1
|
Ebrahimi A, Wiil UK, Baskaran R, Peimankar A, Andersen K, Nielsen AS. AUD-DSS: a decision support system for early detection of patients with alcohol use disorder. BMC Bioinformatics 2023; 24:329. [PMID: 37658294 PMCID: PMC10474761 DOI: 10.1186/s12859-023-05450-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 08/21/2023] [Indexed: 09/03/2023] Open
Abstract
BACKGROUND Alcohol use disorder (AUD) causes significant morbidity, mortality, and injuries. According to reports, approximately 5% of all registered deaths in Denmark could be due to AUD. The problem is compounded by the late identification of patients with AUD, a situation that can cause enormous problems, from psychological to physical to economic problems. Many individuals suffering from AUD never undergo specialist treatment during their addiction due to obstacles such as taboo and the poor performance of current screening tools. Therefore, there is a lack of rapid intervention. This can be mitigated by the early detection of patients with AUD. A clinical decision support system (DSS) powered by machine learning (ML) methods can be used to diagnose patients' AUD status earlier. METHODS This study proposes an effective AUD prediction model (AUDPM), which can be used in a DSS. The proposed model consists of four distinct components: (1) imputation to address missing values using the k-nearest neighbours approach, (2) recursive feature elimination with cross validation to select the most relevant subset of features, (3) a hybrid synthetic minority oversampling technique-edited nearest neighbour approach to remove noise and balance the distribution of the training data, and (4) an ML model for the early detection of patients with AUD. Two data sources, including a questionnaire and electronic health records of 2571 patients, were collected from Odense University Hospital in the Region of Southern Denmark for the AUD-Dataset. Then, the AUD-Dataset was used to build ML models. The results of different ML models, such as support vector machine, K-nearest neighbour, decision tree, random forest, and extreme gradient boosting, were compared. Finally, a combination of all these models in an ensemble learning approach was selected for the AUDPM. RESULTS The results revealed that the proposed ensemble AUDPM outperformed other single models and our previous study results, achieving 0.96, 0.94, 0.95, and 0.97 precision, recall, F1-score, and accuracy, respectively. In addition, we designed and developed an AUD-DSS prototype. CONCLUSION It was shown that our proposed AUDPM achieved high classification performance. In addition, we identified clinical factors related to the early detection of patients with AUD. The designed AUD-DSS is intended to be integrated into the existing Danish health care system to provide novel information to clinical staff if a patient shows signs of harmful alcohol use; in other words, it gives staff a good reason for having a conversation with patients for whom a conversation is relevant.
Collapse
Affiliation(s)
- Ali Ebrahimi
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.
| | - Uffe Kock Wiil
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Ruben Baskaran
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Abdolrahman Peimankar
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Kjeld Andersen
- Unit for Clinical Alcohol Research, Clinical Institute, University of Southern Denmark, Odense, Denmark
| | - Anette Søgaard Nielsen
- Unit for Clinical Alcohol Research, Clinical Institute, University of Southern Denmark, Odense, Denmark
| |
Collapse
|
2
|
Laroque FM, Boers E, Afzali MH, Conrod PJ. Personality-specific pathways from bullying victimization to adolescent alcohol use: a multilevel longitudinal moderated mediation analysis. Dev Psychopathol 2023; 35:1454-1467. [PMID: 35129105 DOI: 10.1017/s0954579421001358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Bullying victimization is common in adolescence and has been associated with a broad variety of psychopathology and alcohol use. The present study assessed time-varying associations between bullying victimization and alcohol use through internalizing and externalizing symptoms and whether this indirect association throughout time is moderated by personality. This 5-year longitudinal study (3,800 grade 7 adolescents) used Bayesian multilevel moderated mediation models: independent variable was bullying victimization; moderators were four personality dimensions (anxiety sensitivity, hopelessness, impulsivity, and sensation seeking); internalizing symptoms (anxiety, depressive symptoms) and externalizing symptoms (conduct, hyperactivity problems) were the mediators; and alcohol use, the outcome. Results indicated significant between, within, and lagged effects on alcohol use through internalizing and externalizing symptoms. There were significant between and within effects on alcohol use through internalizing symptoms for adolescents with high anxiety sensitivity and hopelessness, and significant between, within, and lagged effects on alcohol use through externalizing symptoms for adolescents with high impulsivity and sensation seeking. These findings implicate two risk pathways that account for how bullying victimization enhances alcohol use risk and emphasize the importance of personality profiles that can shape the immediate and long-term consequences of victimization.
