1
|
Yu H, Kotlyar M, Thuras P, Dufresne S, Pakhomov SV. Towards Predicting Smoking Events for Just-in-time Interventions. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:468-477. [PMID: 38827079 PMCID: PMC11141818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Consumer-grade heart rate (HR) sensors are widely used for tracking physical and mental health status. We explore the feasibility of using Polar H10 electrocardiogram (ECG) sensor to detect and predict cigarette smoking events in naturalistic settings with several machine learning approaches. We have collected and analyzed data for 28 participants observed over a two-week period. We found that using bidirectional long short-term memory (BiLSTM) with ECG-derived and GPS location input features yielded the highest mean accuracy of 69% for smoking event detection. For predicting smoking events, the highest accuracy of 67% was achieved using the fine-tuned LSTM approach. We also found a significant correlation between accuracy and the number of smoking events available from each participant. Our findings indicate that both detection and prediction of smoking events are feasible but require an individualized approach to training the models, particularly for prediction.
Collapse
Affiliation(s)
- Hang Yu
- University of Minnesota, Minneapolis, MN, United States
| | | | - Paul Thuras
- University of Minnesota, Minneapolis, MN, United States
| | | | | |
Collapse
|
2
|
Wu T, Sherman G, Giorgi S, Thanneeru P, Ungar LH, Kamath PS, Simonetto DA, Curtis BL, Shah VH. Smartphone sensor data estimate alcohol craving in a cohort of patients with alcohol-associated liver disease and alcohol use disorder. Hepatol Commun 2023; 7:e0329. [PMID: 38055637 PMCID: PMC10984664 DOI: 10.1097/hc9.0000000000000329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 09/22/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Sensors within smartphones, such as accelerometer and location, can describe longitudinal markers of behavior as represented through devices in a method called digital phenotyping. This study aimed to assess the feasibility of digital phenotyping for patients with alcohol-associated liver disease and alcohol use disorder, determine correlations between smartphone data and alcohol craving, and establish power assessment for future studies to prognosticate clinical outcomes. METHODS A total of 24 individuals with alcohol-associated liver disease and alcohol use disorder were instructed to download the AWARE application to collect continuous sensor data and complete daily ecological momentary assessments on alcohol craving and mood for up to 30 days. Data from sensor streams were processed into features like accelerometer magnitude, number of calls, and location entropy, which were used for statistical analysis. We used repeated measures correlation for longitudinal data to evaluate associations between sensors and ecological momentary assessments and standard Pearson correlation to evaluate within-individual relationships between sensors and craving. RESULTS Alcohol craving significantly correlated with mood obtained from ecological momentary assessments. Across all sensors, features associated with craving were also significantly correlated with all moods (eg, loneliness and stress) except boredom. Individual-level analysis revealed significant relationships between craving and features of location entropy and average accelerometer magnitude. CONCLUSIONS Smartphone sensors may serve as markers for alcohol craving and mood in alcohol-associated liver disease and alcohol use disorder. Findings suggest that location-based and accelerometer-based features may be associated with alcohol craving. However, data missingness and low participant retention remain challenges. Future studies are needed for further digital phenotyping of relapse risk and progression of liver disease.
Collapse
Affiliation(s)
- Tiffany Wu
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Garrick Sherman
- National Institute on Drug Abuse Intramural Research Program, National Institute of Health Baltimore, Maryland, USA
| | - Salvatore Giorgi
- National Institute on Drug Abuse Intramural Research Program, National Institute of Health Baltimore, Maryland, USA
| | - Priya Thanneeru
- Department of Medicine and Pediatrics, The Brooklyn Hospital Center, Brooklyn, New York, USA
| | - Lyle H. Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Patrick S. Kamath
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A. Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Brenda L. Curtis
- National Institute on Drug Abuse Intramural Research Program, National Institute of Health Baltimore, Maryland, USA
| | - Vijay H. Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
3
|
Perski O, Li K, Pontikos N, Simons D, Goldstein SP, Naughton F, Brown J. Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App. Nicotine Tob Res 2023; 25:1330-1339. [PMID: 36971111 PMCID: PMC10256890 DOI: 10.1093/ntr/ntad051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023]
Abstract
INTRODUCTION Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. AIMS AND METHODS We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. RESULTS Participants (N = 791) provided 37 002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC = 0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000). CONCLUSIONS Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual's dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual's data, had improved performance but could only be constructed for a minority of participants. IMPLICATIONS This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data.
