, measures of socioeconomic distress and information about birth complications were obtained from the national registers in Sweden for 19,787 children with ADHD born between 1989-1993. We trained 1) cross-sectional machine learning models using data available by age 17 to predict SUD diagnosis between ages 18-19; and 2) a longitudinal model to predict new diagnoses at each age.ResultsThe area under the receiver operating characteristic curve (AUC) was 0.73 and 0.71 for the random forest and multilayer perceptron cross-sectional models. A prior diagnosis of SUD was the most important predictor, accounting for 25% of correct predictions. However, after excluding this predictor, our model still significantly predicted the first-time diagnosis of SUD during age 18-19 with an AUC of 0.67. The average of the AUCs from longitudinal models predicting new diagnoses one, two, five and ten years in the future was 0.63.ConclusionsSignificant predictions of at-risk co-morbid SUDs in individuals with ADHD can be achieved using population registry data, even many years prior to the first diagnosis. Longitudinal models can potentially monitor their risks over time. More work is needed to create prediction models based on electronic health records or linked population-registers that are sufficiently accurate for use in the clinic.

scholarly journals Can population registry data predict which children with ADHD are at risk of later substance use disorders?

2020 ◽  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Child Psychiatry ◽  
Registry Data ◽  
Early Prediction ◽  
Children With Adhd ◽  
Youth With Adhd

The first study to examine the potential of machine learning in early prediction of later substance use disorders (SUDs) in youth with ADHD has been published in the Journal of Child Psychiatry and Psychology.

scholarly journals Machine‐Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data

2020 ◽  
Vol 61 (12) ◽  
pp. 1370-1379
Author(s):  
Yanli Zhang‐James ◽  
Qi Chen ◽  
Ralf Kuja‐Halkola ◽  
Paul Lichtenstein ◽  
Henrik Larsson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Registry Data

scholarly journals The impact of poly-traumatization on treatment outcomes in young people with substance use disorders

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sidsel Karsberg ◽  
Morten Hesse ◽  
Michael Mulbjerg Pedersen ◽  
Ruby Charak ◽  
Mads Uffe Pedersen
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Psychological Trauma ◽  
Well Being ◽  
Traumatic Experiences ◽  
Treatment Entry ◽  
Trauma Types ◽  
Using Data ◽  
The Impact ◽  
Drug Use Disorder

Abstract Background It is believed that clients with psychological trauma experiences have a poor prognosis with regard to treatment participation and outcomes for substance use disorders. However, knowledge on the effect of the number of trauma experiences is scarce. Methods Using data from drug use disorder (DUD) treatment in Denmark, we assessed the impact of having experienced multiple potentially traumatic experiences on DUD treatment efficacy. Baseline and follow-up data from 775 young participants (mean age = 20.2 years, standard deviation = 2.6) recruited at nine treatment centers were included in analyses. Results Analyses showed that participants who were exposed multiple trauma experiences also reported a significantly higher intake of cannabis at treatment entry, and a lower well-being score than participants who reported less types or no types of victimization experiences. During treatment, patients with multiple types of trauma experiences showed a slower rate of reduction of cannabis than patients with few or no trauma experiences. The number of trauma types was not associated with number of sessions attended or the development of well-being in treatment. Conclusion Overall, the results show that although traumatized youth in DUD treatment show up for treatment, helping them to reduce substance use during treatment is uniquely challenging. Trial registration ISRCTN88025085, date of registration: 29.08.2016, retrospectively registered.

scholarly journals Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

2021 ◽  
Author(s):  
Kazutaka Uchida ◽  
Junichi Kouno ◽  
Shinichi Yoshimura ◽  
Norito Kinjo ◽  
Fumihiro Sakakibara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Training Cohort ◽  
Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

scholarly journals Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryan Smith ◽  
◽  
Justin S. Feinstein ◽  
Rayus Kuplicki ◽  
Katherine L. Forthman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Individual Differences ◽  
Substance Use Disorders ◽  
Breath Hold ◽  
Healthy Individuals ◽  
Performance Improvements ◽  
Heartbeat Perception ◽  
Minimal Improvement ◽  
Healthy Participants