Collapse
Affiliation(s)
- Flavie M Laroque
- Department of Psychiatry and Addiction, University of Montreal, and CHU Ste Justine Research Center, Montreal, QC, Canada
| | - Elroy Boers
- Department of Psychiatry and Addiction, University of Montreal, and CHU Ste Justine Research Center, Montreal, QC, Canada
| | - Mohammad H Afzali
- Department of Psychiatry and Addiction, University of Montreal, and CHU Ste Justine Research Center, Montreal, QC, Canada
| | - Patricia J Conrod
- Department of Psychiatry and Addiction, University of Montreal, and CHU Ste Justine Research Center, Montreal, QC, Canada
| |
Collapse
|
3
|
Lu CH, Jette G, Falls Z, Jacobs DM, Gibson W, Bednarczyk EM, Kuo TY, Lape-Newman B, Leonard KE, Elkin PL. A cohort of patients in New York State with an alcohol use disorder and subsequent treatment information - A merging of two administrative data sources. J Biomed Inform 2023; 144:104443. [PMID: 37455008 PMCID: PMC11178131 DOI: 10.1016/j.jbi.2023.104443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/05/2023] [Accepted: 07/08/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVE Despite the high prevalence of alcohol use disorder (AUD) in the United States, limited research is focused on the associations among AUD, pain, and opioids/benzodiazepine use. In addition, little is known regarding individuals with a history of AUD and their potential risk for pain diagnoses, pain prescriptions, and subsequent misuse. Moreover, the potential risk of pain diagnoses, prescriptions, and subsequent misuse among individuals with a history of AUD is not well known. The objective was to develop a tailored dataset by linking data from 2 New York State (NYS) administrative databases to investigate a series of hypotheses related to AUD and painful medical disorders. METHODS Data from the NYS Office of Addiction Services and Supports (OASAS) Client Data System (CDS) and Medicaid claims data from the NYS Department of Health Medicaid Data Warehouse (MDW) were merged using a stepwise deterministic method. Multiple patient-level identifier combinations were applied to create linkage rules. We included patients aged 18 and older from the OASAS CDS who initially entered treatment with a primary substance use of alcohol and no use of opioids between January 1, 2003, and September 23, 2019. This cohort was then linked to corresponding Medicaid claims. RESULTS A total of 177,685 individuals with a primary AUD problem and no opioid use history were included in the dataset. Of these, 37,346 (21.0%) patients had an OUD diagnosis, and 3,365 (1.9%) patients experienced an opioid overdose. There were 121,865 (68.6%) patients found to have a pain condition. CONCLUSION The integrated database allows researchers to examine the associations among AUD, pain, and opioids/benzodiazepine use, and propose hypotheses to improve outcomes for at-risk patients. The findings of this study can contribute to the development of a prognostic prediction model and the analysis of longitudinal outcomes to improve the care of patients with AUD.
Collapse
Affiliation(s)
- Chi-Hua Lu
- Department of Pharmacy Practice, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, NY, USA.
| | - Gail Jette
- Division of Outcomes, Management, and Systems Information, Office of Addiction Services and Supports, Albany, NY, USA
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - David M Jacobs
- Department of Pharmacy Practice, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Walter Gibson
- Department of Pharmacy Practice, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Edward M Bednarczyk
- Department of Pharmacy Practice, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Tzu-Yin Kuo
- Department of Pharmacy Practice, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, NY, USA
| | | | - Kenneth E Leonard
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY, USA
| | - Peter L Elkin
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA; Faculty of Engineering, University of Southern Denmark, Denmark; U.S. Department of Veterans Affairs, WNY VA, Buffalo, NY, USA
| |
Collapse
|
4
|
Dharma C, Fu R, Chaiton M. Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6194. [PMID: 37444042 PMCID: PMC10340623 DOI: 10.3390/ijerph20136194] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
Abstract
There is a lack of rigorous methodological development for descriptive epidemiology, where the goal is to describe and identify the most important associations with an outcome given a large set of potential predictors. This has often led to the Table 2 fallacy, where one presents the coefficient estimates for all covariates from a single multivariable regression model, which are often uninterpretable in a descriptive analysis. We argue that machine learning (ML) is a potential solution to this problem. We illustrate the power of ML with an example analysis identifying the most important predictors of alcohol abuse among sexual minority youth. The framework we propose for this analysis is as follows: (1) Identify a few ML methods for the analysis, (2) optimize the parameters using the whole data with a nested cross-validation approach, (3) rank the variables using variable importance scores, (4) present partial dependence plots (PDP) to illustrate the association between the important variables and the outcome, (5) and identify the strength of the interaction terms using the PDPs. We discuss the potential strengths and weaknesses of using ML methods for descriptive analysis and future directions for research. R codes to reproduce these analyses are provided, which we invite other researchers to use.
Collapse
Affiliation(s)
- Christoffer Dharma
- Dalla Lana School of Public Health, University of Toronto, Toronton, ON M5T 3M7, Canada; (C.D.); (R.F.)
- Center for Addictions and Mental Health, Toronto, ON M6J 1H4, Canada
| | - Rui Fu
- Dalla Lana School of Public Health, University of Toronto, Toronton, ON M5T 3M7, Canada; (C.D.); (R.F.)
- Department of Otolaryngology—Head and Neck Surgery, Temerty Faculty of Medicine, Sunnybrook Hospital, Toronto, ON M4N 3M5, Canada
| | - Michael Chaiton
- Dalla Lana School of Public Health, University of Toronto, Toronton, ON M5T 3M7, Canada; (C.D.); (R.F.)
