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Choe S, Agley J, Elam K, Bidulescu A, Seo DC. Identifying predictors of multi-year cannabis vaping in U.S. Young adults using machine learning. Addict Behav 2024; 160:108167. [PMID: 39341185 DOI: 10.1016/j.addbeh.2024.108167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 09/14/2024] [Accepted: 09/14/2024] [Indexed: 09/30/2024]
Abstract
INTRODUCTION Increasing number of current cannabis users report using a vaporized form of cannabis and young adults are most likely to vape cannabis. However, the number of studies on cannabis vaping is limited, and predictors of cannabis vaping among U.S. young adults remain unclear. Previous studies on cannabis vaping have known limitations, as they (1) relied heavily on regression-based approaches that often fail to examine complex and non-linear interactive effects, (2) focused on examining cannabis vaping initiation but not on its use over multiple years, and (3) failed to account for recreational cannabis legalization (RCL) status. METHODS This study was a secondary analysis of the restricted use files of the Population Assessment of Tobacco and Health Study, Waves 4-6 (December 2016-November 2021). A two-stage machine learning approach, which included Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Tree (CART), was used to identify predictors of multi-year cannabis vaping while accounting for state-level RCL status among a representative sample of U.S. young adults. RESULTS Stratified CART created a five-terminal-node prediction model for states with RCL (split by cannabis use, cigarette use, bullying behavior, and ethnicity) and a different five-terminal-node prediction model for states without RCL (split by cannabis use, heroin use, nicotine vaping, and hookah use). CONCLUSIONS Characteristics predicting multi-year cannabis vaping appear to differ from those of cannabis vaping initiation. Results also highlight the importance of accounting for RCL status because predictors of cannabis vaping may differ for individuals living in states with and without RCL.
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Affiliation(s)
- Siyoung Choe
- Department of Applied Health Science, Indiana University School of Public Health, 1025 E. 7th St., Bloomington, IN 47405-7109, USA.
| | - Jon Agley
- Department of Applied Health Science, Indiana University School of Public Health, 1025 E. 7th St., Bloomington, IN 47405-7109, USA.
| | - Kit Elam
- Department of Applied Health Science, Indiana University School of Public Health, 1025 E. 7th St., Bloomington, IN 47405-7109, USA.
| | - Aurelian Bidulescu
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, 1025 E. 7th St., Bloomington, IN 47405-7109, USA.
| | - Dong-Chul Seo
- Department of Applied Health Science, Indiana University School of Public Health, 1025 E. 7th St., Bloomington, IN 47405-7109, USA.
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Choquette EM, Forthman KL, Kirlic N, Stewart JL, Cannon MJ, Akeman E, McMillan N, Mesker M, Tarrasch M, Kuplicki R, Paulus MP, Aupperle RL. Impulsivity, trauma history, and interoceptive awareness contribute to completion of a criminal diversion substance use treatment program for women. Front Psychol 2024; 15:1390199. [PMID: 39295754 PMCID: PMC11408307 DOI: 10.3389/fpsyg.2024.1390199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 07/19/2024] [Indexed: 09/21/2024] Open
Abstract
Introduction In the US, women are one of the fastest-growing segments of the prison population and more than a quarter of women in state prison are incarcerated for drug offenses. Substance use criminal diversion programs can be effective. It may be beneficial to identify individuals who are most likely to complete the program versus terminate early as this can provide information regarding who may need additional or unique programming to improve the likelihood of successful program completion. Prior research investigating prediction of success in these programs has primarily focused on demographic factors in male samples. Methods The current study used machine learning (ML) to examine other non-demographic factors related to the likelihood of completing a substance use criminal diversion program for women. A total of 179 women who were enrolled in a criminal diversion program consented and completed neuropsychological, self-report symptom measures, criminal history and demographic surveys at baseline. Model one entered 145 variables into a machine learning (ML) ensemble model, using repeated, nested cross-validation, predicting subsequent graduation versus termination from the program. An identical ML analysis was conducted for model two, in which 34 variables were entered, including the Women's Risk/Needs Assessment (WRNA). Results ML models were unable to predict graduation at an individual level better than chance (AUC = 0.59 [SE = 0.08] and 0.54 [SE = 0.13]). Post-hoc analyses indicated measures of impulsivity, trauma history, interoceptive awareness, employment/financial risk, housing safety, antisocial friends, anger/hostility, and WRNA total score and risk scores exhibited medium to large effect sizes in predicting treatment completion (p < 0.05; ds = 0.29 to 0.81). Discussion Results point towards the complexity involved in attempting to predict treatment completion at the individual level but also provide potential targets to inform future research aiming to reduce recidivism.
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Affiliation(s)
| | | | - Namik Kirlic
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
| | | | | | - Nick McMillan
- Women in Recovery, Family and Children's Services, Tulsa, OK, United States
| | - Micah Mesker
- Women in Recovery, Family and Children's Services, Tulsa, OK, United States
| | - Mimi Tarrasch
- Women in Recovery, Family and Children's Services, Tulsa, OK, United States
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
| | - Robin L Aupperle
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
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Sándor MC, Bakó B. Unmasking Risky Habits: Identifying and Predicting Problem Gamblers Through Machine Learning Techniques. J Gambl Stud 2024; 40:1367-1377. [PMID: 38568337 PMCID: PMC11390795 DOI: 10.1007/s10899-024-10297-4] [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] [Accepted: 02/11/2024] [Indexed: 09/12/2024]
Abstract
The use of machine learning techniques to identify problem gamblers has been widely established. However, existing methods often rely on self-reported labeling, such as temporary self-exclusion or account closure. In this study, we propose a novel approach that combines two documented methods. First we create labels for problem gamblers in an unsupervised manner. Subsequently, we develop prediction models to identify these users in real-time. The methods presented in this study offer useful insights that can be leveraged to implement interventions aimed at guiding or discouraging players from engaging in disordered gambling behaviors. This has potential implications for promoting responsible gambling and fostering healthier player habits.
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Affiliation(s)
- Máté Cs Sándor
- Institute of Economics, Corvinus University of Budapest, Fővám tér 8, 1093, Budapest, Hungary
| | - Barna Bakó
- Institute of Economics, Corvinus University of Budapest, Fővám tér 8, 1093, Budapest, Hungary.
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Zhong F, Gu C. The impact of health information echo chambers on older adults avoidance behavior: the mediating role of information fatigue and the moderating role of trait mindfulness. Front Psychol 2024; 15:1412515. [PMID: 39228876 PMCID: PMC11370067 DOI: 10.3389/fpsyg.2024.1412515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 08/05/2024] [Indexed: 09/05/2024] Open
Abstract
Background In the digital media era, the prevalence of algorithm-driven content push has intensified the health information echo chambers phenomenon, characterized by excessive homogenization and overload of information. This phenomenon may negatively impact the older adults, a "digitally vulnerable" group, by limiting their access to diverse health information and potentially inducing health information avoidance behaviors. However, the psychological mechanisms within this impact process remain unclear, and this study aims to explore them. Additionally, current study introduces trait mindfulness as a potential intervention tool in reducing health information avoidance behavior among the older adults. Methods This study constructs an impact model based on the Stress-Coping Theory and the Stress-Strain-Outcome (SSO) framework. It identifies health information similarity, relevance, and overload as characteristics of the echo chamber, constituting the Stress part; information fatigue as the Strain; and health information avoidance as the Outcome, with trait mindfulness serving as a moderating factor between Strain and Outcome. A cross-sectional survey of 236 older adults individuals aged 60 and above was conducted, and the collected data were analyzed using SmartPLS and SPSS. Results The study found that health information similarity and overload significantly increased health information fatigue among the older adults, thereby promoting avoidance behavior, but no significant correlation was found between relevance and fatigue. While there was no significant correlation between trait mindfulness and health information fatigue, it effectively moderated the avoidance behavior induced by fatigue. Conclusion Current study reveals the impact of the health information echo chamber phenomenon on health information avoidance behavior among the older adults and the moderating role of trait mindfulness. It emphasizes the importance of breaking the health information echo chamber and promoting diverse information dissemination to improve the health information behavior of the older adults. Furthermore, the moderating role of trait mindfulness demonstrates positive intervention potential, providing the older adults with a psychological adjustment mechanism to accept and process health information with a more open attitude, thereby reducing unproductive avoidance behavior.
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Affiliation(s)
- Fuxiu Zhong
- Department of Hepatobiliary Surgery Nursing, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chenyu Gu
- School of Journalism and Communication, Minjiang University, Fuzhou, China
- Fujian Digital Media Economy Research Center, Fujian Social Science Research Base, Fuzhou, China
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Wang KC, Ojeda NB, Wang H, Chiang HS, Tucci MA, Lee JW, Wei HC, Kaizaki-Mitsumoto A, Tanaka S, Dankhara N, Tien LT, Fan LW. Neonatal brain inflammation enhances methamphetamine-induced reinstated behavioral sensitization in adult rats analyzed with explainable machine learning. Neurochem Int 2024; 176:105743. [PMID: 38641026 PMCID: PMC11102812 DOI: 10.1016/j.neuint.2024.105743] [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: 12/18/2023] [Revised: 03/15/2024] [Accepted: 04/15/2024] [Indexed: 04/21/2024]
Abstract
Neonatal brain inflammation produced by intraperitoneal (i.p.) injection of lipopolysaccharide (LPS) results in long-lasting brain dopaminergic injury and motor disturbances in adult rats. The goal of the present work is to investigate the effect of neonatal systemic LPS exposure (1 or 2 mg/kg, i.p. injection in postnatal day 5, P5, male rats)-induced dopaminergic injury to examine methamphetamine (METH)-induced behavioral sensitization as an indicator of drug addiction. On P70, subjects underwent a treatment schedule of 5 once daily subcutaneous (s.c.) administrations of METH (0.5 mg/kg) (P70-P74) to induce behavioral sensitization. Ninety-six hours following the 5th treatment of METH (P78), the rats received one dose of 0.5 mg/kg METH (s.c.) to reintroduce behavioral sensitization. Hyperlocomotion is a critical index caused by drug abuse, and METH administration has been shown to produce remarkable locomotor-enhancing effects. Therefore, a random forest model was used as the detector to extract the feature interaction patterns among the collected high-dimensional locomotor data. Our approaches identified neonatal systemic LPS exposure dose and METH-treated dates as features significantly associated with METH-induced behavioral sensitization, reinstated behavioral sensitization, and perinatal inflammation in this experimental model of drug addiction. Overall, the analysis suggests that the implementation of machine learning strategies is sensitive enough to detect interaction patterns in locomotor activity. Neonatal LPS exposure also enhanced METH-induced reduction of dopamine transporter expression and [3H]dopamine uptake, reduced mitochondrial complex I activity, and elevated interleukin-1β and cyclooxygenase-2 concentrations in the P78 rat striatum. These results indicate that neonatal systemic LPS exposure produces a persistent dopaminergic lesion leading to a long-lasting change in the brain reward system as indicated by the enhanced METH-induced behavioral sensitization and reinstated behavioral sensitization later in life. These findings indicate that early-life brain inflammation may enhance susceptibility to drug addiction development later in life, which provides new insights for developing potential therapeutic treatments for drug addiction.
