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Bisby MA, Barrett V, Staples LG, Nielssen O, Dear BF, Titov N. Things You Do: A randomized controlled trial of an unguided ultra-brief intervention to reduce symptoms of depression and anxiety. J Anxiety Disord 2024; 105:102882. [PMID: 38850775 DOI: 10.1016/j.janxdis.2024.102882] [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: 01/03/2024] [Revised: 05/05/2024] [Accepted: 05/22/2024] [Indexed: 06/10/2024]
Abstract
The 'Things You Do' encompass five types of actions that are strongly associated with good mental health: Healthy Thinking, Meaningful Activities, Goals and Plans, Healthy Habits, and Social Connections. Ultra-brief interventions which increase how often people perform these actions may decrease depression and anxiety. A two-arm randomized controlled trial (N = 349) compared an unguided ultra-brief intervention based on the 'Things You Do' against a waitlist control. The intervention included one online module, two practice guides, and four weeks of daily text messages. The primary timepoint was 5-weeks post-baseline. The intervention resulted in moderate reductions in depression (d = 0.51) and anxiety (d = 0.55) alongside moderate increases in the frequency of Things You Do actions (d = 0.54), compared to controls. No significant change in number of days out of role or life satisfaction were observed. Treatment completion was high (92 %), most participants reported being satisfied with the treatment (66 %), and improvements were maintained at 3-month follow-up. This study demonstrated that an automated ultra-brief 'Things You Do' intervention resulted in clinically significant reductions in depression and anxiety. Ultra-brief interventions may provide a scalable solution to support individuals who are unlikely to engage in longer forms of psychological treatment.
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Affiliation(s)
- Madelyne A Bisby
- eCentreClinic, School of Psychological Sciences, Macquarie University, Australia; MindSpot Clinic, MQ Health, Macquarie University, Australia.
| | | | | | - Olav Nielssen
- MindSpot Clinic, MQ Health, Macquarie University, Australia
| | - Blake F Dear
- eCentreClinic, School of Psychological Sciences, Macquarie University, Australia; MindSpot Clinic, MQ Health, Macquarie University, Australia
| | - Nickolai Titov
- eCentreClinic, School of Psychological Sciences, Macquarie University, Australia; MindSpot Clinic, MQ Health, Macquarie University, Australia
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2
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Hammelrath L, Hilbert K, Heinrich M, Zagorscak P, Knaevelsrud C. Select or adjust? How information from early treatment stages boosts the prediction of non-response in internet-based depression treatment. Psychol Med 2024; 54:1641-1650. [PMID: 38087867 DOI: 10.1017/s0033291723003537] [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] [Indexed: 05/29/2024]
Abstract
BACKGROUND Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these low response rates by predicting therapy outcomes on an individual level and tailoring treatment accordingly. Few studies implemented ML algorithms in internet-based depression treatment using baseline self-report data, but differing results hinder inferences on clinical practicability. This work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression. METHODS Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained random forest algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. Non-responders were defined as participants not fulfilling the criteria for reliable and clinically significant change on PHQ-9 post-treatment. Our benchmark models were logistic regressions trained on baseline PHQ-9 sum or PHQ-9 early change, using 100 iterations of randomly sampled 80/20 train-test-splits. RESULTS Best performances were reached by our models involving early treatment characteristics (recall: 0.75-0.76; AUC: 0.71-0.77). Therapeutic alliance and early symptom change constituted the most important predictors. Models trained on baseline data were not significantly better than our benchmark. CONCLUSIONS Fair accuracies were only attainable by involving information from early treatment stages. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. Implementation trials are needed to determine clinical usefulness.