Collapse
Affiliation(s)
- Olga Perski
- Department of Behavioural Science and Health, University College London, London, UK
- SPECTRUM Consortium, London, UK
| | - Kezhi Li
- Institute of Health Informatics, University College London, London, UK
| | - Nikolas Pontikos
- UCL Institute of Ophthalmology, University College London, London, UK
| | - David Simons
- Centre for Emerging, Endemic and Exotic Diseases, Royal Veterinary College, London, UK
| | - Stephanie P Goldstein
- Weight Control and Diabetes Research Center, The Miriam Hospital, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
| | - Felix Naughton
- Behavioural and Implementation Science Research Group, School of Health Sciences, University of East Anglia, Norwich, UK
| | - Jamie Brown
- Department of Behavioural Science and Health, University College London, London, UK
- SPECTRUM Consortium, London, UK
| |
Collapse
|
4
|
Bickel WK, Tomlinson DC, Craft WH, Ma M, Dwyer CL, Yeh YH, Tegge AN, Freitas-Lemos R, Athamneh LN. Predictors of smoking cessation outcomes identified by machine learning: A systematic review. ADDICTION NEUROSCIENCE 2023; 6:100068. [PMID: 37214256 PMCID: PMC10194042 DOI: 10.1016/j.addicn.2023.100068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
This systematic review aims to characterize the utility of machine learning to identify the predictors of smoking cessation outcomes and identify the machine learning methods applied in this area. In the current study, multiple searches occurred through December 9, 2022 in MEDLINE, Science Citation Index, Social Science Citation Index, EMBASE, CINAHL Plus, APA PsycINFO, PubMed, Cochrane Central Register of Controlled Trials, and the IEEE Xplore were performed. Inclusion criteria included various machine learning techniques, studies reporting cigarette smoking cessation outcomes (smoking status and the number of cigarettes), and various experimental designs (e.g., cross-sectional and longitudinal). Predictors of smoking cessation outcomes were assessed, including behavioral markers, biomarkers, and other predictors. Our systematic review identified 12 papers fitting our inclusion criteria. In this review, we identified gaps in knowledge and innovation opportunities for machine learning research in the field of smoking cessation.
Collapse
Affiliation(s)
- Warren K. Bickel
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
| | - Devin C. Tomlinson
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
- Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, USA
| | - William H. Craft
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
- Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, USA
| | - Manxiu Ma
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
| | - Candice L. Dwyer
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
- Department of Psychology, Virginia Tech, Blacksburg, VA, USA
| | - Yu-Hua Yeh
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
| | - Allison N. Tegge
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
- Department of Statistics, Virginia Tech, Blacksburg, VA, USA
| | | | - Liqa N. Athamneh
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
| |
Collapse
|
5
|
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.
Collapse
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.
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Ferreira MIASN, Barbieri FA, Moreno VC, Penedo T, Tavares JMRS. Machine learning models for Parkinson's disease detection and stage classification based on spatial-temporal gait parameters. Gait Posture 2022; 98:49-55. [PMID: 36049418 DOI: 10.1016/j.gaitpost.2022.08.014] [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: 02/08/2022] [Revised: 08/16/2022] [Accepted: 08/18/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up. RESEARCH QUESTION This article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry. METHODS Gait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments. RESULTS In the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability. SIGNIFICANCE The results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.
Collapse
Affiliation(s)
| | | | - Vinícius Christianini Moreno
- São Paulo State University (Unesp), Department of Physical Education, Human Movement Research Laboratory (MOVI-LAB), Bauru, Brazil
| | - Tiago Penedo
- São Paulo State University (Unesp), Department of Physical Education, Human Movement Research Laboratory (MOVI-LAB), Bauru, Brazil
| | - João Manuel R S Tavares
- Faculdade de Engenharia, Universidade do Porto, Portugal; Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Portugal
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models. PLoS One 2022; 17:e0266752. [PMID: 35544468 PMCID: PMC9094505 DOI: 10.1371/journal.pone.0266752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 03/27/2022] [Indexed: 11/22/2022] Open
Abstract
To increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using standard regression approaches. The outcome is modeled as a linear function of the precision variables and confounders; however, for many complex phenotypes, the assumptions of the linear regression models are not always met. As an alternative, we used neural networks for the modeling of complex phenotypes and covariate adjustments. We compared the prediction accuracy of the neural network models to that of classical approaches based on linear regression. Using data from the UK Biobank, COPDGene study, and Childhood Asthma Management Program (CAMP), we examined the features of neural networks in this context and compared them with traditional regression approaches for prediction of three outcomes: forced expiratory volume in one second (FEV1), age at smoking cessation, and log transformation of age at smoking cessation (due to age at smoking cessation being right-skewed). We used mean squared error to compare neural network and regression models, and found the models performed similarly unless the observed distribution of the phenotype was skewed, in which case the neural network had smaller mean squared error. Our results suggest neural network models have an advantage over standard regression approaches when the phenotypic distribution is skewed. However, when the distribution is not skewed, the approaches performed similarly. Our findings are relevant to studies that analyze phenotypes that are skewed by nature or where the phenotype of interest is skewed as a result of the ascertainment condition.