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Affiliation(s)
- Kuo-Ching Wang
- Department of Anesthesiology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei City, Taiwan
| | - Norma B Ojeda
- Department of Pediatrics, Division of Newborn Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA; Department of Advanced Biomedical Education, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Haifeng Wang
- Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, 39762, USA
| | - Han-Sun Chiang
- School of Medicine, Fu Jen Catholic University, Xinzhuang Dist, New Taipei City, 24205, Taiwan
| | - Michelle A Tucci
- Department of Anesthesiology, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Jonathan W Lee
- Department of Pediatrics, Division of Newborn Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Han-Chi Wei
- School of Medicine, Fu Jen Catholic University, Xinzhuang Dist, New Taipei City, 24205, Taiwan
| | - Asuka Kaizaki-Mitsumoto
- Department of Pediatrics, Division of Newborn Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA; Department of Toxicology, Showa University Graduate School of Pharmacy, Shinagawa-ku, Tokyo, 142-8555, Japan
| | - Sachiko Tanaka
- Center for Research and Development in Pharmacy Education, School of Pharmacy, Nihon University, Funabashi, Chiba, 274-8555, Japan
| | - Nilesh Dankhara
- Department of Pediatrics, Division of Newborn Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Lu-Tai Tien
- School of Medicine, Fu Jen Catholic University, Xinzhuang Dist, New Taipei City, 24205, Taiwan.
| | - Lir-Wan Fan
- Department of Pediatrics, Division of Newborn Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA.
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Hampton J, Mugambi P, Caggiano E, Eugene R, Valente A, Taylor M, Carreiro S. Closing the Digital Divide in Interventions for Substance Use Disorder. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2024; 9:e240002. [PMID: 38726224 PMCID: PMC11081399 DOI: 10.20900/jpbs.20240002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2024]
Abstract
Digital health interventions are exploding in today's medical practice and have tremendous potential to support the treatment of substance use disorders (SUD). Developers and healthcare providers alike must be cognizant of the potential for digital interventions to exacerbate existing inequities in SUD treatment, particularly as they relate to Social Determinants of Health (SDoH). To explore this evolving area of study, this manuscript will review the existing concepts of the digital divide and digital inequities, and the role SDoH play as drivers of digital inequities. We will then explore how the data used and modeling strategies can create bias in digital health tools for SUD. Finally, we will discuss potential solutions and future directions to bridge these gaps including smartphone ownership, Wi-Fi access, digital literacy, and mitigation of historical, algorithmic, and measurement bias. Thoughtful design of digital interventions is quintessential to reduce the risk of bias, decrease the digital divide, and create equitable health outcomes for individuals with SUD.
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Affiliation(s)
- Jazmin Hampton
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
- Washington University of Health and Science, San Pedro, Belize, Central America
- Division of Public Health, Walden University, Minneapolis, MN 55401, USA
| | - Purity Mugambi
- Manning College of Information and Computer Sciences, University of Massachusetts-Amherst, Amherst, MA 01003, USA
| | - Emily Caggiano
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Reynalde Eugene
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Alycia Valente
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Melissa Taylor
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Stephanie Carreiro
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
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Buchman DZ, Imahori D, Lo C, Hui K, Walker C, Shaw J, Davis KD. The Influence of Using Novel Predictive Technologies on Judgments of Stigma, Empathy, and Compassion among Healthcare Professionals. AJOB Neurosci 2024; 15:32-45. [PMID: 37450417 DOI: 10.1080/21507740.2023.2225470] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND Our objective was to evaluate whether the description of a machine learning (ML) app or brain imaging technology to predict the onset of schizophrenia or alcohol use disorder (AUD) influences healthcare professionals' judgments of stigma, empathy, and compassion. METHODS We randomized healthcare professionals (N = 310) to one vignette about a person whose clinician seeks to predict schizophrenia or an AUD, using a ML app, brain imaging, or a psychosocial assessment. Participants used scales to measure their judgments of stigma, empathy, and compassion. RESULTS Participants randomized to the ML vignette endorsed less anger and more fear relative to the psychosocial vignette, and the brain imaging vignette elicited higher pity ratings. The brain imaging and ML vignettes evoked lower personal responsibility judgments compared to the psychosocial vignette. Physicians and nurses reported less empathy than clinical psychologists. CONCLUSIONS The use of predictive technologies may reinforce essentialist views about mental health and substance use that may increase specific aspects of stigma and reduce others.
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Affiliation(s)
- Daniel Z Buchman
- Centre for Addiction and Mental Health
- Dalla Lana School of Public Health, University of Toronto
- University of Toronto Joint Centre for Bioethics
| | | | - Christopher Lo
- Dalla Lana School of Public Health, University of Toronto
- Temerty Faculty of Medicine, University of Toronto
- College of Healthcare Sciences, James Cook University, Singapore
| | - Katrina Hui
- Centre for Addiction and Mental Health
- Temerty Faculty of Medicine, University of Toronto
| | | | - James Shaw
- University of Toronto Joint Centre for Bioethics
- Temerty Faculty of Medicine, University of Toronto
| | - Karen D Davis
- Temerty Faculty of Medicine, University of Toronto
- Krembil Brain Institute, University Health Network
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Heberle BA, Kluwe-Schiavon B, Bicca C, Melo Rothmann L, Grassi-Oliveira R, Viola TW. Examining predictors of cocaine withdrawal syndrome at the end of detoxification treatment in women with cocaine use disorder. J Psychiatr Res 2024; 169:247-256. [PMID: 38048674 PMCID: PMC10805009 DOI: 10.1016/j.jpsychires.2023.11.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 12/06/2023]
Abstract
BACKGROUND Detoxification is frequently recommended as a treatment for moderate to severe Cocaine Use Disorder (CUD). However, the response to detoxification varies among patients, and previous studies have focused mostly on patterns of drug use behavior to test associations with treatment outcomes, overlooking the potential impact of psychosocial factors, other clinical variables, and individual life experiences. In this study we comprehensively examined several variables aiming to find the most relevant predictors to classify patients with severe versus non-severe cocaine withdrawal symptoms at the end of detoxification. METHODS Data from 284 women with CUD who enrolled in a 3-week detoxification program was used in this longitudinal study. Psychosocial, clinical, and drug use behavior characteristics were evaluated, generating a dataset with 256 potential predictors. We tested six different machine learning classification algorithms. RESULTS The best classification algorithm achieved an average accuracy and ROC-AUC of approximately 70%. The 16 features selected as best predictors were the severity of psychiatric, family, and social problems and the level of exposure to childhood maltreatment. Features associated with drug-use behavior included days consuming drugs and having craving symptoms in the last month before treatment, number of previous drug/alcohol-related treatments, and a composite score of addiction severity. The level of cocaine withdrawal syndrome at the beginning of detoxification was also a key feature for classification. A network analysis revealed the pattern of association between predictors. CONCLUSION These variables can be assessed in real-world clinical settings, potentially helping clinicians to identify individuals with severe cocaine withdrawal that is likely to be sustained over the course of detoxification.
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Affiliation(s)
| | - Bruno Kluwe-Schiavon
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Carla Bicca
- School of Medicine, Brain Institute of Rio Grande do Sul, Developmental Cognitive Neuroscience Lab, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Leonardo Melo Rothmann
- Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Rodrigo Grassi-Oliveira
- School of Medicine, Brain Institute of Rio Grande do Sul, Developmental Cognitive Neuroscience Lab, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil; Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Thiago Wendt Viola
- School of Medicine, Brain Institute of Rio Grande do Sul, Developmental Cognitive Neuroscience Lab, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil.
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Garbin C, Marques N, Marques O. Machine learning for predicting opioid use disorder from healthcare data: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107573. [PMID: 37148670 DOI: 10.1016/j.cmpb.2023.107573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/16/2023] [Accepted: 04/26/2023] [Indexed: 05/08/2023]
Abstract
INTRODUCTION The US opioid epidemic has been one of the leading causes of injury-related deaths according to the CDC Injury Center. The increasing availability of data and tools for machine learning (ML) resulted in more researchers creating datasets and models to help analyze and mitigate the crisis. This review investigates peer-reviewed journal papers that applied ML models to predict opioid use disorder (OUD). The review is split into two parts. The first part summarizes the current research in OUD prediction with ML. The second part evaluates how ML techniques and processes were used to achieve these results and suggests improvements to refine further attempts to use ML for OUD prediction. METHODS The review includes peer-reviewed journal papers published on or after 2012 that use healthcare data to predict OUD. We searched Google Scholar, Semantic Scholar, PubMed, IEEE Xplore, and Science.gov in September of 2022. Data extracted includes the study's goal, dataset used, cohort selected, types of ML models created, model evaluation metrics, and the details of the ML tools and techniques used to create the models. RESULTS The review analyzed 16 papers. Three papers created their dataset, five used a publicly available dataset, and the remaining eight used a private dataset. Cohort size ranged from the low hundreds to over half a million. Six papers used one type of ML model, and the remaining ten used up to five different ML models. The reported ROC AUC was higher than 0.8 for all but one of the papers. Five papers used only non-interpretable models, and the other 11 used interpretable models exclusively or in combination with non-interpretable ones. The interpretable models were the highest or second-highest ROC AUC values. Most papers did not sufficiently describe the ML techniques and tools used to produce their results. Only three papers published their source code. CONCLUSIONS We found that while there are indications that ML methods applied to OUD prediction may be valuable, the lack of details and transparency in creating the ML models limits their usefulness. We end the review with recommendations to improve studies on this critical healthcare subject.
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Affiliation(s)
- Christian Garbin
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA.
| | - Nicholas Marques
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA
| | - Oge Marques
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA
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Kulkarni KR, Schafer M, Berner LA, Fiore VG, Heflin M, Hutchison K, Calhoun V, Filbey F, Pandey G, Schiller D, Gu X. An Interpretable and Predictive Connectivity-Based Neural Signature for Chronic Cannabis Use. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:320-330. [PMID: 35659965 PMCID: PMC9708942 DOI: 10.1016/j.bpsc.2022.04.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/10/2022] [Accepted: 04/27/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Cannabis is one of the most widely used substances in the world, with usage trending upward in recent years. However, although the psychiatric burden associated with maladaptive cannabis use has been well established, reliable and interpretable biomarkers associated with chronic use remain elusive. In this study, we combine large-scale functional magnetic resonance imaging with machine learning and network analysis and develop an interpretable decoding model that offers both accurate prediction and novel insights into chronic cannabis use. METHODS Chronic cannabis users (n = 166) and nonusing healthy control subjects (n = 124) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic users and nonusers based on whole-brain functional connectivity. Network analysis was used to identify the most predictive regions and communities. RESULTS We obtained high (∼80% out-of-sample) accuracy across 4 different classification models, demonstrating that task-evoked connectivity can successfully differentiate chronic cannabis users from nonusers. We also identified key predictive regions implicating motor, sensory, attention, and craving-related areas, as well as a core set of brain networks that contributed to successful classification. The most predictive networks also strongly correlated with cannabis craving within the chronic user group. CONCLUSIONS This novel approach produced a neural signature of chronic cannabis use that is both accurate in terms of out-of-sample prediction and interpretable in terms of predictive networks and their relation to cannabis craving.
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Affiliation(s)
- Kaustubh R Kulkarni
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew Schafer
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Laura A Berner
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Vincenzo G Fiore
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matt Heflin
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kent Hutchison
- Institute for Cognitive Science, University of Colorado, Boulder, Colorado
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia
| | - Francesca Filbey
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Daniela Schiller
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Xiaosi Gu
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York.