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Affiliation(s)
- Leona Hammelrath
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Kevin Hilbert
- Department of Psychology, Health and Medical University Erfurt, Erfurt, Germany
| | - Manuel Heinrich
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Pavle Zagorscak
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
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3
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Gutierrez G, Stephenson C, Eadie J, Asadpour K, Alavi N. Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review. Front Psychiatry 2024; 15:1356773. [PMID: 38774435 PMCID: PMC11106393 DOI: 10.3389/fpsyt.2024.1356773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 04/22/2024] [Indexed: 05/24/2024] Open
Abstract
Introduction Online mental healthcare has gained significant attention due to its effectiveness, accessibility, and scalability in the management of mental health symptoms. Despite these advantages over traditional in-person formats, including higher availability and accessibility, issues with low treatment adherence and high dropout rates persist. Artificial intelligence (AI) technologies could help address these issues, through powerful predictive models, language analysis, and intelligent dialogue with users, however the study of these applications remains underexplored. The following mixed methods review aimed to supplement this gap by synthesizing the available evidence on the applications of AI in online mental healthcare. Method We searched the following databases: MEDLINE, CINAHL, PsycINFO, EMBASE, and Cochrane. This review included peer-reviewed randomized controlled trials, observational studies, non-randomized experimental studies, and case studies that were selected using the PRISMA guidelines. Data regarding pre and post-intervention outcomes and AI applications were extracted and analyzed. A mixed-methods approach encompassing meta-analysis and network meta-analysis was used to analyze pre and post-intervention outcomes, including main effects, depression, anxiety, and study dropouts. We applied the Cochrane risk of bias tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to assess the quality of the evidence. Results Twenty-nine studies were included revealing a variety of AI applications including triage, psychotherapy delivery, treatment monitoring, therapy engagement support, identification of effective therapy features, and prediction of treatment response, dropout, and adherence. AI-delivered self-guided interventions demonstrated medium to large effects on managing mental health symptoms, with dropout rates comparable to non-AI interventions. The quality of the data was low to very low. Discussion The review supported the use of AI in enhancing treatment response, adherence, and improvements in online mental healthcare. Nevertheless, given the low quality of the available evidence, this study highlighted the need for additional robust and high-powered studies in this emerging field. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=443575, identifier CRD42023443575.
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Affiliation(s)
- Gilmar Gutierrez
- Department of Psychiatry, Faculty of Health Sciences, Queen’s University, Kingston, ON, Canada
| | - Callum Stephenson
- Department of Psychiatry, Faculty of Health Sciences, Queen’s University, Kingston, ON, Canada
| | - Jazmin Eadie
- Department of Psychiatry, Faculty of Health Sciences, Queen’s University, Kingston, ON, Canada
- Faculty of Education, Queen’s University, Kingston, ON, Canada
- Department of Psychology, Faculty of Arts and Sciences, Queen’s University, Kingston, ON, Canada
| | - Kimia Asadpour
- Department of Psychiatry, Faculty of Health Sciences, Queen’s University, Kingston, ON, Canada
| | - Nazanin Alavi
- Department of Psychiatry, Faculty of Health Sciences, Queen’s University, Kingston, ON, Canada
- Centre for Neuroscience Studies, Faculty of Health Sciences, Queen’s University, Kingston, ON, Canada
- OPTT Inc., Toronto, ON, Canada
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4
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Zainal NH, Newman MG. Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model. J Anxiety Disord 2024; 102:102825. [PMID: 38245961 PMCID: PMC10922999 DOI: 10.1016/j.janxdis.2024.102825] [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: 02/21/2023] [Revised: 12/26/2023] [Accepted: 01/02/2024] [Indexed: 01/23/2024]
Abstract
Precision medicine methods (machine learning; ML) can identify which clients with generalized anxiety disorder (GAD) benefit from mindfulness ecological momentary intervention (MEMI) vs. self-monitoring app (SM). We used randomized controlled trial data of MEMI vs. SM for GAD (N = 110) and tested three ML models to predict one-month follow-up reliable improvement in GAD severity, perseverative cognitions (PC), trait mindfulness (TM), and executive function (EF). Eleven baseline predictors were tested regarding differential reliable change from MEMI vs. SM (age, sex, race, EF errors, inhibitory dyscontrol, set-shifting deficits, verbal fluency, working memory, GAD severity, TM, PC). The final top five prescriptive predictor models of all outcomes performed well (AUC = .752 .886). The following variables predicted better outcome from MEMI vs. SM: Higher GAD severity predicted more GAD improvement but less EF improvement. Elevated PC, inhibitory dyscontrol, and verbal dysfluency predicted better improvement in most outcomes. Greater set-shifting and TM predicted stronger improvements in GAD symptoms and TM. Older age predicted more alleviation of GAD and PC symptoms. Women exhibited more enhancements in trait mindfulness and EF than men. White individuals benefitted more than non-White. PC, TM, EF, and sociodemographic data might help predictive models optimize intervention selection for GAD.