Collapse
|
10
|
Hojjatinia S, Daly ER, Hnat T, Hossain SM, Kumar S, Lagoa CM, Nahum-Shani I, Samiei SA, Spring B, Conroy DE. Dynamic models of stress-smoking responses based on high-frequency sensor data. NPJ Digit Med 2021; 4:162. [PMID: 34815538 PMCID: PMC8611062 DOI: 10.1038/s41746-021-00532-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/26/2021] [Indexed: 11/09/2022] Open
Abstract
Self-reports indicate that stress increases the risk for smoking; however, intensive data from sensors can provide a more nuanced understanding of stress in the moments leading up to and following smoking events. Identifying personalized dynamical models of stress-smoking responses can improve characterizations of smoking responses following stress, but techniques used to identify these models require intensive longitudinal data. This study leveraged advances in wearable sensing technology and digital markers of stress and smoking to identify person-specific models of stress and smoking system dynamics by considering stress immediately before, during, and after smoking events. Adult smokers (n = 45) wore the AutoSense chestband (respiration-inductive plethysmograph, electrocardiogram, accelerometer) with MotionSense (accelerometers, gyroscopes) on each wrist for three days prior to a quit attempt. The odds of minute-level smoking events were regressed on minute-level stress probabilities to identify person-specific dynamic models of smoking responses to stress. Simulated pulse responses to a continuous stress episode revealed a consistent pattern of increased odds of smoking either shortly after the beginning of the simulated stress episode or with a delay, for all participants. This pattern is followed by a dramatic reduction in the probability of smoking thereafter, for about half of the participants (49%). Sensor-detected stress probabilities indicate a vulnerability for smoking that may be used as a tailoring variable for just-in-time interventions to support quit attempts.
Collapse
Affiliation(s)
- Sahar Hojjatinia
- School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Elyse R Daly
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Timothy Hnat
- Department of Computer Science, University of Memphis, Memphis, TN, 38152, USA
| | | | - Santosh Kumar
- Department of Computer Science, University of Memphis, Memphis, TN, 38152, USA
| | - Constantino M Lagoa
- School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, 48106, USA
| | - Shahin Alan Samiei
- Department of Computer Science, University of Memphis, Memphis, TN, 38152, USA
| | - Bonnie Spring
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - David E Conroy
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Department of Kinesiology, The Pennsylvania State University, University Park, PA, 16802, USA.
| |
Collapse
|
11
|
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.
Collapse
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.)
| |
Collapse
|
12
|
Han DH, Lee SH, Lee S, Seo DC. Identifying emerging predictors for adolescent electronic nicotine delivery systems use: A machine learning analysis of the Population Assessment of Tobacco and Health Study. Prev Med 2021; 145:106418. [PMID: 33422574 DOI: 10.1016/j.ypmed.2021.106418] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 12/30/2020] [Accepted: 01/05/2021] [Indexed: 11/28/2022]
Abstract
Intervention strategies to prevent adolescents from using electronic nicotine delivery systems (ENDS) should be based on robust predictors of ENDS use that may differ from predictors of conventional cigarette use. Literature points to the need for uncovering emerging predictors of ENDS use. This study identified emerging predictors of adolescent ENDS use using machine learning (ML) techniques. We analyzed nationally representative multi-wave longitudinal survey data (2013-2018) drawn from the Population Assessment of Tobacco and Health Study. A sample of adolescents (12-17 years) who never used any tobacco products at baseline and completed Wave 2 (n = 7958), Wave 3 (n = 6260) and Wave 4 (n = 4544) were analyzed. We developed a supervised ML prediction model using the penalized logistic regression to assess self-reported past-month ENDS use (i.e., current use) at Waves 2-4 based on the variables measured at the previous wave. We then extracted important predictors from each model. The penalized logistic regression models showed suitable capability to discriminate between ENDS uses and non-uses at each wave based on the area under the receiver operating characteristic curve and the area under the precision-recall curve. Interestingly, social media use emerged as an important variable in predicting adolescent ENDS use. ML models appear to be a promising method to identify unique population-level predictors for U.S. adolescent ENDS use behaviors. More research is warranted to investigate emerging predictors of ENDS use and experimentally examine the mechanism by which these emerging predictors affect ENDS use behavior across different spectrum of populations.