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Wang L, Zhang Z, Wang S, Wang M, Dong H, Chen S, Du X, Dong GH. Deficient dynamics of prefrontal-striatal and striatal-default mode network (DMN) neural circuits in internet gaming disorder. J Affect Disord 2023; 323:336-344. [PMID: 36435399 DOI: 10.1016/j.jad.2022.11.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 11/07/2022] [Accepted: 11/20/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Studies have proven that individuals with internet gaming disorder (IGD) show impaired cognitive control over game craving; however, the neural mechanism underlying this process remains unclear. Accordingly, the present study aimed to investigate the dynamic features of brain functional networks of individuals with IGD during rest, which have barely been understood until now. METHODS Resting-state fMRI data were collected from 333 subjects (123 subjects with IGD (males/females: 73/50) and 210 healthy controls (males/females: 135/75)). First, the data-driven methodology, named co-activation pattern analysis, was applied to investigate the dynamic features of nucleus accumbens (the core region involved in craving/reward processing and addiction)-centered brain networks in IGD. Further, machine learning analysis was conducted to investigate the prediction effect of the dynamic features on participants' addiction severity. RESULTS Compared to controls, subjects in the IGD group showed decreased resilience, betweenness centrality and occurrence in the prefrontal-striatal neural circuit, and decreased in-degree in the striatal-default mode network (DMN) circuit. Moreover, these decreased dynamic features could significantly predict participants' addiction severity. LIMITATIONS The causal relationship between IGD and the abnormal dynamic features cannot be identified in this study. All the subjects were university students. CONCLUSIONS The present results revealed the underlying brain networks of uncontrollable craving and game-seeking behaviors in individuals with IGD during rest. The decreased dynamics of the prefrontal-striatal and striatal-DMN neural circuits might be potential biomarkers for predicting the addiction severity of IGD and potential targets for effective interventions to reduce game craving of this disorder.
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Affiliation(s)
- Lingxiao Wang
- Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China.
| | - Zhengjie Zhang
- Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China
| | - Shizhen Wang
- Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China
| | - Min Wang
- Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Hefei, Anhui Province, China
| | - Haohao Dong
- Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China
| | - Shuaiyu Chen
- Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China
| | - Xiaoxia Du
- School of Psychology, Shanghai University of Sport, Shanghai, China
| | - Guang-Heng Dong
- Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, China.
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12
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Fu R, Kundu A, Mitsakakis N, Elton-Marshall T, Wang W, Hill S, Bondy SJ, Hamilton H, Selby P, Schwartz R, Chaiton MO. Machine learning applications in tobacco research: a scoping review. Tob Control 2023; 32:99-109. [PMID: 34452986 DOI: 10.1136/tobaccocontrol-2020-056438] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 04/14/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Identify and review the body of tobacco research literature that self-identified as using machine learning (ML) in the analysis. DATA SOURCES MEDLINE, EMABSE, PubMed, CINAHL Plus, APA PsycINFO and IEEE Xplore databases were searched up to September 2020. Studies were restricted to peer-reviewed, English-language journal articles, dissertations and conference papers comprising an empirical analysis where ML was identified to be the method used to examine human experience of tobacco. Studies of genomics and diagnostic imaging were excluded. STUDY SELECTION Two reviewers independently screened the titles and abstracts. The reference list of articles was also searched. In an iterative process, eligible studies were classified into domains based on their objectives and types of data used in the analysis. DATA EXTRACTION Using data charting forms, two reviewers independently extracted data from all studies. A narrative synthesis method was used to describe findings from each domain such as study design, objective, ML classes/algorithms, knowledge users and the presence of a data sharing statement. Trends of publication were visually depicted. DATA SYNTHESIS 74 studies were grouped into four domains: ML-powered technology to assist smoking cessation (n=22); content analysis of tobacco on social media (n=32); smoker status classification from narrative clinical texts (n=6) and tobacco-related outcome prediction using administrative, survey or clinical trial data (n=14). Implications of these studies and future directions for ML researchers in tobacco control were discussed. CONCLUSIONS ML represents a powerful tool that could advance the research and policy decision-making of tobacco control. Further opportunities should be explored.
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Affiliation(s)
- Rui Fu
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Anasua Kundu
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Nicholas Mitsakakis
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Tara Elton-Marshall
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Wei Wang
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Sean Hill
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Susan J Bondy
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Hayley Hamilton
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Peter Selby
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Robert Schwartz
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Michael Oliver Chaiton
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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13
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Amlung M, Owens MM, Hargreaves T, Gray JC, Murphy CM, MacKillop J, Sweet LH. Neuroeconomic predictors of smoking cessation outcomes: A preliminary study of delay discounting in treatment-seeking adult smokers. Psychiatry Res Neuroimaging 2022; 327:111555. [PMID: 36327864 PMCID: PMC9729436 DOI: 10.1016/j.pscychresns.2022.111555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 08/31/2022] [Accepted: 10/17/2022] [Indexed: 12/04/2022]
Abstract
Large proportions of smokers are unsuccessful in evidence-based smoking cessation treatment and identifying prognostic predictors may inform improvements in treatment. Steep discounting of delayed rewards (delay discounting) is a robust predictor of poor smoking cessation outcome, but the underlying neural predictors have not been investigated. Forty-one treatment-seeking adult smokers completed a functional magnetic resonance imaging (fMRI) delay discounting paradigm prior to initiating a 9-week smoking cessation treatment protocol. Behavioral performance significantly predicted treatment outcomes (verified 7-day abstinence, n = 18; relapse, n = 23). Participants in the relapse group exhibited smaller area under the curve (d = 1.10) and smaller AUC was correlated with fewer days to smoking relapse (r = 0.56, p < 0.001) Neural correlates of discounting included medial and dorsolateral prefrontal cortex, posterior cingulate, precuneus and anterior insula, and interactions between choice type and relapse status were present for the dorsolateral prefrontal cortex, precuneus and the striatum. This initial investigation implicates differential neural activity in regions associated with frontal executive and default mode activity, as well as motivational circuits. Larger samples are needed to improve the resolution in identifying the neural underpinnings linking steep delay discounting to smoking cessation.
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Affiliation(s)
- Michael Amlung
- Department of Applied Behavioral Science, University of Kansas, Lawrence, KS, United States of America; Cofrin Logan Center for Addiction Research and Treatment, University of Kansas, Lawrence, KS, United States of America.
| | - Max M Owens
- Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON Canada; Peter Boris Centre for Addictions Research, McMaster University, Hamilton, ON, Canada
| | - Tegan Hargreaves
- Peter Boris Centre for Addictions Research, McMaster University, Hamilton, ON, Canada
| | - Joshua C Gray
- Department of Medical and Clinical Psychology, Uniformed Services University, Bethesda, MD, United States of America
| | - Cara M Murphy
- Behavioral and Social Sciences, Brown University, Providence, RI, United States of America
| | - James MacKillop
- Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON Canada; Peter Boris Centre for Addictions Research, McMaster University, Hamilton, ON, Canada
| | - Lawrence H Sweet
- Department of Psychology, University of Georgia, Athens, GA United States of America
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Ye S, Wang M, Yang Q, Dong H, Dong GH. Predicting the severity of internet gaming disorder with resting-state brain features: A multi-voxel pattern analysis. J Affect Disord 2022; 318:113-122. [PMID: 36031000 DOI: 10.1016/j.jad.2022.08.078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 06/09/2022] [Accepted: 08/22/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Internet gaming disorder (IGD) has become a worldwide mental health concern; however, the neural mechanism underlying this disorder remains unclear. Multivoxel pattern analysis (MVPA), a newly developed data-driven approach, can be used to investigate the neural features of IGD based on massive neural data. METHODS Resting-state fMRI data from four hundred and two participants with varying levels of IGD severity were recruited. Regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF) were calculated and subsequently decoded by applying MVPA. The highly weighted regions in both predictive models were selected as regions of interest for further graph theory and Granger causality analysis (GCA) to explore how they affect IGD severity. RESULTS The results revealed that the neural patterns of ReHo and ALFF can independently and significantly predict IGD severity. The highly weighted regions that contributed to both predictive models were the right precentral gyrus and left postcentral gyrus. Moreover, topological properties of the right precentral gyrus were significantly correlated with IGD severity; further GCA revealed effective connectivity from the right precentral gyrus to left precentral gyrus and dorsal anterior cingulate cortex, both of which were significantly associated with IGD severity. CONCLUSIONS The present study demonstrated that IGD has distinctive neural patterns, and this pattern could be found by machine learning. In addition, the neural features in the right precentral gyrus play a key role in predicting IGD severity. The current study revealed the neural features of IGD and provided a potential target for IGD interventions using brain modulation.
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Affiliation(s)
- Shuer Ye
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, PR China; Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China; Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Min Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Qun Yang
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, PR China.
| | - Haohao Dong
- Department of Psychology, Zhejiang Normal University, Jinhua, PR China
| | - Guang-Heng Dong
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China.
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15
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Ghaharian K, Abarbanel B, Phung D, Puranik P, Kraus S, Feldman A, Bernhard B. Applications of data science for responsible gambling: a scoping review. INTERNATIONAL GAMBLING STUDIES 2022. [DOI: 10.1080/14459795.2022.2135753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Kasra Ghaharian
- International Gaming Institute, University of Nevada, Las Vegas, NV, USA
- William F. Harrah College of Hospitality, University of Nevada, Las Vegas, NV, USA
| | - Brett Abarbanel
- International Gaming Institute, University of Nevada, Las Vegas, NV, USA
- William F. Harrah College of Hospitality, University of Nevada, Las Vegas, NV, USA
- Gambling Treatment and Research Clinic, University of Sydney, Sydney, New South Wales, Australia
| | - Dylan Phung
- Marshall School of Business, University of Southern California, Los Angeles, CA, USA
| | - Piyush Puranik
- Department of Computer Science, University of Nevada, Las Vegas, NV, USA
| | - Shane Kraus
- Department of Psychology, University of Nevada, Las Vegas, NV, USA
| | - Alan Feldman
- International Gaming Institute, University of Nevada, Las Vegas, NV, USA
| | - Bo Bernhard
- International Gaming Institute, University of Nevada, Las Vegas, NV, USA
- William F. Harrah College of Hospitality, University of Nevada, Las Vegas, NV, USA
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16
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Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
BACKGROUND Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people's health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. OBJECTIVE To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. METHODS A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. RESULTS The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. CONCLUSION Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
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Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
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17
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Kundu A, Fu R, Grace D, Logie C, Abramovich A, Baskerville B, Yager C, Schwartz R, Mitsakakis N, Planinac L, Chaiton M. Correlates of past year suicidal thoughts among sexual and gender minority young adults: A machine learning analysis. J Psychiatr Res 2022; 152:269-277. [PMID: 35759979 DOI: 10.1016/j.jpsychires.2022.06.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 05/27/2022] [Accepted: 06/07/2022] [Indexed: 01/14/2023]
Abstract
Sexual and gender minority populations are at elevated risk of experiencing suicidal thoughts and attempting suicide. The COVID-19 pandemic exacerbated mental health and substance use challenges among this population. We aimed to examine the relative importance and effects of intersectional factors and strong interactions associated with the risk of suicidal thoughts among Canadian lesbian, gay, bisexual, transgender, queer, questioning, intersex and Two Spirit (LGBTQI2S+) young adults. A cross-sectional online survey was conducted among LGBTQI2S + participants aged 16-29 years living in two Canadian provinces (Ontario, Quebec). Among 1414 participants (mean age 21.90 years), 61% (n = 857) participants reported suicidal thoughts in last 12 months. We built a random forest model to predict the risk of having past year suicidal thoughts, which achieved high performance with an area under the receiver operating characteristic curve (AUC) of 0.84. The top 10 correlates identified were: seeking help from health professionals for mental health or substance use issues since the start of the pandemic, current self-rated mental health status, insulted by parents or adults in childhood, ever heard that being identifying as LGBTQI2S+ is not normal, age in years, past week feeling depressed, lifetime diagnosis of mental illness, lifetime diagnosis of depressive disorder, past week feeling sad, ever pretended to be straight or cisgender to be accepted. The increase in the risk of suicidal thoughts for those having mental health challenges or facing minority stressors is more pronounced in those living in urban areas or being unemployed than those living in rural areas or being employed.