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Affiliation(s)
- Nur Hani Zainal
- Harvard Medical School, Boston, MA, USA; National University of Singapore, Kent Ridge, Singapore.
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5
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Thielecke J, Kuper P, Ebert D, Cuijpers P, Smit F, Riper H, Lehr D, Buntrock C. Does outcome expectancy predict outcomes in online depression prevention? Secondary analysis of randomised-controlled trials. Health Expect 2024; 27:e13951. [PMID: 39102655 PMCID: PMC10753640 DOI: 10.1111/hex.13951] [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: 07/17/2023] [Revised: 12/06/2023] [Accepted: 12/14/2023] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Evidence shows that online interventions could prevent depression. However, to improve the effectiveness of preventive online interventions in individuals with subthreshold depression, it is worthwhile to study factors influencing intervention outcomes. Outcome expectancy has been shown to predict treatment outcomes in psychotherapy for depression. However, little is known about whether this also applies to depression prevention. The aim of this study was to investigate the role of participants' outcome expectancy in an online depression prevention intervention. METHODS A secondary data analysis was conducted using data from two randomised-controlled trials (N = 304). Multilevel modelling was used to explore the effect of outcome expectancy on depressive symptoms and close-to-symptom-free status postintervention (6-7 weeks) and at follow-up (3-6 months). In a subsample (n = 102), Cox regression was applied to assess the effect on depression onset within 12 months. Explorative analyses included baseline characteristics as possible moderators. Outcome expectancy did not predict posttreatment outcomes or the onset of depression. RESULTS Small effects were observed at follow-up for depressive symptoms (β = -.39, 95% confidence interval [CI]: [-0.75, -0.03], p = .032, padjusted = .130) and close-to-symptom-free status (relative risk = 1.06, 95% CI: [1.01, 1.11], p = .013, padjusted = 0.064), but statistical significance was not maintained when controlling for multiple testing. Moderator analyses indicated that expectancy could be more influential for females and individuals with higher initial symptom severity. CONCLUSION More thoroughly designed, predictive studies targeting outcome expectancy are necessary to assess the full impact of the construct for effective depression prevention. PATIENT OR PUBLIC CONTRIBUTION This secondary analysis did not involve patients, service users, care-givers, people with lived experience or members of the public. However, the findings incorporate the expectations of participants using the preventive online intervention, and these exploratory findings may inform the future involvement of participants in the design of indicated depression prevention interventions for adults. CLINICAL TRIAL REGISTRATION Original studies: DRKS00004709, DRKS00005973; secondary analysis: osf.io/9xj6a.