Collapse
Affiliation(s)
- Dae-Hee Han
- Indiana University School of Public Health, Bloomington, IN, USA
| | - Shin Hyung Lee
- Indiana University School of Public Health, Bloomington, IN, USA
| | - Shieun Lee
- Indiana University School of Public Health, Bloomington, IN, USA
| | - Dong-Chul Seo
- Indiana University School of Public Health, Bloomington, IN, USA.
| |
Collapse
|
13
|
Cho PJ, Singh K, Dunn J. Roles of artificial intelligence in wellness, healthy living, and healthy status sensing. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00009-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
14
|
Epstein DH, Tyburski M, Kowalczyk WJ, Burgess-Hull AJ, Phillips KA, Curtis BL, Preston KL. Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data. NPJ Digit Med 2020; 3:26. [PMID: 32195362 PMCID: PMC7055250 DOI: 10.1038/s41746-020-0234-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 02/06/2020] [Indexed: 12/16/2022] Open
Abstract
Just-in-time adaptive interventions (JITAIs), typically smartphone apps, learn to deliver therapeutic content when users need it. The challenge is to "push" content at algorithmically chosen moments without making users trigger it with effortful input. We trained a randomForest algorithm to predict heroin craving, cocaine craving, or stress (reported via smartphone app 3x/day) 90 min into the future, using 16 weeks of field data from 189 outpatients being treated for opioid-use disorder. We used only one form of continuous input (along with person-level demographic data), collected passively: an indicator of environmental exposures along the past 5 h of movement, as assessed by GPS. Our models achieved excellent overall accuracy-as high as 0.93 by the end of 16 weeks of tailoring-but this was driven mostly by correct predictions of absence. For predictions of presence, "believability" (positive predictive value, PPV) usually peaked in the high 0.70s toward the end of the 16 weeks. When the prediction target was more rare, PPV was lower. Our findings complement those of other investigators who use machine learning with more broadly based "digital phenotyping" inputs to predict or detect mental and behavioral events. When target events are comparatively subtle, like stress or drug craving, accurate detection or prediction probably needs effortful input from users, not passive monitoring alone. We discuss ways in which accuracy is difficult to achieve or even assess, and warn that high overall accuracy (including high specificity) can mask the abundance of false alarms that low PPV reveals.
Collapse
Affiliation(s)
- David H. Epstein
- Intramural Research Program, National Institute on Drug Abuse, 251 Bayview Blvd., Suite 200, Baltimore, MD 21224 USA
| | - Matthew Tyburski
- Intramural Research Program, National Institute on Drug Abuse, 251 Bayview Blvd., Suite 200, Baltimore, MD 21224 USA
| | - William J. Kowalczyk
- Intramural Research Program, National Institute on Drug Abuse, 251 Bayview Blvd., Suite 200, Baltimore, MD 21224 USA
| | - Albert J. Burgess-Hull
- Intramural Research Program, National Institute on Drug Abuse, 251 Bayview Blvd., Suite 200, Baltimore, MD 21224 USA
| | - Karran A. Phillips
- Intramural Research Program, National Institute on Drug Abuse, 251 Bayview Blvd., Suite 200, Baltimore, MD 21224 USA
| | - Brenda L. Curtis
- Intramural Research Program, National Institute on Drug Abuse, 251 Bayview Blvd., Suite 200, Baltimore, MD 21224 USA
| | - Kenzie L. Preston
- Intramural Research Program, National Institute on Drug Abuse, 251 Bayview Blvd., Suite 200, Baltimore, MD 21224 USA
| |
Collapse
|
15
|
Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events. SENSORS 2020; 20:s20041099. [PMID: 32079359 PMCID: PMC7070428 DOI: 10.3390/s20041099] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 02/10/2020] [Accepted: 02/13/2020] [Indexed: 11/17/2022]
Abstract
Nicotine consumption is considered a major health problem, where many of those who wish to quit smoking relapse. The problem is that overtime smoking as behaviour is changing into a habit, in which it is connected to internal (e.g., nicotine level, craving) and external (action, time, location) triggers. Smoking cessation apps have proved their efficiency to support smoking who wish to quit smoking. However, still, these applications suffer from several drawbacks, where they are highly relying on the user to initiate the intervention by submitting the factor the causes the urge to smoke. This research describes the creation of a combined Control Theory and deep learning model that can learn the smoker’s daily routine and predict smoking events. The model’s structure combines a Control Theory model of smoking with a 1D-CNN classifier to adapt to individual differences between smokers and predict smoking events based on motion and geolocation values collected using a mobile device. Data were collected from 5 participants in the UK, and analysed and tested on 3 different machine learning model (SVM, Decision tree, and 1D-CNN), 1D-CNN has proved it’s efficiency over the three methods with average overall accuracy 86.6%. The average MSE of forecasting the nicotine level was (0.04) in the weekdays, and (0.03) in the weekends. The model has proved its ability to predict the smoking event accurately when the participant is well engaged with the app.