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Affiliation(s)
- Anasua Kundu
- Institute of Medical Science, University of Toronto, Toronto, Canada; Centre for Addiction and Mental Health, Toronto, Canada; Ontario Tobacco Research Unit, University of Toronto, Toronto, Canada.
| | - Rui Fu
- Department of Otolaryngology-Head and Neck Surgery, Sunnybrook Research Institute, University of Toronto, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Canada
| | - Daniel Grace
- Dalla Lana School of Public Health, University of Toronto, Canada
| | - Carmen Logie
- Factor-Inwentash Faculty of Social Work, University of Toronto, Canada; United Nations University Institute for Water, Environment & Health, Hamilton, Canada
| | - Alex Abramovich
- Centre for Addiction and Mental Health, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Canada
| | - Bruce Baskerville
- Canadian Institutes of Health Research, Ottawa, Canada; School of Pharmacy, Faculty of Science, University of Waterloo, Kitchener, Canada
| | | | - Robert Schwartz
- Centre for Addiction and Mental Health, Toronto, Canada; Ontario Tobacco Research Unit, University of Toronto, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Canada
| | - Nicholas Mitsakakis
- Dalla Lana School of Public Health, University of Toronto, Canada; Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Lynn Planinac
- Ontario Tobacco Research Unit, University of Toronto, Toronto, Canada
| | - Michael Chaiton
- Centre for Addiction and Mental Health, Toronto, Canada; Ontario Tobacco Research Unit, University of Toronto, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Canada
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18
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Vergara VM, Espinoza FA, Calhoun VD. Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers. Front Psychol 2022; 13:867067. [PMID: 35756267 PMCID: PMC9226579 DOI: 10.3389/fpsyg.2022.867067] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/23/2022] [Indexed: 11/25/2022] Open
Abstract
Alcohol use disorder (AUD) is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use of other substances such as nicotine that has been present in previous studies. Recent machine learning algorithms have raised the attention of researchers as a useful tool in studying and detecting AUD. This work uses AUD and controls samples free of any other substance use to assess the performance of a set of commonly used machine learning classifiers detecting AUD from resting state functional network connectivity (rsFNC) derived from independent component analysis. The cohort used included 51 alcohol dependent subjects and 51 control subjects. Despite alcohol, none of the 102 subjects reported use of nicotine, cannabis or any other dependence or habit formation substance. Classification features consisted of whole brain rsFNC estimates undergoing a feature selection process using a random forest approach. Features were then fed to 10 different machine learning classifiers to be evaluated based on their classification performance. A neural network classifier showed the highest performance with an area under the curve (AUC) of 0.79. Other good performers with similar AUC scores were logistic regression, nearest neighbor, and support vector machine classifiers. The worst results were obtained with Gaussian process and quadratic discriminant analysis. The feature selection outcome pointed to functional connections between visual, sensorimotor, executive control, reward, and salience networks as the most relevant for classification. We conclude that AUD can be identified using machine learning classifiers in the absence of nicotine comorbidity.
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Affiliation(s)
- Victor M Vergara
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Flor A Espinoza
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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Cresta Morgado P, Carusso M, Alonso Alemany L, Acion L. Practical foundations of machine learning for addiction research. Part I. Methods and techniques. THE AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 2022; 48:260-271. [PMID: 35389305 DOI: 10.1080/00952990.2021.1995739] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 06/14/2023]
Abstract
Machine learning assembles a broad set of methods and techniques to solve a wide range of problems, such as identifying individuals with substance use disorders (SUD), finding patterns in neuroimages, understanding SUD prognostic factors and their association, or determining addiction genetic underpinnings. However, the addiction research field underuses machine learning. This two-part narrative review focuses on machine learning tools and concepts, providing an introductory insight into their capabilities to facilitate their understanding and acquisition by addiction researchers. This first part presents supervised and unsupervised methods such as linear models, naive Bayes, support vector machines, artificial neural networks, and k-means. We illustrate each technique with examples of its use in current addiction research. We also present some open-source programming tools and methodological good practices that facilitate using these techniques. Throughout this work, we emphasize a continuum between applied statistics and machine learning, we show their commonalities, and provide sources for further reading to deepen the understanding of these methods. This two-part review is a primer for the next generation of addiction researchers incorporating machine learning in their projects. Researchers will find a bridge between applied statistics and machine learning, ways to expand their analytical toolkit, recommendations to incorporate well-established good practices in addiction data analysis (e.g., stating the rationale for using newer analytical tools, calculating sample size, improving reproducibility), and the vocabulary to enhance collaboration between researchers who do not conduct data analyses and those who do.
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Affiliation(s)
- Pablo Cresta Morgado
- Instituto de Cálculo, FCEyN, Universidad de Buenos Aires - CONICET, Buenos Aires, Argentina
| | - Martín Carusso
- Instituto de Cálculo, FCEyN, Universidad de Buenos Aires - CONICET, Buenos Aires, Argentina
| | | | - Laura Acion
- Instituto de Cálculo, FCEyN, Universidad de Buenos Aires - CONICET, Buenos Aires, Argentina
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
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20
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Lin Y, Sharma B, Thompson HM, Boley R, Perticone K, Chhabra N, Afshar M, Karnik NS. External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients. Addiction 2022; 117:925-933. [PMID: 34729829 PMCID: PMC9296269 DOI: 10.1111/add.15730] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 10/13/2021] [Indexed: 01/29/2023]
Abstract
BACKGROUND AND AIMS Unhealthy alcohol use (UAU) is one of the leading causes of global morbidity. A machine learning approach to alcohol screening could accelerate best practices when integrated into electronic health record (EHR) systems. This study aimed to validate externally a natural language processing (NLP) classifier developed at an independent medical center. DESIGN Retrospective cohort study. SETTING The site for validation was a midwestern United States tertiary-care, urban medical center that has an inpatient structured universal screening model for unhealthy substance use and an active addiction consult service. PARTICIPANTS/CASES Unplanned admissions of adult patients between October 23, 2017 and December 31, 2019, with EHR documentation of manual alcohol screening were included in the cohort (n = 57 605). MEASUREMENTS The Alcohol Use Disorders Identification Test (AUDIT) served as the reference standard. AUDIT scores ≥5 for females and ≥8 for males served as cases for UAU. To examine error in manual screening or under-reporting, a post hoc error analysis was conducted, reviewing discordance between the NLP classifier and AUDIT-derived reference. All clinical notes excluding the manual screening and AUDIT documentation from the EHR were included in the NLP analysis. FINDINGS Using clinical notes from the first 24 hours of each encounter, the NLP classifier demonstrated an area under the receiver operating characteristic curve (AUCROC) and precision-recall area under the curve (PRAUC) of 0.91 (95% CI = 0.89-0.92) and 0.56 (95% CI = 0.53-0.60), respectively. At the optimal cut point of 0.5, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.66 (95% CI = 0.62-0.69), 0.98 (95% CI = 0.98-0.98), 0.35 (95% CI = 0.33-0.38), and 1.0 (95% CI = 1.0-1.0), respectively. CONCLUSIONS External validation of a publicly available alcohol misuse classifier demonstrates adequate sensitivity and specificity for routine clinical use as an automated screening tool for identifying at-risk patients.
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Affiliation(s)
- Yiqi Lin
- Rush Medical CollegeRush UniversityChicagoILUSA
| | - Brihat Sharma
- Department of Psychiatry and Behavioral SciencesRush Medical College, Rush UniversityChicagoILUSA
| | - Hale M. Thompson
- Department of Psychiatry and Behavioral SciencesRush Medical College, Rush UniversityChicagoILUSA
| | - Randy Boley
- Department of Psychiatry and Behavioral SciencesRush Medical College, Rush UniversityChicagoILUSA
| | | | - Neeraj Chhabra
- Department of Emergency Medicine, Rush Medical CollegeRush UniversityChicagoILUSA,Department of Emergency MedicineJohn. H. Stroger, Jr. Hospital of Cook CountyChicagoILUSA
| | - Majid Afshar
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine and Public HealthUniversity of WisconsinMadisonWIUSA
| | - Niranjan S. Karnik
- Rush Medical CollegeRush UniversityChicagoILUSA,Department of Psychiatry and Behavioral SciencesRush Medical College, Rush UniversityChicagoILUSA
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21
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Roberts W, Zhao Y, Verplaetse T, Moore KE, Peltier MR, Burke C, Zakiniaeiz Y, McKee S. Using machine learning to predict heavy drinking during outpatient alcohol treatment. Alcohol Clin Exp Res 2022; 46:657-666. [PMID: 35420710 PMCID: PMC9180421 DOI: 10.1111/acer.14802] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 02/15/2022] [Accepted: 02/22/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Accurate clinical prediction supports the effective treatment of alcohol use disorder (AUD) and other psychiatric disorders. Traditional statistical techniques have identified patient characteristics associated with treatment outcomes. However, less work has focused on systematically leveraging these associations to create optimal predictive models. The current study demonstrates how machine learning can be used to predict clinical outcomes in people completing outpatient AUD treatment. METHOD We used data from the COMBINE multisite clinical trial (n = 1383) to develop and test predictive models. We identified three priority prediction targets, including (1) heavy drinking during the first month of treatment, (2) heavy drinking during the last month of treatment, and (3) heavy drinking between weekly/bi-weekly sessions. Models were generated using the random forest algorithm. We used "leave sites out" partitioning to externally validate the models in trial sites that were not included in the model training. Stratified model development was used to test for sex differences in the relative importance of predictive features. RESULTS Models predicting heavy alcohol use during the first and last months of treatment showed internal cross-validation area under the curve (AUC) scores ranging from 0.67 to 0.74. AUC was comparable in the external validation using data from held-out sites (AUC range = 0.69 to 0.72). The model predicting between-session heavy drinking showed strong classification accuracy in internal cross-validation (AUC = 0.89) and external test samples (AUC range = 0.80 to 0.87). Stratified analyses showed substantial sex differences in optimal feature sets. CONCLUSION Machine learning techniques can predict alcohol treatment outcomes using routinely collected clinical data. This technique has the potential to greatly improve clinical prediction accuracy without requiring expensive or invasive assessment methods. More research is needed to understand how best to deploy these models.