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Affiliation(s)
- Janika Thielecke
- Professorship of Psychology and Digital Mental Health Care, Department of Sports and Health SciencesTechnical University of MunichMunichGermany
- Department of Clinical Psychology and Psychotherapy, Institute of PsychologyFriedrich‐Alexander ‐University Erlangen‐NürnbergErlangenGermany
- The Netherlands Organization for Applied Scientific Research (TNO)LeidenThe Netherlands
| | - Paula Kuper
- Professorship of Psychology and Digital Mental Health Care, Department of Sports and Health SciencesTechnical University of MunichMunichGermany
- Institute of Social Medicine and Health Systems Research, Faculty of MedicineOtto‐von‐Guericke University MagdeburgMagdeburgGermany
| | - David Ebert
- Department of Clinical Psychology and Psychotherapy, Institute of PsychologyFriedrich‐Alexander ‐University Erlangen‐NürnbergErlangenGermany
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental PsychologyVU UniversityAmsterdamThe Netherlands
- Amsterdam Public HealthAmsterdam University Medical CentersAmsterdamThe Netherlands
| | - Filip Smit
- Department of Clinical, Neuro and Developmental PsychologyVU UniversityAmsterdamThe Netherlands
- Amsterdam Public HealthAmsterdam University Medical CentersAmsterdamThe Netherlands
- Department of Mental Health and PreventionTrimbos Institute, Netherlands Institute of Mental Health and AddictionUtrechtThe Netherlands
- Department of Epidemiology and BiostatisticsUniversity Medical Center AmsterdammsterdamThe Netherlands
| | - Heleen Riper
- Department of Clinical, Neuro and Developmental PsychologyVU UniversityAmsterdamThe Netherlands
- Amsterdam Public HealthAmsterdam University Medical CentersAmsterdamThe Netherlands
- Department of PsychiatryVU University Medical CenterAmsterdamThe Netherlands
| | - Dirk Lehr
- Department of Health Psychology and Applied Biological PsychologyLeuphana University LuneburgLüneburgGermany
| | - Claudia Buntrock
- Institute of Social Medicine and Health Systems Research, Faculty of MedicineOtto‐von‐Guericke University MagdeburgMagdeburgGermany
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6
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Deisenhofer AK, Barkham M, Beierl ET, Schwartz B, Aafjes-van Doorn K, Beevers CG, Berwian IM, Blackwell SE, Bockting CL, Brakemeier EL, Brown G, Buckman JEJ, Castonguay LG, Cusack CE, Dalgleish T, de Jong K, Delgadillo J, DeRubeis RJ, Driessen E, Ehrenreich-May J, Fisher AJ, Fried EI, Fritz J, Furukawa TA, Gillan CM, Gómez Penedo JM, Hitchcock PF, Hofmann SG, Hollon SD, Jacobson NC, Karlin DR, Lee CT, Levinson CA, Lorenzo-Luaces L, McDanal R, Moggia D, Ng MY, Norris LA, Patel V, Piccirillo ML, Pilling S, Rubel JA, Salazar-de-Pablo G, Schleider JL, Schnurr PP, Schueller SM, Siegle GJ, Uher R, Watkins E, Webb CA, Wiltsey Stirman S, Wynants L, Youn SJ, Zilcha-Mano S, Lutz W, Cohen ZD. Implementing precision methods in personalizing psychological therapies: Barriers and possible ways forward. Behav Res Ther 2024; 172:104443. [PMID: 38086157 DOI: 10.1016/j.brat.2023.104443] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 12/26/2023]
Affiliation(s)
| | | | | | | | | | | | | | | | - Claudi L Bockting
- AmsterdamUMC, Department of Psychiatry, Research Program Amsterdam Public Health and Centre for Urban Mental Health, University of Amsterdam, the Netherlands
| | | | | | | | | | | | | | - Kim de Jong
- Leiden University, Institute of Psychology, USA
| | | | | | | | | | | | | | - Jessica Fritz
- University of Cambridge, UK; Philipps University of Marburg, Germany
| | | | - Claire M Gillan
- School of Psychology, Trinity College Institute for Neuroscience, And Global Brain Health Institute, Trinity College Dublin, USA
| | | | | | | | | | | | | | | | | | | | | | | | - Mei Yi Ng
- Florida International University, USA
| | | | | | | | | | | | | | - Jessica L Schleider
- Stony Brook University and Feinberg School of Medicine Northwestern University, USA
| | - Paula P Schnurr
- National Center for PTSD and Geisel School of Medicine at Dartmouth, USA
| | | | | | | | | | | | | | | | - Soo Jeong Youn
- Reliant Medical Group, OptumCare and Harvard Medical School, USA
| | | | | | - Zachary D Cohen
- University of California, Los Angeles and University of Arizona, USA.