Collapse
|
16
|
Suchting R, Hébert ET, Ma P, Kendzor DE, Businelle MS. Using Elastic Net Penalized Cox Proportional Hazards Regression to Identify Predictors of Imminent Smoking Lapse. Nicotine Tob Res 2020; 21:173-179. [PMID: 29059349 DOI: 10.1093/ntr/ntx201] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 09/05/2017] [Indexed: 11/14/2022]
Abstract
Introduction Machine learning algorithms such as elastic net regression and backward selection provide a unique and powerful approach to model building given a set of psychosocial predictors of smoking lapse measured repeatedly via ecological momentary assessment (EMA). Understanding these predictors may aid in developing interventions for smoking lapse prevention. Methods In a randomized-controlled smoking cessation trial, smartphone-based EMAs were collected from 92 participants following a scheduled quit date. This secondary analysis utilized elastic net-penalized cox proportional hazards regression and model approximation via backward elimination to (1) optimize a predictive model of time to first lapse and (2) simplify that model to its core constituent predictors to maximize parsimony and generalizability. Results Elastic net proportional hazards regression selected 17 of 26 possible predictors from 2065 EMAs to model time to first lapse. The predictors with the highest magnitude regression coefficients were having consumed alcohol in the past hour, being around and interacting with a smoker, and having cigarettes easily available. This model was reduced using backward elimination, retaining five predictors and approximating to 93.9% of model fit. The retained predictors included those mentioned above as well as feeling irritable and being in areas where smoking is either discouraged or allowed (as opposed to not permitted). Conclusions The strongest predictors of smoking lapse were environmental in nature (e.g., being in smoking-permitted areas) as opposed to internal factors such as psychological affect. Interventions may be improved by a renewed focus of interventions on these predictors. Implications The present study demonstrated the utility of machine learning algorithms to optimize the prediction of time to smoking lapse using EMA data. The two models generated by the present analysis found that environmental factors were most strongly related to smoking lapse. The results support the use of machine learning algorithms to investigate intensive longitudinal data, and provide a foundation for the development of highly tailored, just-in-time interventions that can target on multiple antecedents of smoking lapse.
Collapse
Affiliation(s)
- Robert Suchting
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX
| | - Emily T Hébert
- Oklahoma Tobacco Research Center, Stephenson Cancer Center, Oklahoma City, OK
| | - Ping Ma
- Division of Population Health, Children's Medical Center, Dallas, TX
| | - Darla E Kendzor
- Oklahoma Tobacco Research Center, Stephenson Cancer Center, Oklahoma City, OK.,Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK
| | - Michael S Businelle
- Oklahoma Tobacco Research Center, Stephenson Cancer Center, Oklahoma City, OK.,Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK
| |
Collapse
|
17
|
Mak KK, Lee K, Park C. Applications of machine learning in addiction studies: A systematic review. Psychiatry Res 2019; 275:53-60. [PMID: 30878857 DOI: 10.1016/j.psychres.2019.03.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 03/02/2019] [Accepted: 03/02/2019] [Indexed: 02/09/2023]
Abstract
This study aims to provide a systematic review of the applications of machine learning methods in addiction research. In this study, multiple searches on MEDLINE, Embase and the Cochrane Database of Systematic Reviews were performed. 23 full-text articles were assessed and 17 articles met the inclusion criteria for the final review. The selected studies covered mainly substance addiction (N = 14, 82.4%), including smoking (N = 4), alcohol drinking (N = 3), as well as uses of cocaine (N = 4), opioids (N = 1), and multiple substances (N = 2). Other studies were non-substance addiction (N = 3, 17.6%), including gambling (N = 2) and internet gaming (N = 1). There were eight cross-sectional, seven cohort, one non-randomized controlled, and one crossover trial studies. Majority of the studies employed supervised learning (N = 13), and others employed unsupervised learning (N = 2) and reinforcement learning (N = 2). Among the supervised learning studies, five studies used ensemble learning methods or multiple algorithm comparisons, six used regression, and two used classification. The two included reinforcement learning studies used the direct methods. These results suggest that machine learning methods, particularly supervised learning are increasingly used in addiction psychiatry for informing medical decisions.