AbstractThis study employed a series of heartbeat perception tasks to assess the hypothesis that cardiac interoceptive processing in individuals with depression/anxiety (N = 221), and substance use disorders (N = 136) is less flexible than that of healthy individuals (N = 53) in the context of physiological perturbation. Cardiac interoception was assessed via heartbeat tapping when: (1) guessing was allowed; (2) guessing was not allowed; and (3) experiencing an interoceptive perturbation (inspiratory breath hold) expected to amplify cardiac sensation. Healthy participants showed performance improvements across the three conditions, whereas those with depression/anxiety and/or substance use disorder showed minimal improvement. Machine learning analyses suggested that individual differences in these improvements were negatively related to anxiety sensitivity, but explained relatively little variance in performance. These results reveal a perceptual insensitivity to the modulation of interoceptive signals that was evident across several common psychiatric disorders, suggesting that interoceptive deficits in the realm of psychopathology manifest most prominently during states of homeostatic perturbation.

scholarly journals Machine Learning-based in-hospital Mortality Prediction Models for Patients With Acute Coronary Syndrome

2020 ◽  
Author(s):  
Jun Ke ◽  
Yiwei Chen ◽  
Xiaoping Wang ◽  
Zhiyong Wu ◽  
qiongyao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Hospital Mortality ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Hdl Cholesterol ◽  
Coronary Syndrome

Abstract BackgroundThe purpose of this study is to identify the risk factors of in-hospital mortality in patients with acute coronary syndrome (ACS) and to evaluate the performance of traditional regression and machine learning prediction models.MethodsThe data of ACS patients who entered the emergency department of Fujian Provincial Hospital from January 1, 2017 to March 31, 2020 for chest pain were retrospectively collected. The study used univariate and multivariate logistic regression analysis to identify risk factors for in-hospital mortality of ACS patients. The traditional regression and machine learning algorithms were used to develop predictive models, and the sensitivity, specificity, and receiver operating characteristic curve were used to evaluate the performance of each model.ResultsA total of 7810 ACS patients were included in the study, and the in-hospital mortality rate was 1.75%. Multivariate logistic regression analysis found that age and levels of D-dimer, cardiac troponin I, N-terminal pro-B-type natriuretic peptide (NT-proBNP), lactate dehydrogenase (LDH), high-density lipoprotein (HDL) cholesterol, and calcium channel blockers were independent predictors of in-hospital mortality. The study found that the area under the receiver operating characteristic curve of the models developed by logistic regression, gradient boosting decision tree (GBDT), random forest, and support vector machine (SVM) for predicting the risk of in-hospital mortality were 0.963, 0.960, 0.963, and 0.959, respectively. Feature importance evaluation found that NT-proBNP, LDH, and HDL cholesterol were top three variables that contribute the most to the prediction performance of the GBDT model and random forest model.ConclusionsThe predictive model developed using logistic regression, GBDT, random forest, and SVM algorithms can be used to predict the risk of in-hospital death of ACS patients. Based on our findings, we recommend that clinicians focus on monitoring the changes of NT-proBNP, LDH, and HDL cholesterol, as this may improve the clinical outcomes of ACS patients.

scholarly journals Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning (Preprint)

2019 ◽  
Author(s):  
Karen-Inge Karstoft ◽  
Ioannis Tsamardinos ◽  
Kasper Eskelund ◽  
Søren Bo Andersen ◽  
Lars Ravnborg Nissen
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Linear Models ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Ptsd Symptoms ◽  
Forest Models ◽  
Random Forest Models ◽  
Automated Machine Learning ◽  
Military Rank

BACKGROUND Posttraumatic stress disorder (PTSD) is a relatively common consequence of deployment to war zones. Early postdeployment screening with the aim of identifying those at risk for PTSD in the years following deployment will help deliver interventions to those in need but have so far proved unsuccessful. OBJECTIVE This study aimed to test the applicability of automated model selection and the ability of automated machine learning prediction models to transfer across cohorts and predict screening-level PTSD 2.5 years and 6.5 years after deployment. METHODS Automated machine learning was applied to data routinely collected 6-8 months after return from deployment from 3 different cohorts of Danish soldiers deployed to Afghanistan in 2009 (cohort 1, N=287 or N=261 depending on the timing of the outcome assessment), 2010 (cohort 2, N=352), and 2013 (cohort 3, N=232). RESULTS Models transferred well between cohorts. For screening-level PTSD 2.5 and 6.5 years after deployment, random forest models provided the highest accuracy as measured by area under the receiver operating characteristic curve (AUC): 2.5 years, AUC=0.77, 95% CI 0.71-0.83; 6.5 years, AUC=0.78, 95% CI 0.73-0.83. Linear models performed equally well. Military rank, hyperarousal symptoms, and total level of PTSD symptoms were highly predictive. CONCLUSIONS Automated machine learning provided validated models that can be readily implemented in future deployment cohorts in the Danish Defense with the aim of targeting postdeployment support interventions to those at highest risk for developing PTSD, provided the cohorts are deployed on similar missions.