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7
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Hallenbeck HW, Wielgosz J, Cohen ZD, Kuhn E, Cloitre M. A prognostic index to predict symptom and functional outcomes of a coached, web-based intervention for trauma-exposed veterans. Psychol Serv 2023:2024-38515-001. [PMID: 38127501 PMCID: PMC11190026 DOI: 10.1037/ser0000828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Researchers at the Department of Veterans Affairs (VA) have studied interventions for posttraumatic stress disorder and co-occurring conditions in both traditional and digital formats. One such empirically supported intervention is web skills training in affective and interpersonal regulation (webSTAIR), a coached, 10-module web program based on STAIR. To understand which patient characteristics were predictive of webSTAIR outcomes in a sample of trauma-exposed veterans (N = 189), we used machine learning (ML) to develop a prognostic index from among 18 baseline characteristics (i.e., demographic, military, trauma history, and clinical) to predict posttreatment posttraumatic stress disorder severity, depression severity, and psychosocial functioning impairment. We compared the ML models to a benchmark of linear regression models in which the only predictor was the baseline severity score of the outcome measure. The ML and "severity-only" models performed similarly, explaining 39%-45% of the variance in outcomes. This suggests that baseline symptom severity and functioning are strong indicators for webSTAIR outcomes in veterans, with higher severity indicating worse prognosis, and that the other variables examined did not contribute significant added predictive signal. Findings also highlight the importance of comparing ML models to an appropriate benchmark. Future research with larger samples could potentially detect smaller patient-level effects as well as effects driven by other types of variables (e.g., therapeutic process variables). As a transdiagnostic, digital intervention, webSTAIR can potentially serve a diverse veteran population with varying trauma histories and may be best conceptualized as a beneficial first step of a stepped care model for those with heightened symptoms or impairment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Haijing Wu Hallenbeck
- National Center for PTSD Dissemination and Training Division, VA Palo Alto Health Care System
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine
| | - Joseph Wielgosz
- National Center for PTSD Dissemination and Training Division, VA Palo Alto Health Care System
| | | | - Eric Kuhn
- National Center for PTSD Dissemination and Training Division, VA Palo Alto Health Care System
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine
| | - Marylene Cloitre
- National Center for PTSD Dissemination and Training Division, VA Palo Alto Health Care System
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine
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8
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Sun Z, Yuan Y, Dong X, Liu Z, Cai K, Cheng W, Wu J, Qiao Z, Chen A. Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder. Int J Clin Health Psychol 2023; 23:100409. [PMID: 37711468 PMCID: PMC10498172 DOI: 10.1016/j.ijchp.2023.100409] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/22/2023] [Indexed: 09/16/2023] Open
Abstract
The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.
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Affiliation(s)
- Zhiyuan Sun
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Yunhao Yuan
- School of Information Engineering, Yangzhou University, Yangzhou 225127, China
| | - Xiaoxiao Dong
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Zhimei Liu
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Kelong Cai
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Wei Cheng
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Jingjing Wu
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Zhiyuan Qiao
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Aiguo Chen
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
- Nanjing Institute of Physical Education, Nanjing 210014, China
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9
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Han J, Li H, Lin H, Wu P, Wang S, Tu J, Lu J. Depression prediction based on LassoNet-RNN model: A longitudinal study. Heliyon 2023; 9:e20684. [PMID: 37842633 PMCID: PMC10570602 DOI: 10.1016/j.heliyon.2023.e20684] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/21/2023] [Accepted: 10/04/2023] [Indexed: 10/17/2023] Open
Abstract
Depression has become a widespread health concern today. Understanding the influencing factors can promote human mental health as well as provide a basis for exploring preventive measures. Combining LassoNet with recurrent neural network (RNN), this study constructed a screening model ,LassoNet-RNN, for identifying influencing factors of individual depression. Based on multi-wave surveys of China Health and Retirement Longitudinal Study (CHARLS) dataset (11,661 observations), we analyzed the multivariate time series data and recognized 27 characteristic variables selected from four perspectives: demographics, health-related risk factors, household economic status, and living environment. Additionally, the importance rankings of the characteristic variables were obtained. These results offered insightful recommendations for theoretical developments and practical decision making in public health.