Collapse
Affiliation(s)
- Kwok Kei Mak
- Department of Statistics, Keimyung University, Republic of Korea.
| | - Kounseok Lee
- Department of Psychiatry, Hanyang University Hospital, Hanyang University, Republic of Korea
| | - Cheolyong Park
- Department of Statistics, Keimyung University, Republic of Korea
| |
Collapse
|
18
|
Using Naive Bayes Classifier to predict osteonecrosis of the femoral head with cannulated screw fixation. Injury 2018; 49:1865-1870. [PMID: 30097310 DOI: 10.1016/j.injury.2018.07.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2018] [Revised: 07/03/2018] [Accepted: 07/26/2018] [Indexed: 02/02/2023]
Abstract
Predictive models permitting personalized prognostication for patients with cannulated screw fixation for the femoral neck fracture before operation are lacking. The objective of this study was to train, test, and cross-validate a Naive Bayes Classifier to predict the occurrence of postoperative osteonecrosis of cannulated screw fixation before the patient underwent the operation. The data for the classifier model were obtained from a ambispective cohort of 120 patients who had undergone closed reduction and cannulated screw fixation from January 2011 to June 2013. Three spatial displaced parameters of femoral neck: displacement of centre of femoral head, displacement of deepest of femoral head foveae and rotational displacement were measured from preoperative CT scans using a 3-dimensional software. The Naive Bayes Classifier was modelled with age, gender, side of fractures, mechanism of injury, preoperative traction, Pauwels angle and the three spatial parameters. After modelling, the ten-fold cross-validation method was used in this study to validate its performance. The ten-fold cross-validation method uses the whole dataset to be trained and tested by the given algorithm. Two of the three spatial parameters of femoral neck (displacement of center of femoral head and rotational displacement) were included successfully in the final Naive Bayes Classifier. The Classifier achieved good performance of the accuracy (74.4%), sensitivity (74.2%), specificity (75%), positive predictive value (92%), negative predictive value (42.9%) and AUC (0.746). We showed that the Naive Bayes Classifier have the potential utility to be used to predict the osteonecrosis of femoral head within 5 years after surgery. Although this study population was restricted to patients treated with cannulated screws fixation, Bayesian-derived models may be developed for application to patients with other surgical procedures at risk of osteonecrosis.
Collapse
|
19
|
Engelhard M, Xu H, Carin L, Oliver JA, Hallyburton M, McClernon FJ. Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2018; 85:312-331. [PMID: 30899917 PMCID: PMC6424486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Health risks from cigarette smoking - the leading cause of preventable death in the United States - can be substantially reduced by quitting. Although most smokers are motivated to quit, the majority of quit attempts fail. A number of studies have explored the role of self-reported symptoms, physiologic measurements, and environmental context on smoking risk, but less work has focused on the temporal dynamics of smoking events, including daily patterns and related nicotine effects. In this work, we examine these dynamics and improve risk prediction by modeling smoking as a self-triggering process, in which previous smoking events modify current risk. Specifically, we fit smoking events self-reported by 42 smokers to a time-varying semi-parametric Hawkes process (TV-SPHP) developed for this purpose. Results show that the TV-SPHP achieves superior prediction performance compared to related and existing models, with the incorporation of time-varying predictors having greatest benefit over longer prediction windows. Moreover, the impact function illustrates previously unknown temporal dynamics of smoking, with possible connections to nicotine metabolism to be explored in future work through a randomized study design. By more effectively predicting smoking events and exploring a self-triggering component of smoking risk, this work supports development of novel or improved cessation interventions that aim to reduce death from smoking.
Collapse
Affiliation(s)
- Matthew Engelhard
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Hongteng Xu
- Department of Electrical and Computer Engineering, Duke University, InfiniaML, Inc., Durham, NC, USA
| | - Lawrence Carin
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Jason A Oliver
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Matthew Hallyburton
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - F Joseph McClernon
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| |
Collapse
|