An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Fathima Aliyar Vellameeran ◽  
Thomas Brindha
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Research Papers ◽  
Using Data ◽  
Intelligent Methods

Abstract Objectives To make a clear literature review on state-of-the-art heart disease prediction models. Methods It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

Screening, Assessment, and Diagnosis of Substance Use Disorders among Emerging Adults

2017 ◽  
pp. 38-70 ◽  
Author(s):  
Douglas C. Smith ◽  
Kyle M. Bennett ◽  
Michael L. Dennis ◽  
Rodney Funk
Keyword(s):  
Substance Use ◽  
Emerging Adults ◽  
Substance Use Disorders ◽  
Emerging Adult ◽  
Assessment Instruments ◽  
Specific Research ◽  
Dsm 5 ◽  
Administration Time ◽  
Symptom Patterns ◽  
Using Data

Several challenges may hinder accurate screening for and assessment of substance use disorders among emerging adults ages 18–29. This chapter discusses emerging adult–specific research on diagnosing substance use disorders and several empirically supported screeners and assessments that may be useful to those working with emerging adults. First, emerging adult–specific research supporting changes to the most recent version of the Diagnostic and Statistical Manual for Mental Disorders, the DSM-5, is reviewed, and nuances in using the DSM-5 with emerging adults are discussed. The chapter highlights idiosyncrasies in emerging adult symptom patterns using data from large national surveys. Finally, a practice-friendly review of screening and assessment instruments commonly used with emerging adults is provided. For screening instruments, administration time, the instrument’s ability to discern which emerging adults exhibit substance use problems, and emerging adult–specific cutoff points in the literature are addressed. For assessment tools, comprehensiveness of the instrument, administration time, and contexts in which the instrument has been used with emerging adults are discussed.

scholarly journals Patients with stroke and psychiatric comorbidities have lower carotid revascularization rates

Neurology ◽  
2019 ◽  
Vol 92 (22) ◽  
pp. e2514-e2521 ◽  
Author(s):  
Diana M. Bongiorno ◽  
Gail L. Daumit ◽  
Rebecca F. Gottesman ◽  
Roland Faigle
Keyword(s):  
Mental Illness ◽  
Substance Use ◽  
Carotid Stenosis ◽  
Substance Use Disorders ◽  
Psychiatric Comorbidity ◽  
Cross Sectional ◽  
Psychiatric Comorbidities ◽  
Lower Odds ◽  
Carotid Revascularization ◽  
Psychiatric Conditions

ObjectiveWe investigated whether mental illness is associated with lower rates of carotid endarterectomy (CEA)/carotid artery stenting (CAS) after stroke due to carotid stenosis.MethodsIn this retrospective cross-sectional study, ischemic stroke cases due to carotid stenosis were identified in the 2007–2014 Nationwide (National) Inpatient Sample. Psychiatric conditions were identified by secondary ICD-9-CM diagnosis codes for schizophrenia/psychoses, bipolar disorder, depression, anxiety, or substance use disorders. Using logistic regression, we tested the association between psychiatric conditions and CEA/CAS, controlling for demographic, clinical, and hospital factors.ResultsAmong 37,474 included stroke cases, 6,922 (18.5%) had a psychiatric comorbidity. The presence of any psychiatric condition was associated with lower odds of CEA/CAS (adjusted odds ratio [OR] 0.84, 95% confidence interval [CI] 0.78–0.90). Schizophrenia/psychoses (OR 0.72, 95% CI 0.55–0.93), depression (OR 0.83, 95% CI 0.75–0.91), and substance use disorders (OR 0.73, 95% CI 0.65–0.83) were each associated with lower odds of CEA/CAS. The association of mental illness and CEA/CAS was dose-dependent: compared to patients without mental illness, patients with multiple psychiatric comorbidities (OR 0.74, 95% CI 0.62–0.87) had lower odds of CEA/CAS than those with only one psychiatric comorbidity (OR 0.86, 95% CI 0.79–0.92; p value for trend <0.001).ConclusionThe odds of carotid revascularization after stroke is lower in patients with mental illness, particularly those with schizophrenia/psychoses, depression, substance use disorders, and multiple psychiatric diagnoses.

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