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Affiliation(s)
- Walter Roberts
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.,Department of Psychology, East Tennessee State University, Johnson City, Tennessee, USA
| | - Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Terril Verplaetse
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Kelly E Moore
- Department of Psychology, East Tennessee State University, Johnson City, Tennessee, USA
| | - MacKenzie R Peltier
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.,Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
| | - Catherine Burke
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Yasmin Zakiniaeiz
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Sherry McKee
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
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22
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Javidan AP, Li A, Lee MH, Forbes TL, Naji F. A Systematic Review and Bibliometric Analysis of Applications of Artificial Intelligence and Machine Learning in Vascular Surgery. Ann Vasc Surg 2022; 85:395-405. [PMID: 35339595 DOI: 10.1016/j.avsg.2022.03.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 02/22/2022] [Accepted: 03/12/2022] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) and machine learning (ML) have seen increasingly intimate integration with medicine and healthcare in the last two decades. The objective of this study was to summarize all current applications of AI and ML in the vascular surgery literature and to conduct a bibliometric analysis of the published studies. METHODS A comprehensive literature search was conducted through EMBASE, MEDLINE, and Ovid HealthStar from inception until February 19, 2021. Reporting of this study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Title and abstract screening, full-text screening, and data extraction were conducted in duplicate. Data extracted included study meta-data, the clinical area of study within vascular surgery, type of AI/ML method used, data set, and the application of AI/ML. Publishing journals were classified as having either a clinical scope or technical scope. Author academic background was classified as clinical, non-clinical (e.g., engineering) or both, depending on author affiliation. RESULTS The initial search identified 7434 studies, of which 249 were included for final analysis. The rate of publications is exponentially increasing, with 158 (63%) studies being published in the last 5 years. Studies were most commonly related to carotid artery disease (118, 47%), abdominal aortic aneurysms (51, 20%), and peripheral arterial disease (26, 10%). Study authors employed an average of 1.50 (range: 1-6) distinct AI methods in their studies. The application of AI/ML methods broadly related to predictive models (54, 22%), image segmentation (49, 19.4%), diagnostic methods (46, 18%), or multiple combined applications (91, 37%). The most commonly used AI/ML methods were artificial neural networks (155/378 use cases, 41%), support vector machines (64, 17%), k-nearest neighbors algorithm (26, 7%), and random forests (23, 6%). Data sets to which these AI/ML methods were applied frequently involved ultrasound images (87, 35%), CT images (42, 17%), clinical data (34, 14%) or multiple data sets (36, 14%). Overall, 22 (9%) studies were published in journals specific to vascular surgery, with the majority (147/249, 59%) being published in journals with a scope related to computer science or engineering. Among 1576 publishing authors, 46% had exclusively a clinical background, 48% a non-clinical background, and 5% had both a clinical and non-clinical background. CONCLUSION There is an exponentially growing body of literature describing the use of AI and ML in vascular surgery. There is a focus on carotid artery disease and abdominal aortic disease, with many other areas of vascular surgery underrepresented. Neural networks and support vector machines composed most AI methods in the literature. As AI/ML continues to see more expanded applications in the field, it is important that vascular surgeons appreciate its potential and limitations. Additionally, as it sees increasing use, there is a need for clinicians with expertise in AI/ML methods who can optimize its transition into daily practice.
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Affiliation(s)
- Arshia P Javidan
- Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Institute of Health Policy Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada.
| | - Allen Li
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Michael H Lee
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Thomas L Forbes
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Faysal Naji
- Department of Vascular Surgery, McMaster University, Hamilton, Ontario, Canada
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23
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Han DH, Seo DC. Identifying risk profiles for marijuana vaping among U.S. young adults by recreational marijuana legalization status: A machine learning approach. Drug Alcohol Depend 2022; 232:109330. [PMID: 35123363 DOI: 10.1016/j.drugalcdep.2022.109330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/17/2022] [Accepted: 01/21/2022] [Indexed: 11/28/2022]
Abstract
INTRODUCTION This study attempted to identify risk profiles of marijuana vaping by state-level recreational marijuana legalization (RML) status among U.S. young adults (YA). METHODS Data were drawn from the most recent two waves of restricted use files of the Population Assessment of Tobacco and Health Study with state identifiers. We analyzed 6155 young adult (18-24 years) respondents who were naïve to marijuana vaping at Wave 4 and had matched data at Wave 5. We employed a two-stage machine learning approach to predict marijuana vaping initiation at Wave 5 with predictors measured at Wave 4. RESULTS Among YA who had never vaped marijuana at Wave 4, 19% of those who lived in the states with RML and 15% of those who lived in the states without RML reported marijuana vaping at Wave 5. Substance-use-related predictors were rarely found as leading predictors in the states with RML. In the states without RML, substance use behaviors, including electronic nicotine delivery systems and smokeless tobacco use, and the presence of externalizing symptoms emerged as predictors for marijuana vaping. Results also revealed that nonlinear interactions between the predictors of marijuana vaping. CONCLUSIONS Our results highlight the importance of accounting for the RML status in developing risk profiles of marijuana vaping. Externalizing symptoms may be a behavioral endophenotype of marijuana vaping in the states without RML. Machine learning appears to be a promising analytical approach to identify complex interactions between factors in predicting an emerging risk behavior such as marijuana vaping.
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Affiliation(s)
- Dae-Hee Han
- University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | - Dong-Chul Seo
- Indiana University School of Public Health, Bloomington, IN, USA.
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24
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Fu R, Schwartz R, Mitsakakis N, Diemert LM, O’Connor S, Cohen JE. Predictors of perceived success in quitting smoking by vaping: A machine learning approach. PLoS One 2022; 17:e0262407. [PMID: 35030208 PMCID: PMC8759658 DOI: 10.1371/journal.pone.0262407] [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: 09/11/2020] [Accepted: 12/25/2021] [Indexed: 11/18/2022] Open
Abstract
Prior research has suggested that a set of unique characteristics may be associated with adult cigarette smokers who are able to quit smoking using e-cigarettes (vaping). In this cross-sectional study, we aimed to identify and rank the importance of these characteristics using machine learning. During July and August 2019, an online survey was administered to a convenience sample of 889 adult smokers (age ≥ 20) in Ontario, Canada who tried vaping to quit smoking in the past 12 months. Fifty-one person-level characteristics, including a Vaping Experiences Score, were assessed in a gradient boosting machine model to classify the status of perceived success in vaping-assisted smoking cessation. This model was trained using cross-validation and tested using the receiver operating characteristic (ROC) curve. The top five most important predictors were identified using a score between 0% and 100% that represented the relative importance of each variable in model training. About 20% of participants (N = 174, 19.6%) reported success in vaping-assisted smoking cessation. The model achieved relatively high performance with an area under the ROC curve of 0.865 and classification accuracy of 0.831 (95% CI [confidence interval] 0.780 to 0.874). The top five most important predictors of perceived success in vaping-assisted smoking cessation were more positive experiences measured by the Vaping Experiences Score (100%), less previously failed quit attempts by vaping (39.0%), younger age (21.9%), having vaped 100 times (16.8%), and vaping shortly after waking up (15.8%). Our findings provide strong statistical evidence that shows better vaping experiences are associated with greater perceived success in smoking cessation by vaping. Furthermore, our study confirmed the strength of machine learning techniques in vaping-related outcomes research based on observational data.
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Affiliation(s)
- Rui Fu
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Robert Schwartz
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
| | - Nicholas Mitsakakis
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Lori M. Diemert
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Shawn O’Connor
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Joanna E. Cohen
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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25
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Rousseau S, Polachek IS, Frenkel TI. A machine learning approach to identifying pregnant women's risk for persistent post-traumatic stress following childbirth. J Affect Disord 2022; 296:136-149. [PMID: 34601301 DOI: 10.1016/j.jad.2021.09.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 08/11/2021] [Accepted: 09/12/2021] [Indexed: 10/20/2022]
Abstract
INTRO Recent literature identifies childbirth as a potentially traumatic event, following which mothers may develop symptoms of Post-Traumatic-Stress-Following-Childbirth (PTS-FC). Especially when persistent, PTS-FC may interfere with mothers' caregiving and associated infant development, underscoring the need for accurate predictive screening of risk. Drawing on recent developments in advanced statistical modeling, the aim of the current study was to identify a set of prenatal indicators and prediction rules that may accurately identify pregnant women's risk for developing symptoms of PTS-FC which persist throughout the early postpartum period. METHODS 182 women from the general population completed a comprehensive set of approximately 200 potentially predictive questions during pregnancy, and subsequently reported on their acute stress and PTS-FC at three days, one month, and three months postpartum (self-report and clinician-administered interview). Based on the postpartum acute stress and PTS-FC data, women were classified into profiles of "Stable-High-PTS-FC" and "Stable-Low-PTS-FC" by means of Latent-Class Analyses. Prenatal data were modeled to identify women at risk for "Stable-High PTS-FC". RESULTS Employing machine-learning decision-tree analyses, a total of 36 questions and 7 prediction-rules were selected. Based on a cost-rate of 15 versus 100 for false-negative "Stable-Low-PTS-FC" versus false-negative "Stable-High-PTS-FC", the final model showed 80.6% accuracy for "Stable-High-PTS-FC" prediction. DISCUSSION This study identifies a short set of questions and prediction rules that may be included in future large-scale validation studies aimed at developing and validating a brief PTS-FC screening instrument that could be implemented in general population prenatal healthcare practice. Accurate screening would allow for selective administering of preventive interventions towards women at risk.
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Affiliation(s)
- Sofie Rousseau
- Ziama Arkin Infancy Institute, Interdisciplinary Center (IDC) Herzliya, Hanadiv 71, 1st floor, Herzliya 46485, Israel; Baruch Ivcher School of Psychology, Interdisciplinary Center (IDC) Herzliya, HaUniversity 8, Herzliya 4610101, Israel
| | - Inbal Shlomi Polachek
- Be'er Ya'akov Medical Center, Israel; Tel Aviv University, Sackler School of Medicine, Tel Aviv, Israel
| | - Tahl I Frenkel
- Ziama Arkin Infancy Institute, Interdisciplinary Center (IDC) Herzliya, Hanadiv 71, 1st floor, Herzliya 46485, Israel; Baruch Ivcher School of Psychology, Interdisciplinary Center (IDC) Herzliya, HaUniversity 8, Herzliya 4610101, Israel.
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26
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Ottino-González J, Uhlmann A, Hahn S, Cao Z, Cupertino RB, Schwab N, Allgaier N, Alia-Klein N, Ekhtiari H, Fouche JP, Goldstein RZ, Li CSR, Lochner C, London ED, Luijten M, Masjoodi S, Momenan R, Oghabian MA, Roos A, Stein DJ, Stein EA, Veltman DJ, Verdejo-García A, Zhang S, Zhao M, Zhong N, Jahanshad N, Thompson PM, Conrod P, Mackey S, Garavan H. White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group. Drug Alcohol Depend 2022; 230:109185. [PMID: 34861493 PMCID: PMC8952409 DOI: 10.1016/j.drugalcdep.2021.109185] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/27/2021] [Accepted: 11/14/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND Nicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention. METHODS Eleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence. RESULTS The cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p < 0.001) and methamphetamine (AUC = 0.71, p < 0.001) relative to non-dependent controls. Classifications related to nicotine dependence proved modest (AUC = 0.62, p = 0.014). CONCLUSIONS Stimulant dependence was related to FA disturbances within tracts consistent with a role in addiction. The multivariate pattern of white matter differences proved sufficient to identify individuals with stimulant dependence, particularly for cocaine and methamphetamine.