- Center for Addictions and Mental Health, Toronto, ON M6J 1H4, Canada
- Ontario Tobacco Research Unit, Toronto, ON M5S 2S1, Canada
| |
Collapse
|
5
|
m6A-Related Genes Contribute to Poor Prognosis of Hepatocellular Carcinoma. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2427987. [PMID: 36339682 PMCID: PMC9629938 DOI: 10.1155/2022/2427987] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 09/08/2022] [Accepted: 09/09/2022] [Indexed: 12/03/2022]
Abstract
Background Hepatocellular carcinoma (HCC) is one of the most common and lethal digestive system cancers worldwide. N6-methyladenosine (m6A) modification plays an essential role in diverse critical biological processes and may participate in the development and progression of HCC. Methods We downloaded transcriptome data and clinical data from TCGA as the training set. COX and LASSO screened prognostic m6A genes. ROC and Kaplan-Meier curve analysis evaluated the effectiveness of the model. ICGC and our center data were used as verification sets. Results We include the “writer (METTL3, METTL14, WTAP, KIAA1429, RBM15, ZC3H13),” the “reader (YTHDC1, YTHDC2, YTHDF1, YTHDF2, HNRNPC),” and the “eraser (FTO, ALKBH5)” in the study. We obtained YTHDF2, YTHDF1, METTL3, and KIAA1429 through differential analysis, survival analysis, and LASSO regression analysis. The prediction model was established based on the expression of these 4 molecules. HCC patients were divided into “high-risk” and “low-risk” groups to compare survival differences. The model suggested a poor prognosis in the validation sets. Conclusion The four-m6A-related-gene combination model was an independent prognostic factor of HCC and could improve the prediction of the prognosis of HCC.
Collapse
|
6
|
Negriff S, Dilkina B, Matai L, Rice E. Using machine learning to determine the shared and unique risk factors for marijuana use among child-welfare versus community adolescents. PLoS One 2022; 17:e0274998. [PMID: 36129944 PMCID: PMC9491564 DOI: 10.1371/journal.pone.0274998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 09/08/2022] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVE This study used machine learning (ML) to test an empirically derived set of risk factors for marijuana use. Models were built separately for child welfare (CW) and non-CW adolescents in order to compare the variables selected as important features/risk factors. METHOD Data were from a Time 4 (Mage = 18.22) of longitudinal study of the effects of maltreatment on adolescent development (n = 350; CW = 222; non-CW = 128; 56%male). Marijuana use in the past 12 months (none versus any) was obtained from a single item self-report. Risk factors entered into the model included mental health, parent/family social support, peer risk behavior, self-reported risk behavior, self-esteem, and self-reported adversities (e.g., abuse, neglect, witnessing family violence or community violence). RESULTS The ML approaches indicated 80% accuracy in predicting marijuana use in the CW group and 85% accuracy in the non-CW group. In addition, the top features differed for the CW and non-CW groups with peer marijuana use emerging as the most important risk factor for CW youth, whereas externalizing behavior was the most important for the non-CW group. The most important common risk factor between group was gender, with males having higher risk. CONCLUSIONS This is the first study to examine the shared and unique risk factors for marijuana use for CW and non-CW youth using a machine learning approach. The results support our assertion that there may be similar risk factors for both groups, but there are also risks unique to each population. Therefore, risk factors derived from normative populations may not have the same importance when used for CW youth. These differences should be considered in clinical practice when assessing risk for substance use among adolescents.
Collapse
Affiliation(s)
- Sonya Negriff
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America
| | - Bistra Dilkina
- Department of Computer Science, University of Southern California, Los Angeles, California, United States of America
| | - Laksh Matai
- Department of Computer Science, University of Southern California, Los Angeles, California, United States of America
| | - Eric Rice
- Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, California, United States of America
| |
Collapse
|
7
|
Obesity-Associated Differentially Methylated Regions in Colon Cancer. J Pers Med 2022; 12:jpm12050660. [PMID: 35629083 PMCID: PMC9142939 DOI: 10.3390/jpm12050660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/11/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Obesity with adiposity is a common disorder in modern days, influenced by environmental factors such as eating and lifestyle habits and affecting the epigenetics of adipose-based gene regulations and metabolic pathways in colorectal cancer (CRC). We compared epigenetic changes of differentially methylated regions (DMR) of genes in colon tissues of 225 colon cancer cases (154 non-obese and 71 obese) and 15 healthy non-obese controls by accessing The Cancer Genome Atlas (TCGA) data. We applied machine-learning-based analytics including generalized regression (GR) as a confirmatory validation model to identify the factors that could contribute to DMRs impacting colon cancer to enhance prediction accuracy. We found that age was a significant predictor in obese cancer patients, both alone (p = 0.003) and interacting with hypomethylated DMRs of ZBTB46, a tumor suppressor gene (p = 0.008). DMRs of three additional genes: HIST1H3I (p = 0.001), an oncogene with a hypomethylated DMR in the promoter region; SRGAP2C (p = 0.006), a tumor suppressor gene with a hypermethylated DMR in the promoter region; and NFATC4 (p = 0.006), an adipocyte differentiating oncogene with a hypermethylated DMR in an intron region, are also significant predictors of cancer in obese patients, independent of age. The genes affected by these DMR could be potential novel biomarkers of colon cancer in obese patients for cancer prevention and progression.