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Affiliation(s)
- Jiatong Han
- School of Computer Science, Nanjing Audit University, China
| | - Hao Li
- School of Computer Science, Nanjing Audit University, China
| | - Han Lin
- Jiangsu Key Laboratory of Public Project Audit, School of Engineering Audit, Nanjing Audit University, China
| | - Pingping Wu
- Jiangsu Key Laboratory of Public Project Audit, School of Engineering Audit, Nanjing Audit University, China
| | - Shidan Wang
- School of Computer Science, Nanjing Audit University, China
| | - Juan Tu
- Key Laboratory of Modern Acoustics (MOE), School of Physics, Nanjing University, China
| | - Jing Lu
- Key Laboratory of Modern Acoustics (MOE), School of Physics, Nanjing University, China
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10
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Collins AC, Price GD, Woodworth RJ, Jacobson NC. Predicting Individual Response to a Web-Based Positive Psychology Intervention: A Machine Learning Approach. THE JOURNAL OF POSITIVE PSYCHOLOGY 2023; 19:675-685. [PMID: 38854972 PMCID: PMC11156258 DOI: 10.1080/17439760.2023.2254743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 07/13/2023] [Indexed: 06/11/2024]
Abstract
Positive psychology interventions (PPIs) are effective at increasing happiness and decreasing depressive symptoms. PPIs are often administered as self-guided web-based interventions, but not all persons benefit from web-based interventions. Therefore, it is important to identify whether someone is likely to benefit from web-based PPIs, in order to triage persons who may not benefit from other interventions. In the current study, we used machine learning to predict individual response to a web-based PPI, in order to investigate baseline prognostic indicators of likelihood of response (N = 120). Our models demonstrated moderate correlations (happiness: r Test = 0.30 ± 0.09; depressive symptoms: r Test = 0.39 ± 0.06), indicating that baseline features can predict changes in happiness and depressive symptoms at a 6-month follow-up. Thus, machine learning can be used to predict outcome changes from a web-based PPI and has important clinical implications for matching individuals to PPIs based on their individual characteristics.
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Affiliation(s)
- Amanda C. Collins
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
- Department of Psychiatry, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
- Department of Psychology, Mississippi State University, Mississippi State, MS, United States
| | - George D. Price
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
- Quantitative Biomedical Sciences Program, Dartmouth College, Lebanon, NH, United States
| | | | - Nicholas C. Jacobson
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
- Quantitative Biomedical Sciences Program, Dartmouth College, Lebanon, NH, United States
- Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
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11
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Cohen ZD, Schueller SM. Expanding, improving, and understanding behaviour research and therapy through digital mental health. Behav Res Ther 2023; 167:104358. [PMID: 37418857 DOI: 10.1016/j.brat.2023.104358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Affiliation(s)
- Zachary D Cohen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, USA.