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Affiliation(s)
- Jonatan Ottino-González
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States.
| | - Anne Uhlmann
- Department of Child & Adolescent Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Sage Hahn
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Zhipeng Cao
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Renata B. Cupertino
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Nathan Schwab
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Nicholas Allgaier
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Nelly Alia-Klein
- Department of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, New York, United States
| | - Hamed Ekhtiari
- Institute for Cognitive Sciences Studies, University of Tehran, Tehran, Iran,Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Jean-Paul Fouche
- SA MRC Genomics and Brain Disorders Unit, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Rita Z. Goldstein
- Department of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, New York, United States
| | - Chiang-Shan R. Li
- Department of Psychiatry, Yale University, New Haven, Connecticut, United States
| | - Christine Lochner
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Edythe D. London
- Department of Psychiatry and Biobehavioural Sciences, University of California, Los Angeles, California, United States
| | - Maartje Luijten
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Sadegh Masjoodi
- Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Reza Momenan
- Clinical Neuroimaging Research Core, National Institutes on Alcohol Abuse & Alcoholism, National Institutes of Health, Bethesda, Maryland, United States
| | - Mohammad Ali Oghabian
- Neuroimaging & Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Annerine Roos
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa,SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Dan J. Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Elliot A. Stein
- Neuroimaging Research Branch, Intramural Research Program, National Institute of Drug Abuse, Baltimore, Maryland, United States
| | - Dick J. Veltman
- Department of Psychiatry, Amsterdam UMC – location VUMC, Amsterdam, the Netherlands
| | - Antonio Verdejo-García
- School of Psychological Sciences & Turner Institute for Brain & Mental Health, Monash University, Melbourne, Australia
| | - Sheng Zhang
- Department of Psychiatry, Yale University, New Haven, Connecticut, United States
| | - Min Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Na Zhong
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Neda Jahanshad
- Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, San Diego, California, United States
| | - Paul M. Thompson
- Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, San Diego, California, United States
| | - Patricia Conrod
- Department of Psychiatry, Université de Montreal, Montreal, Quebec, Canada
| | - Scott Mackey
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
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Yan KX, Liu L, Li H. Application of machine learning in oral and maxillofacial surgery. Artif Intell Med Imaging 2021; 2:104-114. [DOI: 10.35711/aimi.v2.i6.104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 02/06/2023] Open
Abstract
Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.
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Affiliation(s)
- Kai-Xin Yan
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lei Liu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hui Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
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Perski O. Scientific and ethical challenges to defining what constitutes 'proportionate evidence' for the regulation and accreditation of applications to treat addiction. Addiction 2021; 116:3285-3287. [PMID: 34231264 DOI: 10.1111/add.15619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 06/17/2021] [Indexed: 01/12/2023]
Affiliation(s)
- Olga Perski
- Research Department of Behavioural Science and Health, University College London, London, UK
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29
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Kundu A, Chaiton M, Billington R, Grace D, Fu R, Logie C, Baskerville B, Yager C, Mitsakakis N, Schwartz R. Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review. JMIR Med Inform 2021; 9:e28962. [PMID: 34762059 PMCID: PMC8663464 DOI: 10.2196/28962] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 09/02/2021] [Accepted: 10/03/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods. OBJECTIVE This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field. METHODS The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings. RESULTS We included 11 studies in this review, of which 81% (9/11) were on mental health and 18% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45%), whereas sexual minority women were studied the least (2/11, 18%). Studies were categorized into 3 major domains: web content analysis (6/11, 54%), prediction modeling (4/11, 36%), and imaging studies (1/11, 9%). CONCLUSIONS ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health.
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Affiliation(s)
- Anasua Kundu
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Michael Chaiton
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Rebecca Billington
- Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada
| | - Daniel Grace
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Rui Fu
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Carmen Logie
- Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada
- Women's College Research Institute, Toronto, ON, Canada
| | - Bruce Baskerville
- Canadian Institutes of Health Research, Government of Canada, Ottawa, ON, Canada
- School of Pharmacy, Faculty of Science, University of Waterloo, Kitchener, ON, Canada
| | | | - Nicholas Mitsakakis
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Robert Schwartz
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
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Wang N, Yao D, Ma L, Liu M. Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI. Med Image Anal 2021; 75:102279. [PMID: 34731776 DOI: 10.1016/j.media.2021.102279] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/16/2021] [Accepted: 10/18/2021] [Indexed: 12/22/2022]
Abstract
Brain functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been widely employed to study neuropsychiatric disorders such as autism spectrum disorder (ASD). Existing studies usually suffer from (1) significant data heterogeneity caused by different scanners or studied populations in multiple sites, (2) curse of dimensionality caused by millions of voxels in each fMRI scan and a very limited number (tens or hundreds) of training samples, and (3) poor interpretability, which hinders the identification of reproducible disease biomarkers. To this end, we propose a Multi-site Clustering and Nested Feature Extraction (MC-NFE) method for fMRI-based ASD detection. Specifically, we first divide multi-site training data into ASD and healthy control (HC) groups. To model inter-site heterogeneity within each category, we use a similarity-driven multiview linear reconstruction model to learn latent representations and perform subject clustering within each group. We then design a nested singular value decomposition (SVD) method to mitigate inter-site heterogeneity and extract FC features by learning both local cluster-shared features across sites within each category and global category-shared features across ASD and HC groups, followed by a linear support vector machine (SVM) for ASD detection. Experimental results on 609 subjects with rs-fMRI from the ABIDE database with 21 imaging sites suggest that the proposed MC-NFE outperforms several state-of-the-art methods in ASD detection. The most discriminative FCs identified by the MC-NFE are mainly located in default mode network, salience network, and cerebellum region, which could be used as potential biomarkers for fMRI-based ASD analysis.
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Affiliation(s)
- Nan Wang
- East China Normal University, Shanghai 200062, China
| | - Dongren Yao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Lizhuang Ma
- East China Normal University, Shanghai 200062, China; Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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Mehrpour O, Saeedi F, Hoyte C. Decision tree outcome prediction of acute acetaminophen exposure in the United States: A study of 30,000 cases from the National Poison Data System. Basic Clin Pharmacol Toxicol 2021; 130:191-199. [PMID: 34649297 DOI: 10.1111/bcpt.13674] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/08/2021] [Accepted: 10/12/2021] [Indexed: 12/25/2022]
Abstract
Acetaminophen is one of the most commonly used analgesic drugs in the United States. However, the outcomes of acute acetaminophen overdose might be very serious in some cases. Therefore, prediction of the outcomes of acute acetaminophen exposure is crucial. This study is a 6-year retrospective cohort study using National Poison Data System (NPDS) data. A decision tree algorithm was used to determine the risk predictors of acetaminophen exposure. The decision tree model had an accuracy of 0.839, an accuracy of 0.836, a recall of 0.72, a specificity of 0.86 and an F1_score of 0.76 for the test group and an accuracy of 0.848, a recall of 0.85, a recall of 0.74, a specificity of 0.87 and an F1_score of 0.78 for the training group. Our results showed that elevated serum levels of liver enzymes, other liver function test abnormality, anorexia, acidosis, electrolyte abnormality, increased bilirubin, coagulopathy, abdominal pain, coma, increased anion gap, tachycardia and hypotension were the most important factors in determining the outcome of acute acetaminophen exposure. Therefore, the decision tree model is a reliable approach in determining the prognosis of acetaminophen exposure cases and can be used in an emergency room or during hospitalization.
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Affiliation(s)
- Omid Mehrpour
- Data Science Institute, Southern Methodist University, Dallas, Texas, USA.,Rocky Mountain Poison and Drug Safety, Denver Health and Hospital Authority, Denver, Colorado, USA
| | - Farhad Saeedi
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran.,Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
| | - Christopher Hoyte
- University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.,University of Colorado Hospital, Aurora, Colorado, USA
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Chi HM, Hsiao TC. Extended classifier system with continuous real-coded variables for feature extraction of instantaneous pulse-rate variability and respiration of individuals with gaming disorder. Biomed Eng Online 2021; 20:93. [PMID: 34556149 PMCID: PMC8461950 DOI: 10.1186/s12938-021-00930-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 09/06/2021] [Indexed: 02/03/2023] Open
Abstract
Background Individuals with gaming disorder (GD) exhibit autonomic nervous system responses that indicate dysfunctional emotion regulation. Pulse rate variability (PRV) is a valuable biomarker for investigating the autonomic function of patients with mental disorders. Because individuals with GD dynamically regulate emotions during gaming, the PRV response relating to GD is not well understood. To investigate the dynamic PRV responses of individuals with GD, this study proposed the indexes of instantaneous PRV (iPRV) and instantaneous respiratory frequency (IFresp) of arterial blood pressure signals using empirical mode decomposition and normalized direct-quadrature algorithms. iPRV consists of low-frequency (LF), high-frequency (HF), and very high-frequency (VHF) bands. Moreover, a novel method of extended classifier system with continuous real-coded variables (XCSR) was used to detect GD and extract GD-related iPRV features using iPRV and IFresp as input data. Results A total of 32 college students without depressive and anxiety symptoms or cardiovascular diseases were recruited in this study. Participants were grouped into the high-risk GD and low-risk GD using both Chen Internet Addiction Scale and Internet Gaming Disorder Questionnaire. Their arterial blood pressures signals were measured while they watched gameplay videos with negative or positive emotional stimuli. Seven participants with high-risk GD exhibited significantly increased normalized VHF (nVHF) PRV and IFresp readings and significantly decreased normalized LF (nLF) PRV readings and LF/HF PRV ratios (from baseline) during negative or positive gameplay videos stimuli. These participants also exhibited higher nVHF PRV and lower nLF PRV readings and LF/HF PRV ratios when they experienced negative gameplay video stimuli relative to 17 participants with low-risk GD. The classification accuracy of the XCSR reached 90% for both negative and positive video stimuli, and nVHF PRV was most frequently used to detect GD risk. Conclusions iPRV and IFresp can be used to detect GD and analyze the autonomic mechanism of individuals with GD.
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Affiliation(s)
- Hung-Ming Chi
- Department of Computer Science, College of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan, ROC
| | - Tzu-Chien Hsiao
- Department of Computer Science, College of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan, ROC. .,Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan, ROC.
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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.
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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.
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Roth CB, Papassotiropoulos A, Brühl AB, Lang UE, Huber CG. Psychiatry in the Digital Age: A Blessing or a Curse? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8302. [PMID: 34444055 PMCID: PMC8391902 DOI: 10.3390/ijerph18168302] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/31/2021] [Accepted: 08/03/2021] [Indexed: 12/23/2022]
Abstract
Social distancing and the shortage of healthcare professionals during the COVID-19 pandemic, the impact of population aging on the healthcare system, as well as the rapid pace of digital innovation are catalyzing the development and implementation of new technologies and digital services in psychiatry. Is this transformation a blessing or a curse for psychiatry? To answer this question, we conducted a literature review covering a broad range of new technologies and eHealth services, including telepsychiatry; computer-, internet-, and app-based cognitive behavioral therapy; virtual reality; digital applied games; a digital medicine system; omics; neuroimaging; machine learning; precision psychiatry; clinical decision support; electronic health records; physician charting; digital language translators; and online mental health resources for patients. We found that eHealth services provide effective, scalable, and cost-efficient options for the treatment of people with limited or no access to mental health care. This review highlights innovative technologies spearheading the way to more effective and safer treatments. We identified artificially intelligent tools that relieve physicians from routine tasks, allowing them to focus on collaborative doctor-patient relationships. The transformation of traditional clinics into digital ones is outlined, and the challenges associated with the successful deployment of digitalization in psychiatry are highlighted.