Collapse
|
8
|
Senior M, Fanshawe T, Fazel M, Fazel S. Prediction models for child and adolescent mental health: A systematic review of methodology and reporting in recent research. JCPP ADVANCES 2021; 1:e12034. [PMID: 37431439 PMCID: PMC10242964 DOI: 10.1002/jcv2.12034] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 09/08/2021] [Indexed: 08/25/2023] Open
Abstract
Background There has been a rapid growth in the publication of new prediction models relevant to child and adolescent mental health. However, before their implementation into clinical services, it is necessary to appraise the quality of their methods and reporting. We conducted a systematic review of new prediction models in child and adolescent mental health, and examined their development and validation. Method We searched five databases for studies developing or validating multivariable prediction models for individuals aged 18 years old or younger from 1 January 2018 to 18 February 2021. Quality of reporting was assessed using the Transparent Reporting of a multivariable prediction models for Individual Prognosis Or Diagnosis checklist, and quality of methodology using items based on expert guidance and the PROBAST tool. Results We identified 100 eligible studies: 41 developing a new prediction model, 48 validating an existing model and 11 that included both development and validation. Most publications (k = 75) reported a model discrimination measure, while 26 investigations reported calibration. Of 52 new prediction models, six (12%) were for suicidal outcomes, 18 (35%) for future diagnosis, five (10%) for child maltreatment. Other outcomes included violence, crime, and functional outcomes. Eleven new models (21%) were developed for use in high-risk populations. Of development studies, around a third were sufficiently statistically powered (k = 16%, 31%), while this was lower for validation investigations (k = 12, 25%). In terms of performance, the discrimination (as measured by the C-statistic) for new models ranged from 0.57 for a tool predicting ADHD diagnosis in an external validation sample to 0.99 for a machine learning model predicting foster care permanency. Conclusions Although some tools have recently been developed for child and adolescent mental health for prognosis and child maltreatment, none can be currently recommended for clinical practice due to a combination of methodological limitations and poor model performance. New work needs to use ensure sufficient sample sizes, representative samples, and testing of model calibration.
Collapse
Affiliation(s)
- Morwenna Senior
- Department of PsychiatryOxford Health NHS Foundation Trust, University of OxfordOxfordUK
| | - Thomas Fanshawe
- Nuffield Department of Primary Care Health SciencesUniversity of OxfordOxfordUK
| | - Mina Fazel
- Department of PsychiatryOxford Health NHS Foundation Trust, University of OxfordOxfordUK
| | - Seena Fazel
- Department of PsychiatryOxford Health NHS Foundation Trust, University of OxfordOxfordUK
| |
Collapse
|
9
|
Neuropsychosocial markers of binge drinking in young adults. Mol Psychiatry 2021; 26:4931-4943. [PMID: 32398720 PMCID: PMC7658012 DOI: 10.1038/s41380-020-0771-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 04/22/2020] [Accepted: 04/29/2020] [Indexed: 01/26/2023]
Abstract
Binge drinking is associated with disease and death, and developing tools to identify risky drinkers could mitigate its damage. Brain processes underlie risky drinking, so we examined whether neural and psychosocial markers could identify binge drinkers. Reward is the most widely studied neural process in addiction, but processes such as emotion, social cognition, and self-regulation are also involved. Here we examined whether neural processes apart from reward contribute to predicting risky drinking behaviors. From the Human Connectome Project, we identified 177 young adults who binged weekly and 309 nonbingers. We divided the sample into a training and a testing set and used machine-learning algorithms to classify participants based on psychosocial, neural, or both (neuropsychosocial) data. We also developed separate models for each of the seven fMRI tasks used in the study. An ensemble model developed in the training dataset was then applied to the testing dataset. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) and differences between models were assessed using DeLong's test. The three models performed better than chance in the test sample with the neuropsychosocial (AUC = 0.86) and psychosocial (AUC = 0.84) performing better than the neural model (AUC = 0.64). Two fMRI-based models predicted binge drinking status better than chance, corresponding to the social and language tasks. Models developed with psychosocial and neural variables could contribute as diagnostic tools to help classify risky drinkers. Since social and language fMRI tasks performed best among the neural discriminators (including those from gambling and emotion tasks), it suggests the involvement of a broader range of brain processes than those traditionally associated with binge drinking in young adults.
Collapse
|
10
|
Marcon G, de Ávila Pereira F, Zimerman A, da Silva BC, von Diemen L, Passos IC, Recamonde-Mendoza M. Patterns of high-risk drinking among medical students: A web-based survey with machine learning. Comput Biol Med 2021; 136:104747. [PMID: 34449306 DOI: 10.1016/j.compbiomed.2021.104747] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/20/2021] [Accepted: 08/04/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Prior studies have found increased rates of alcohol consumption among physicians and medical students. The present study aims to build machine learning (ML) models to identify patterns of high-risk drinking (HRD), including alcohol use disorder, within this population. METHODS We analyzed data collected through a web-based survey among Brazilian medical students. Variables included sociodemographic data, personal information, university status, and mental health. Stratification for HRD was carried out based on the AUDIT-C scores. Three ML algorithms were used to build classifiers to predict HRD among medical students: elastic net regularization, random forest, and artificial neural networks. Model interpretation techniques were adopted to assess the most influential predictors for models' decisions, which represent potential factors associated with HRD. RESULTS A total of 4840 medical students were included in the study. The prevalence of HRD was 53.03%. The three ML models built were able to distinguish individuals with HRD from low-risk drinking (LRD) with very similar performance. The average AUC scores in the cross-validation procedure were around 0.72, and this performance was replicated in the test set. The most important features for the ML models were the use of tobacco and cannabis, monthly family income, marital status, sexual orientation, and physical activities. CONCLUSIONS This study proposes that ML models may serve as tools for initial screening of students regarding their susceptibility for at-risk drinking or alcohol use disorder. In addition, we identified several key factors associated with HRD that could be further investigated and explored for preventive and assistance measures.