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Affiliation(s)
- Carl B. Roth
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
| | - Andreas Papassotiropoulos
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
- Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, Birmannsgasse 8, CH-4055 Basel, Switzerland
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, CH-4055 Basel, Switzerland
- Biozentrum, Life Sciences Training Facility, University of Basel, Klingelbergstrasse 50/70, CH-4056 Basel, Switzerland
| | - Annette B. Brühl
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
| | - Undine E. Lang
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
| | - Christian G. Huber
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
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Cavicchioli M, Calesella F, Cazzetta S, Mariagrazia M, Ogliari A, Maffei C, Vai B. Investigating predictive factors of dialectical behavior therapy skills training efficacy for alcohol and concurrent substance use disorders: A machine learning study. Drug Alcohol Depend 2021; 224:108723. [PMID: 33965687 DOI: 10.1016/j.drugalcdep.2021.108723] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/22/2021] [Accepted: 03/17/2021] [Indexed: 01/06/2023]
Abstract
BACKGROUND Dialectical Behavior Therapy Skills Training (DBT-ST) as stand-alone treatment has demonstrated promising outcomes for the treatment of alcohol use disorder (AUD) and concurrent substance use disorders (SUDs). However, no studies have so far empirically investigated factors that might predict efficacy of this therapeutic model. METHODS 275 treatment-seeking individuals with AUD and other SUDs were consecutively admitted to a 3-month DBT-ST program (in- + outpatient; outpatient settings). The machine learning routine applied (i.e. penalized regression combined with a nested cross-validation procedure) was conducted in order to estimate predictive values of a wide panel of clinical variables in a single statistical framework on drop-out and substance-use behaviors, dealing with related multicollinearity, and eliminating redundant variables. RESULTS The cross-validated elastic net model significantly predicted the drop-out. The bootstrap analysis revealed that subjects who showed substance-use behaviors during the intervention and who were treated with the mixed setting (i.e., in- and outpatient) program, together with higher ASI alcohol scores were associated with an higher probability of drop-out. On the contrary, older subjects, higher levels of education, together with higher scores of DERS awareness subscale were negatively associated to drop-out. Similarly, lifetime co-diagnoses of anxiety, bipolar, and gambling disorders, together with bulimia nervosa negatively predicted the drop-out. The machine learning model did not identify predictive variables of substance-use behaviors during the treatment. CONCLUSIONS The DBT-ST program could be considered a valid therapeutic approach especially when AUD and other SUDs co-occur with other psychiatric conditions and, it is carried out as a full outpatient intervention.
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Affiliation(s)
- Marco Cavicchioli
- Department of Psychology, University "Vita-Salute San Raffaele", Via Stamira d'Ancona, 20127, Milan, Italy; Unit of Clinical Psychology and Psychotherapy, San Raffaele-Turro Hospital, Via Stamira d'Ancona, 20127, Milan, Italy.
| | - Federico Calesella
- Department of Psychology, University "Vita-Salute San Raffaele", Via Stamira d'Ancona, 20127, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Silvia Cazzetta
- Department of Psychology, University "Vita-Salute San Raffaele", Via Stamira d'Ancona, 20127, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Movalli Mariagrazia
- Department of Psychology, University "Vita-Salute San Raffaele", Via Stamira d'Ancona, 20127, Milan, Italy; Unit of Clinical Psychology and Psychotherapy, San Raffaele-Turro Hospital, Via Stamira d'Ancona, 20127, Milan, Italy
| | - Anna Ogliari
- Department of Psychology, University "Vita-Salute San Raffaele", Via Stamira d'Ancona, 20127, Milan, Italy; Child in Mind Lab, University "Vita-Salute San Raffaele", Via Stamira d'Ancona, 20127, Milan, Italy
| | - Cesare Maffei
- Department of Psychology, University "Vita-Salute San Raffaele", Via Stamira d'Ancona, 20127, Milan, Italy; Unit of Clinical Psychology and Psychotherapy, San Raffaele-Turro Hospital, Via Stamira d'Ancona, 20127, Milan, Italy
| | - Benedetta Vai
- Department of Psychology, University "Vita-Salute San Raffaele", Via Stamira d'Ancona, 20127, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy; Fondazione Centro San Raffaele, Via Olgettina, 60, 20132 Milan, Italy
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Abo-Tabik M, Benn Y, Costen N. Are Machine Learning Methods the Future for Smoking Cessation Apps? SENSORS 2021; 21:s21134254. [PMID: 34206167 PMCID: PMC8271573 DOI: 10.3390/s21134254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/07/2021] [Accepted: 06/16/2021] [Indexed: 11/16/2022]
Abstract
Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.
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Affiliation(s)
- Maryam Abo-Tabik
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK;
| | - Yael Benn
- Department of Psychology, Manchester Metropolitan University, Manchester M15 6GX, UK
- Correspondence: (Y.B.); (N.C.)
| | - Nicholas Costen
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK;
- Correspondence: (Y.B.); (N.C.)
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A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features. Brain Sci 2021; 11:brainsci11060809. [PMID: 34207169 PMCID: PMC8234239 DOI: 10.3390/brainsci11060809] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/11/2021] [Accepted: 06/16/2021] [Indexed: 11/17/2022] Open
Abstract
Betel quid (BQ) is one of the most commonly used psychoactive substances in some parts of Asia and the Pacific. Although some studies have shown brain function alterations in BQ chewers, it is virtually impossible for radiologists’ to visually distinguish MRI maps of BQ chewers from others. In this study, we aimed to construct autoencoder and machine-learning models to discover brain alterations in BQ chewers based on the features of resting-state functional magnetic resonance imaging. Resting-state functional magnetic resonance imaging (rs-fMRI) was obtained from 16 BQ chewers, 15 tobacco- and alcohol-user controls (TA), and 17 healthy controls (HC). We used an autoencoder and machine learning model to identify BQ chewers among the three groups. A convolutional neural network (CNN)-based autoencoder model and supervised machine learning algorithm logistic regression (LR) were used to discriminate BQ chewers from TA and HC. Classifying the brain MRIs of HC, TA controls, and BQ chewers by conducting leave-one-out-cross-validation (LOOCV) resulted in the highest accuracy of 83%, which was attained by LR with two rs-fMRI feature sets. In our research, we constructed an autoencoder and machine-learning model that was able to identify BQ chewers from among TA controls and HC, which were based on data from rs-fMRI, and this might provide a helpful approach for tracking BQ chewers in the future.
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Cassú-Ponsatí D, Pedrero-Pérez EJ, Morales-Alonso S, Ruiz-Sánchez de León JM. Impulsivity-Compulsivity Axis: Evidence of Its Clinical Validity to Individually Classify Subjects on the Use/Abuse of Information and Communication Technologies. Front Psychol 2021; 12:647682. [PMID: 33889117 PMCID: PMC8056074 DOI: 10.3389/fpsyg.2021.647682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/08/2021] [Indexed: 11/13/2022] Open
Abstract
The compulsive habit model proposed by Everitt and Robbins has accumulated important empirical evidence. One of their proposals is the existence of an axis, on which each a person with a particular addiction can be located depending on the evolutionary moment of his/her addictive process. The objective of the present study is to contribute in addressing the identification of such axis, as few studies related to it have been published to date. To do so, the use/abuse of Information and Communication Technologies (ICT) was quantified on an initial sample of 807 subjects. Questionnaires were also delivered to measure impulsivity, compulsivity and symptoms of prefrontal dysfunction. Evidence of the existence of the proposed axis was obtained by means of Machine Learning techniques, thus allowing the classification of each subject along the continuum. The present study provides preliminary evidence of the existence of the Impulsivity-Compulsivity axis, as well as an IT tool so that each patient that starts getting treatment for an addiction can be statistically classified as “impulsive” or “compulsive.” This would allow the matching of each person with the most appropriate treatment depending on his/her moment in the addiction/abuse process, thus facilitating the individualized design of each therapeutic process and a possible improvement of the results of the treatment.
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Affiliation(s)
| | | | - Sara Morales-Alonso
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Faculty of Psychology, Complutense University of Madrid, Madrid, Spain
| | - José María Ruiz-Sánchez de León
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Faculty of Psychology, Complutense University of Madrid, Madrid, Spain
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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.
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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.
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40
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Fu R, Mitsakakis N, Chaiton M. A machine learning approach to identify correlates of current e-cigarette use in Canada. EXPLORATION OF MEDICINE 2021. [DOI: 10.37349/emed.2021.00033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Aim: Popularity of electronic cigarettes (i.e. e-cigarettes) is soaring in Canada. Understanding person-level correlates of current e-cigarette use (vaping) is crucial to guide tobacco policy, but prior studies have not fully identified these correlates due to model overfitting caused by multicollinearity. This study addressed this issue by using classification tree, a machine learning algorithm.
Methods: This population-based cross-sectional study used the Canadian Tobacco, Alcohol, and Drugs Survey (CTADS) from 2017 that targeted residents aged 15 or older. Forty-six person-level characteristics were first screened in a logistic mixed-effects regression procedure for their strength in predicting vaper type (current vs. former vaper) among people who reported to have ever vaped. A 9:1 ratio was used to randomly split the data into a training set and a validation set. A classification tree model was developed using the cross-validation method on the training set using the selected predictors and assessed on the validation set using sensitivity, specificity and accuracy.
Results: Of the 3,059 people with an experience of vaping, the average age was 24.4 years (standard deviation = 11.0), with 41.9% of them being female and 8.5% of them being aboriginal. There were 556 (18.2%) current vapers. The classification tree model performed relatively well and suggested attraction to e-cigarette flavors was the most important correlate of current vaping, followed by young age (< 18) and believing vaping to be less harmful to oneself than cigarette smoking.
Conclusions: People who vape due to flavors are associated with very high risk of becoming current vapers. The findings of this study provide evidence that supports the ongoing ban on flavored vaping products in the US and suggests a similar regulatory intervention may be effective in Canada.
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Affiliation(s)
- Rui Fu
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T3M6, Canada 2Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T3M6, Canada 3Institute for Population Mental Health Research, Centre for Addiction and Mental Health, Toronto, ON M5T1R8, Canada
| | - Nicholas Mitsakakis
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T3M6, Canada 4The Toronto Health Economics and Technology Assessment Collaborative, Toronto General Hospital, Toronto, ON M5G2C4, Canada
| | - Michael Chaiton
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T3M6, Canada 2Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T3M6, Canada 3Institute for Population Mental Health Research, Centre for Addiction and Mental Health, Toronto, ON M5T1R8, Canada
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A Decision Support System for the Prediction of Drug Predisposition Through Personality Traits. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1338:39-46. [DOI: 10.1007/978-3-030-78775-2_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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42
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Popovic D, Schiltz K, Falkai P, Koutsouleris N. Präzisionspsychiatrie und der Beitrag von Brain Imaging und anderen Biomarkern. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2020; 88:778-785. [PMID: 33307561 DOI: 10.1055/a-1300-2162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
'Precision Psychiatry' as the psychiatric variant of 'Precision Medicine' aims to provide high-level diagnosis and treatment based on robust biomarkers and tailored to the individual clinical, neurobiological, and genetic constitution of the patient. The specific peculiarity of psychiatry, in which disease entities are normatively defined based on clinical experience and are also significantly influenced by contemporary history, society and philosophy, has so far made the search for valid and reliable psychobiological connections difficult. Nevertheless, considerable progress has now been made in all areas of psychiatric research, made possible above all by the critical review and renewal of previous concepts of disease and psychopathology, the increased orientation towards neurobiology and genetics, and in particular the use of machine learning methods. Notably, modern machine learning methods make it possible to integrate high-dimensional and multimodal data sets and generate models which provide new psychobiological insights and offer the possibility of individualized, biomarker-driven single-subject prediction of diagnosis, therapy response and prognosis. The aim of the present review is therefore to introduce the concept of 'Precision Psychiatry' to the interested reader, to concisely present modern, machine learning methods required for this, and to clearly present the current state and future of biomarker-based 'precision psychiatry'.