Collapse
Affiliation(s)
- Grasiela Marcon
- Department of Psychiatry, Faculty of Medicine, Universidade Federal da Fronteira Sul, Brazil; Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) e Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil; Graduate Program in Psychiatry and Behavioral Sciences, Department of Psychiatry, Faculty of Medicine, Universidade Federal Do Rio Grande Do Sul (UFRGS), Porto Alegre, RS, Brazil.
| | - Flávia de Ávila Pereira
- Institute of Informatics, Universidade Federal Do Rio Grande Do Sul (UFRGS), Porto Alegre, RS, Brazil.
| | - Aline Zimerman
- Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) e Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil; Graduate Program in Psychiatry and Behavioral Sciences, Department of Psychiatry, Faculty of Medicine, Universidade Federal Do Rio Grande Do Sul (UFRGS), Porto Alegre, RS, Brazil.
| | - Bruno Castro da Silva
- College of Information and Computer Sciences, University of Massachusetts (UMass), Amherst, MA, United States.
| | - Lisia von Diemen
- Graduate Program in Psychiatry and Behavioral Sciences, Department of Psychiatry, Faculty of Medicine, Universidade Federal Do Rio Grande Do Sul (UFRGS), Porto Alegre, RS, Brazil; Center for Drug and Alcohol Research, Hospital de Clínicas de Porto Alegre (HCPA), Universidade Federal Do Rio Grande Do Sul (UFRGS), Porto Alegre, RS, Brazil.
| | - Ives Cavalcante Passos
- Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) e Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil; Graduate Program in Psychiatry and Behavioral Sciences, Department of Psychiatry, Faculty of Medicine, Universidade Federal Do Rio Grande Do Sul (UFRGS), Porto Alegre, RS, Brazil.
| | - Mariana Recamonde-Mendoza
- Institute of Informatics, Universidade Federal Do Rio Grande Do Sul (UFRGS), Porto Alegre, RS, Brazil; Bioinformatics Core, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil.
| |
Collapse
|
11
|
Han DH, Lee SH, Lee S, Seo DC. Identifying emerging predictors for adolescent electronic nicotine delivery systems use: A machine learning analysis of the Population Assessment of Tobacco and Health Study. Prev Med 2021; 145:106418. [PMID: 33422574 DOI: 10.1016/j.ypmed.2021.106418] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 12/30/2020] [Accepted: 01/05/2021] [Indexed: 11/28/2022]
Abstract
Intervention strategies to prevent adolescents from using electronic nicotine delivery systems (ENDS) should be based on robust predictors of ENDS use that may differ from predictors of conventional cigarette use. Literature points to the need for uncovering emerging predictors of ENDS use. This study identified emerging predictors of adolescent ENDS use using machine learning (ML) techniques. We analyzed nationally representative multi-wave longitudinal survey data (2013-2018) drawn from the Population Assessment of Tobacco and Health Study. A sample of adolescents (12-17 years) who never used any tobacco products at baseline and completed Wave 2 (n = 7958), Wave 3 (n = 6260) and Wave 4 (n = 4544) were analyzed. We developed a supervised ML prediction model using the penalized logistic regression to assess self-reported past-month ENDS use (i.e., current use) at Waves 2-4 based on the variables measured at the previous wave. We then extracted important predictors from each model. The penalized logistic regression models showed suitable capability to discriminate between ENDS uses and non-uses at each wave based on the area under the receiver operating characteristic curve and the area under the precision-recall curve. Interestingly, social media use emerged as an important variable in predicting adolescent ENDS use. ML models appear to be a promising method to identify unique population-level predictors for U.S. adolescent ENDS use behaviors. More research is warranted to investigate emerging predictors of ENDS use and experimentally examine the mechanism by which these emerging predictors affect ENDS use behavior across different spectrum of populations.
Collapse
Affiliation(s)
- Dae-Hee Han
- Indiana University School of Public Health, Bloomington, IN, USA
| | - Shin Hyung Lee
- Indiana University School of Public Health, Bloomington, IN, USA
| | - Shieun Lee
- Indiana University School of Public Health, Bloomington, IN, USA
| | - Dong-Chul Seo
- Indiana University School of Public Health, Bloomington, IN, USA.
| |
Collapse
|
12
|
Greenwood CJ, Youssef GJ, Letcher P, Macdonald JA, Hagg LJ, Sanson A, Mcintosh J, Hutchinson DM, Toumbourou JW, Fuller-Tyszkiewicz M, Olsson CA. A comparison of penalised regression methods for informing the selection of predictive markers. PLoS One 2020; 15:e0242730. [PMID: 33216811 PMCID: PMC7678959 DOI: 10.1371/journal.pone.0242730] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 11/06/2020] [Indexed: 12/31/2022] Open
Abstract
Background Penalised regression methods are a useful atheoretical approach for both developing predictive models and selecting key indicators within an often substantially larger pool of available indicators. In comparison to traditional methods, penalised regression models improve prediction in new data by shrinking the size of coefficients and retaining those with coefficients greater than zero. However, the performance and selection of indicators depends on the specific algorithm implemented. The purpose of this study was to examine the predictive performance and feature (i.e., indicator) selection capability of common penalised logistic regression methods (LASSO, adaptive LASSO, and elastic-net), compared with traditional logistic regression and forward selection methods. Design Data were drawn from the Australian Temperament Project, a multigenerational longitudinal study established in 1983. The analytic sample consisted of 1,292 (707 women) participants. A total of 102 adolescent psychosocial and contextual indicators were available to predict young adult daily smoking. Findings Penalised logistic regression methods showed small improvements in predictive performance over logistic regression and forward selection. However, no single penalised logistic regression model outperformed the others. Elastic-net models selected more indicators than either LASSO or adaptive LASSO. Additionally, more regularised models included fewer indicators, yet had comparable predictive performance. Forward selection methods dismissed many indicators identified as important in the penalised logistic regression models. Conclusions Although overall predictive accuracy was only marginally better with penalised logistic regression methods, benefits were most clear in their capacity to select a manageable subset of indicators. Preference to competing penalised logistic regression methods may therefore be guided by feature selection capability, and thus interpretative considerations, rather than predictive performance alone.
Collapse
Affiliation(s)
- Christopher J. Greenwood
- Faculty of Health, School of Psychology, Centre for Social and Early Emotional Development, Deakin University, Geelong, Australia
- Centre for Adolescent Health, Murdoch Children’s Research Institute, Melbourne, Australia
- * E-mail:
| | - George J. Youssef
- Faculty of Health, School of Psychology, Centre for Social and Early Emotional Development, Deakin University, Geelong, Australia
- Centre for Adolescent Health, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Primrose Letcher
- Department of Paediatrics, Royal Children’s Hospital, University of Melbourne, Melbourne, Australia
| | - Jacqui A. Macdonald
- Faculty of Health, School of Psychology, Centre for Social and Early Emotional Development, Deakin University, Geelong, Australia
- Centre for Adolescent Health, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Lauryn J. Hagg
- Faculty of Health, School of Psychology, Centre for Social and Early Emotional Development, Deakin University, Geelong, Australia
| | - Ann Sanson
- Department of Paediatrics, Royal Children’s Hospital, University of Melbourne, Melbourne, Australia
| | - Jenn Mcintosh
- Faculty of Health, School of Psychology, Centre for Social and Early Emotional Development, Deakin University, Geelong, Australia
- Centre for Adolescent Health, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Delyse M. Hutchinson
- Faculty of Health, School of Psychology, Centre for Social and Early Emotional Development, Deakin University, Geelong, Australia
- Centre for Adolescent Health, Murdoch Children’s Research Institute, Melbourne, Australia
- Department of Paediatrics, Royal Children’s Hospital, University of Melbourne, Melbourne, Australia
- Faculty of Medicine, National Drug and Alcohol Research Centre, University of New South Wales, Randwick, Australia
| | - John W. Toumbourou
- Faculty of Health, School of Psychology, Centre for Social and Early Emotional Development, Deakin University, Geelong, Australia
| | - Matthew Fuller-Tyszkiewicz
- Faculty of Health, School of Psychology, Centre for Social and Early Emotional Development, Deakin University, Geelong, Australia
| | - Craig A. Olsson
- Faculty of Health, School of Psychology, Centre for Social and Early Emotional Development, Deakin University, Geelong, Australia
- Centre for Adolescent Health, Murdoch Children’s Research Institute, Melbourne, Australia
- Department of Paediatrics, Royal Children’s Hospital, University of Melbourne, Melbourne, Australia
| |
Collapse
|
13
|
Afzali MH, Dagher A, Edalati H, Bourque J, Spinney S, Sharkey RJ, Conrod P. Adolescent Resting-State Brain Networks and Unique Variability of Conduct Problems Within the Externalizing Dimension. J Pers Disord 2020; 34:609-627. [PMID: 33074059 DOI: 10.1521/pedi.2020.34.5.609] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The externalizing psychopathological dimension is associated with alterations in adolescents' functional brain connectivity. The current study aims to identify the functional correlates of the unique variability in conduct problems within the context of the broad externalizing dimension. The broad externalizing dimension and unique variability in conduct problems were estimated using a bifactor model. Resting-state data were available for a sample of 125 adolescents. Based on multiresolution parcellation of functional brain networks atlas, major resting-state functional brain networks and the connectivity correlates of unique conduct problems and the broad externalizing dimension were established. The broad externalizing dimension was related to connectivity alterations in the ventral attention/salience network, while unique variability in conduct problems dimension was related to connectivity alterations in the cerebellum crusi as well as the mesolimbic network. The current study is a first step toward the identification of functional resting-state network correlates of broad and specific variability in the externalizing dimension.