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Affiliation(s)
- David Popovic
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie.,International Max Planck Research School for Translational Psychiatry
| | - Kolja Schiltz
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie
| | - Peter Falkai
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie.,International Max Planck Research School for Translational Psychiatry
| | - Nikolaos Koutsouleris
- Klinikum der Universität München, Klinik und Poliklinik für Psychiatrie und Psychotherapie.,International Max Planck Research School for Translational Psychiatry
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Symons M, Feeney GFX, Gallagher MR, Young RM, Connor JP. Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus 'trained' machine learning models. Addiction 2020; 115:2164-2175. [PMID: 32150316 DOI: 10.1111/add.15038] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/13/2019] [Accepted: 03/04/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML algorithms demonstrated to be effective in predicting alcohol dependence outcomes, compared with clinical judgement and traditional linear regression. DESIGN Prospective study. ML models were trained on 1016 previously treated patients (training-set) who attended a hospital-based alcohol and drug clinic. ML models (n = 27), clinical psychologists (n = 10) and a 'traditional' logistic regression model (n = 1) predicted treatment outcome during the initial treatment session of an alcohol dependence programme. SETTING A 12-week cognitive behavioural therapy (CBT)-based abstinence programme for alcohol dependence in a hospital-based alcohol and drug clinic in Australia. PARTICIPANTS Prospective predictions were made for 220 new patients (test-set; 70.91% male, mean age = 35.78 years, standard deviation = 9.19). Sixty-nine (31.36%) patients successfully completed treatment. MEASUREMENTS Treatment success was the primary outcome variable. The cross-validated training-set accuracy of ML models was used to determine optimal parameters for selecting models for prospective prediction. Accuracy, sensitivity, specificity, area under the receiver operator curve (AUC), Brier score and calibration curves were calculated and compared across predictions. FINDINGS The mean aggregate accuracy of the ML models (63.06%) was higher than the mean accuracy of psychologist predictions (56.36%). The most accurate ML model achieved 70% accuracy, as did logistic regression. Both were more accurate than psychologists (P < 0.05) and had superior calibration. The high specificity for the selected ML (79%) and logistic regression (90%) meant they were significantly (P < 0.001) more effective than psychologists (50%) at correctly identifying patients whose treatment was unsuccessful. For ML and logistic regression, high specificity came at the expense of sensitivity (26 and 31%, respectively), resulting in poor prediction of successful patients. CONCLUSIONS Machine learning models and logistic regression appear to be more accurate than psychologists at predicting treatment outcomes in an abstinence programme for alcohol dependence, but sensitivity is low.
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Affiliation(s)
- Martyn Symons
- Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.,Discipline of Psychiatry, The University of Queensland, Brisbane, Australia.,National Health and Medical Research Council FASD Research Australia Centre of Research Excellence, Telethon Kids Institute, The University of Western Australia, Perth, Australia
| | - Gerald F X Feeney
- Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.,Centre for Youth Substance Abuse Research, The University of Queensland, Brisbane, Australia
| | - Marcus R Gallagher
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Ross McD Young
- Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.,Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Jason P Connor
- Alcohol and Drug Assessment Unit, Princess Alexandra Hospital, Brisbane, Australia.,Discipline of Psychiatry, The University of Queensland, Brisbane, Australia.,Centre for Youth Substance Abuse Research, The University of Queensland, Brisbane, Australia
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Seo W, Kim N, Lee SK, Park SM. Machine learning-based analysis of adolescent gambling factors. J Behav Addict 2020; 9:734-743. [PMID: 33011712 PMCID: PMC8943669 DOI: 10.1556/2006.2020.00063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 04/12/2020] [Accepted: 08/26/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND AND AIMS Problem gambling among adolescents has recently attracted attention because of easy access to gambling in online environments and its serious effects on adolescent lives. We proposed a machine learning-based analysis method for predicting the degree of problem gambling. METHODS Of the 17,520 respondents in the 2018 National Survey on Youth Gambling Problems dataset (collected by the Korea Center on Gambling Problems), 5,045 students who had gambled in the past 3 months were included in this study. The Gambling Problem Severity Scale was used to provide the binary label information. After the random forest-based feature selection method, we trained four models: random forest (RF), support vector machine (SVM), extra trees (ETs), and ridge regression. RESULTS The online gambling behavior in the past 3 months, experience of winning money or goods, and gambling of personal relationship were three factors exhibiting the high feature importance. All four models demonstrated an area under the curve (AUC) of >0.7; ET showed the highest AUC (0.755), RF demonstrated the highest accuracy (71.8%), and SVM showed the highest F1 score (0.507) on a testing set. DISCUSSION The results indicate that machine learning models can convey meaningful information to support predictions regarding the degree of problem gambling. CONCLUSION Machine learning models trained using important features showed moderate accuracy in a large-scale Korean adolescent dataset. These findings suggest that the method will help screen adolescents at risk of problem gambling. We believe that expandable machine learning-based approaches will become more powerful as more datasets are collected.
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Affiliation(s)
- Wonju Seo
- Department of Creative IT Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, 37673, Republic of Korea
| | - Namho Kim
- Department of Creative IT Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, 37673, Republic of Korea
| | - Sang-Kyu Lee
- Department of Psychology, College of Medicine, Hallym University, 1 Hallymdaehak-gil, Chuncheon, 24252, Republic of Korea,Corresponding author. Tel.: +82 10 7208 7740. E-mail:
| | - Sung-Min Park
- Department of Creative IT Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, 37673, Republic of Korea,Corresponding author. Tel.: +82 10 7208 7740. E-mail:
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Bickman L. Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health. ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH 2020; 47:795-843. [PMID: 32715427 PMCID: PMC7382706 DOI: 10.1007/s10488-020-01065-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.
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Affiliation(s)
- Leonard Bickman
- Center for Children and Families; Psychology, Academic Health Center 1, Florida International University, 11200 Southwest 8th Street, Room 140, Miami, FL, 33199, USA.
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Lindner P, Johansson M, Gajecki M, Berman AH. Using alcohol consumption diary data from an internet intervention for outcome and predictive modeling: a validation and machine learning study. BMC Med Res Methodol 2020; 20:111. [PMID: 32393245 PMCID: PMC7212621 DOI: 10.1186/s12874-020-00995-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 04/27/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Alcohol use disorder (AUD) is highly prevalent and presents a large treatment gap. Self-help internet interventions are an attractive approach to lowering thresholds for seeking help and disseminating evidence-based programs at scale. Internet interventions for AUD however suffer from high attrition and since continuous outcome measurements are uncommon, little is known about trajectories and processes. The current study investigates whether data from a non-mandatory alcohol consumption diary, common in internet interventions for AUD, approximates drinks reported at follow-up, and whether data from the first half of the intervention predict treatment success. METHODS N = 607 participants enrolled in a trial of online self-help for AUD, made an entry in the non-mandatory consumption diary (total of 9117 entries), and completed the follow-up assessment. Using multiple regression and a subset of calendar data overlapping with the follow-up, scaling factors were derived to account for missing entries per participant and week. Generalized estimating equations with an inverse time predictor were then used to calculate point-estimates of drinks per week at follow-up, the confidence intervals of which were compared to that from the measurement at follow-up. Next, calendar data form the first half of the intervention were retained and summary functions used to create 18 predictors for random forest machine learning models, the classification accuracies of which were ultimately estimated using nested cross-validation. RESULTS While the raw calendar data substantially underestimated drinks reported at follow-up, the confidence interval of the trajectory-derived point-estimate from the adjusted data overlapped with the confidence interval of drinks reported at follow-up. Machine learning models achieved prediction accuracies of 64% (predicting non-hazardous drinking) and 48% (predicting AUD severity decrease), in both cases with higher sensitivity than specificity. CONCLUSIONS Data from a non-mandatory alcohol consumption diary, adjusted for missing entries, approximates follow-up data at a group level, suggesting that such data can be used to reveal trajectories and processes during treatment and possibly be used to impute missing follow-up data. At an individual level, however, calendar data from the first half of the intervention did not have high predictive accuracy, presumable due to a high rate of missing data and unclear missing mechanisms.
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Affiliation(s)
- Philip Lindner
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden.
- Center for Dependency Disorders, Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden.
- Department of Psychology, Stockholm University, Stockholm, Sweden.
| | - Magnus Johansson
- Center for Dependency Disorders, Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- Department of Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Gajecki
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- Center for Dependency Disorders, Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Anne H Berman
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- Center for Dependency Disorders, Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- Department of Psychology, Uppsala University, Uppsala, Sweden
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Lin E, Lin CH, Lane HY. Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches. Int J Mol Sci 2020; 21:ijms21030969. [PMID: 32024055 PMCID: PMC7037937 DOI: 10.3390/ijms21030969] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 01/25/2020] [Accepted: 01/30/2020] [Indexed: 12/22/2022] Open
Abstract
A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
- Correspondence: (C.-H.L.); (H.-Y.L.)
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40402, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40402, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
- Correspondence: (C.-H.L.); (H.-Y.L.)
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Popova O, Popov B, Karandey V, Gerashchenko A. Entropy and Algorithm of Obtaining Decision Trees in a Way Approximated to the Natural Intelligence. INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2019. [DOI: 10.4018/ijcini.2019070104] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The classification of knowledge of a specified subject area is an actual task. The well-known methods of obtaining decision trees using entropy are not suitable for the classification of the subject area knowledge. So, a new algorithm of obtaining decision trees, whose way of obtaining is approximated to the natural intelligence, is suggested in the article. Here, the knowledge of a subject area is presented as a complex of answers to questions, which help to find the solution to a current task. The connection of entropy with the appearance of knowledge, the classification of previous knowledge, and the definitions used in decision trees are also analyzed in the article. The latter is necessary to compare the suggested algorithm approximated to the natural intelligence with the traditional method, using a small example. The article contains the analysis of solving a classification task for such a subject area as optimization methods.
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Affiliation(s)
- Olga Popova
- Kuban State Technological University, Krasnodar, Russia
| | - Boris Popov
- Kuban State Technological University, Krasnodar, Russia
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An Ensemble Classifier with Case-Based Reasoning System for Identifying Internet Addiction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16071233. [PMID: 30959905 PMCID: PMC6479715 DOI: 10.3390/ijerph16071233] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 03/29/2019] [Accepted: 04/04/2019] [Indexed: 12/04/2022]
Abstract
Internet usage has increased dramatically in recent decades. With this growing usage trend, the negative impacts of Internet usage have also increased significantly. One recurring concern involves users with Internet addiction, whose Internet usage has become excessive and disrupted their lives. In order to detect users with Internet addiction and disabuse their inappropriate behavior early, a secure Web service-based EMBAR (ensemble classifier with case-based reasoning) system is proposed in this study. The EMBAR system monitors users in the background and can be used for Internet usage monitoring in the future. Empirical results demonstrate that our proposed ensemble classifier with case-based reasoning (CBR) in the proposed EMBAR system for identifying users with potential Internet addiction offers better performance than other classifiers.
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