Collapse
Affiliation(s)
- Mohammad H Afzali
- Department of Psychiatry, University of Montreal, Montréal, Quebec, Canada
| | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Hanie Edalati
- Department of Psychiatry, University of Montreal, Montréal, Quebec, Canada
| | - Josiane Bourque
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sean Spinney
- Department of Psychiatry, University of Montreal, Montréal, Quebec, Canada
| | | | - Patricia Conrod
- Department of Psychiatry, University of Montreal, Montréal, Quebec, Canada
| |
Collapse
|
14
|
Salazar de Pablo G, Studerus E, Vaquerizo-Serrano J, Irving J, Catalan A, Oliver D, Baldwin H, Danese A, Fazel S, Steyerberg EW, Stahl D, Fusar-Poli P. Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice. Schizophr Bull 2020; 47:284-297. [PMID: 32914178 PMCID: PMC7965077 DOI: 10.1093/schbul/sbaa120] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. METHODS PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. FINDINGS Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy. INTERPRETATION To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.
Collapse
Affiliation(s)
- Gonzalo Salazar de Pablo
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK,Institute of Psychiatry and Mental Health, Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón, CIBERSAM, Madrid, Spain,Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Erich Studerus
- Division of Personality and Developmental Psychology, Department of Psychology, University of Basel, Basel, Switzerland
| | - Julio Vaquerizo-Serrano
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK,Institute of Psychiatry and Mental Health, Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón, CIBERSAM, Madrid, Spain,Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Jessica Irving
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK
| | - Ana Catalan
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK,Department of Psychiatry, Basurto University Hospital, Bilbao, Spain,Mental Health Group, BioCruces Health Research Institute, Bizkaia, Spain,Neuroscience Department, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK
| | - Helen Baldwin
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK
| | - Andrea Danese
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK,Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK,National and Specialist CAMHS Clinic for Trauma, Anxiety, and Depression, South London and Maudsley NHS Foundation Trust, London, UK
| | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands,Department of Public Health, Erasmus MC, Rotterdam, the Netherlands
| | - Daniel Stahl
- Biostatistics Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK,OASIS Service, South London and Maudsley NHS Foundation Trust, London, UK,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK,To whom correspondence should be addressed; tel: +44-0-20-7848-0900, fax:+44-0-20-7848-0976, e-mail:
| |
Collapse
|
15
|
Abstract
PURPOSE OF REVIEW To provide an accessible overview of some of the most recent trends in the application of machine learning to the field of substance use disorders and their implications for future research and practice. RECENT FINDINGS Machine-learning (ML) techniques have recently been applied to substance use disorder (SUD) data for multiple predictive applications including detecting current abuse, assessing future risk and predicting treatment success. These models cover a wide range of machine-learning techniques and data types including physiological measures, longitudinal surveys, treatment outcomes, national surveys, medical records and social media. SUMMARY The application of machine-learning models to substance use disorder data shows significant promise, with some use cases and data types showing high predictive accuracy, particularly for models of physiological and behavioral measures for predicting current substance use, portending potential clinical diagnostic applications; however, these results are uneven, with some models performing poorly or at chance, a limitation likely reflecting insufficient data and/or weak validation methods. The field will likely benefit from larger and more multimodal datasets, greater standardization of data recording and rigorous testing protocols as well as greater use of modern deep neural network models applied to multimodal unstructured datasets.
Collapse
|
16
|
Brückl TM, Spoormaker VI, Sämann PG, Brem AK, Henco L, Czamara D, Elbau I, Grandi NC, Jollans L, Kühnel A, Leuchs L, Pöhlchen D, Schneider M, Tontsch A, Keck ME, Schilbach L, Czisch M, Lucae S, Erhardt A, Binder EB. The biological classification of mental disorders (BeCOME) study: a protocol for an observational deep-phenotyping study for the identification of biological subtypes. BMC Psychiatry 2020; 20:213. [PMID: 32393358 PMCID: PMC7216390 DOI: 10.1186/s12888-020-02541-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 03/10/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND A major research finding in the field of Biological Psychiatry is that symptom-based categories of mental disorders map poorly onto dysfunctions in brain circuits or neurobiological pathways. Many of the identified (neuro) biological dysfunctions are "transdiagnostic", meaning that they do not reflect diagnostic boundaries but are shared by different ICD/DSM diagnoses. The compromised biological validity of the current classification system for mental disorders impedes rather than supports the development of treatments that not only target symptoms but also the underlying pathophysiological mechanisms. The Biological Classification of Mental Disorders (BeCOME) study aims to identify biology-based classes of mental disorders that improve the translation of novel biomedical findings into tailored clinical applications. METHODS BeCOME intends to include at least 1000 individuals with a broad spectrum of affective, anxiety and stress-related mental disorders as well as 500 individuals unaffected by mental disorders. After a screening visit, all participants undergo in-depth phenotyping procedures and omics assessments on two consecutive days. Several validated paradigms (e.g., fear conditioning, reward anticipation, imaging stress test, social reward learning task) are applied to stimulate a response in a basic system of human functioning (e.g., acute threat response, reward processing, stress response or social reward learning) that plays a key role in the development of affective, anxiety and stress-related mental disorders. The response to this stimulation is then read out across multiple levels. Assessments comprise genetic, molecular, cellular, physiological, neuroimaging, neurocognitive, psychophysiological and psychometric measurements. The multilevel information collected in BeCOME will be used to identify data-driven biologically-informed categories of mental disorders using cluster analytical techniques. DISCUSSION The novelty of BeCOME lies in the dynamic in-depth phenotyping and omics characterization of individuals with mental disorders from the depression and anxiety spectrum of varying severity. We believe that such biology-based subclasses of mental disorders will serve as better treatment targets than purely symptom-based disease entities, and help in tailoring the right treatment to the individual patient suffering from a mental disorder. BeCOME has the potential to contribute to a novel taxonomy of mental disorders that integrates the underlying pathomechanisms into diagnoses. TRIAL REGISTRATION Retrospectively registered on June 12, 2019 on ClinicalTrials.gov (TRN: NCT03984084).
Collapse
Affiliation(s)
- Tanja M. Brückl
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Victor I. Spoormaker
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Philipp G. Sämann
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Anna-Katharine Brem
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany ,grid.38142.3c000000041936754XBerenson-Allen Center for Noninvasive Brain Stimulation and Division for Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Lara Henco
- grid.419548.50000 0000 9497 5095Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Darina Czamara
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Immanuel Elbau
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Norma C. Grandi
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Lee Jollans
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Anne Kühnel
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany ,grid.419548.50000 0000 9497 5095International Max Planck Research School – Translational Psychiatry (IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany
| | - Laura Leuchs
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Dorothee Pöhlchen
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany ,grid.419548.50000 0000 9497 5095International Max Planck Research School – Translational Psychiatry (IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany
| | - Maximilian Schneider
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Alina Tontsch
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Martin E. Keck
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Leonhard Schilbach
- grid.419548.50000 0000 9497 5095Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Michael Czisch
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Susanne Lucae
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Angelika Erhardt
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Elisabeth B. Binder
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany ,grid.189967.80000 0001 0941 6502Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
| |
Collapse
|
17
|
Abstract
As avid users of technology, adolescents are a key demographic to engage when designing and developing technology applications for health. There are multiple opportunities for improving adolescent health, from promoting preventive behaviors to providing guidance for adolescents with chronic illness in supporting treatment adherence and transition to adult health care systems. This article will provide a brief overview of current technologies and then highlight new technologies being used specifically for adolescent health, such as artificial intelligence, virtual and augmented reality, and machine learning. Because there is paucity of evidence in this field, we will make recommendations for future research.
Collapse
Affiliation(s)
- Ana Radovic
- Department of Pediatrics, School of Medicine, University of Pittsburgh and University of Pittsburgh Medical Center Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania;
| | - Sherif M Badawy
- Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; and.,Division of Hematology, Oncology, Neurooncology, and Stem Cell Transplantation, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| |
Collapse
|
18
|
Whelan R. Commentary on Afzali et al. (2019): Two data sets are better than one. Addiction 2019; 114:672-673. [PMID: 30854749 DOI: 10.1111/add.14574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 01/31/2019] [Indexed: 11/30/2022]
Affiliation(s)
- Robert Whelan
- School of Psychology, Trinity College Dublin, Dublin, Ireland.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| |
Collapse
|
19
|
Vassileva J, Conrod PJ. Impulsivities and addictions: a multidimensional integrative framework informing assessment and interventions for substance use disorders. Philos Trans R Soc Lond B Biol Sci 2019; 374:20180137. [PMID: 30966920 PMCID: PMC6335463 DOI: 10.1098/rstb.2018.0137] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/15/2018] [Indexed: 12/18/2022] Open
Abstract
Impulse control is becoming a critical survival skill for the twenty-first century. Impulsivity is implicated in virtually all externalizing behaviours and disorders, and figures prominently in the aetiology and long-term sequelae of substance use disorders (SUDs). Despite its robust clinical and predictive validity, the study of impulsivity is complicated by its multidimensional nature, characterized by a variety of trait-like personality dimensions, as well as by more state-dependent neurocognitive dimensions, with variable convergence across measures. This review provides a hierarchical framework for linking self-report and neurocognitive measures to latent constructs of impulsivity and, in turn, to different psychopathology vulnerabilities, including substance-specific addictions and comorbidities. Impulsivity dimensions are presented as novel behavioural targets for prevention and intervention. Novel treatment approaches addressing domains of impulsivity are reviewed and recommendations for future directions in research and clinical interventions for SUDs are offered. This article is part of the theme issue 'Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications'.
Collapse
Affiliation(s)
- Jasmin Vassileva
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Patricia J. Conrod
- Department of Psychiatry, University of Montreal, Montreal, Canada
- Centre de Recherche, CHU Ste Justine, Montreal, Canada
| |
Collapse
|