| | - Stephen M Schueller
- Department of Psychological Science, University of California, Irvine, USA; Department of Informatics, University of California, Irvine, USA
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12
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Ziobrowski HN, Cui R, Ross EL, Liu H, Puac-Polanco V, Turner B, Leung LB, Bossarte RM, Bryant C, Pigeon WR, Oslin DW, Post EP, Zaslavsky AM, Zubizarreta JR, Nierenberg AA, Luedtke A, Kennedy CJ, Kessler RC. Development of a model to predict psychotherapy response for depression among Veterans. Psychol Med 2023; 53:3591-3600. [PMID: 35144713 PMCID: PMC9365879 DOI: 10.1017/s0033291722000228] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
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Affiliation(s)
| | - Ruifeng Cui
- VISN 4 Mental Illness Research, Education and Clinical Center, VA Pittsburgh Health Care System, Department of Veterans Affairs, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Eric L. Ross
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Howard Liu
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
| | | | - Brett Turner
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Lucinda B. Leung
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Division of General Internal Medicine and Health Services Research, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Robert M. Bossarte
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, USA
| | - Corey Bryant
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
| | - Wilfred R. Pigeon
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
| | - David W. Oslin
- VISN 4 Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward P. Post
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
- Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Alan M. Zaslavsky
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Jose R. Zubizarreta
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
- Department of Biostatistics, Harvard University, Cambridge, MA, USA
| | - Andrew A. Nierenberg
- Dauten Family Center for Bipolar Treatment Innovation, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris J. Kennedy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
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13
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Giesemann J, Delgadillo J, Schwartz B, Bennemann B, Lutz W. Predicting dropout from psychological treatment using different machine learning algorithms, resampling methods, and sample sizes. Psychother Res 2023:1-13. [PMID: 36669124 DOI: 10.1080/10503307.2022.2161432] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE The occurrence of dropout from psychological interventions is associated with poor treatment outcome and high health, societal and economic costs. Recently, machine learning (ML) algorithms have been tested in psychotherapy outcome research. Dropout predictions are usually limited by imbalanced datasets and the size of the sample. This paper aims to improve dropout prediction by comparing ML algorithms, sample sizes and resampling methods. METHOD Twenty ML algorithms were examined in twelve subsamples (drawn from a sample of N = 49,602) using four resampling methods in comparison to the absence of resampling and to each other. Prediction accuracy was evaluated in an independent holdout dataset using the F1-Measure. RESULTS Resampling methods improved the performance of ML algorithms and down-sampling can be recommended, as it was the fastest method and as accurate as the other methods. For the highest mean F1-Score of .51 a minimum sample size of N = 300 was necessary. No specific algorithm or algorithm group can be recommended. CONCLUSION Resampling methods could improve the accuracy of predicting dropout in psychological interventions. Down-sampling is recommended as it is the least computationally taxing method. The training sample should contain at least 300 cases.
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Affiliation(s)
- Julia Giesemann
- Clinical Psychology and Psychotherapy, Department of Psychology, University of Trier, Trier, Germany
| | - Jaime Delgadillo
- Clinical and Applied Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, UK
| | - Brian Schwartz
- Clinical Psychology and Psychotherapy, Department of Psychology, University of Trier, Trier, Germany
| | - Björn Bennemann
- Clinical Psychology and Psychotherapy, Department of Psychology, University of Trier, Trier, Germany
| | - Wolfgang Lutz
- Clinical Psychology and Psychotherapy, Department of Psychology, University of Trier, Trier, Germany
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14
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Cohen ZD, DeRubeis RJ, Hayes R, Watkins ER, Lewis G, Byng R, Byford S, Crane C, Kuyken W, Dalgleish T, Schweizer S. The development and internal evaluation of a predictive model to identify for whom Mindfulness-Based Cognitive Therapy (MBCT) offers superior relapse prevention for recurrent depression versus maintenance antidepressant medication. Clin Psychol Sci 2023; 11:59-76. [PMID: 36698442 PMCID: PMC7614103 DOI: 10.1177/21677026221076832] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to Mindfulness-Based Cognitive Therapy (MBCT). Using data from the PREVENT trial (N=424), we constructed prognostic models using elastic net regression that combined demographic, clinical and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: AUC=.68) predicted relapse better than baseline depression severity (AUC=.54; one-tailed DeLong's test: z=2.8, p=.003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared to those who maintained ADM (48% vs. 70% relapse, respectively; superior survival times [z=-2.7, p=.008]). For individuals with moderate-to-good ADM prognosis, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression.
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Affiliation(s)
| | | | - Rachel Hayes
- National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South West Peninsula, University of Exeter
| | | | - Glyn Lewis
- Division of Psychiatry, Faulty of Brain Sciences, University College London
- Community Primary Care Research Group, University of Plymouth
| | - Richard Byng
- Community Primary Care Research Group, University of Plymouth
- National Institute of Health Research Collaboration for Leadership in Applied Health Research and Care, South West Peninsula, England
| | - Sarah Byford
- Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London
| | - Catherine Crane
- Department of Psychiatry, Medical Sciences Division, University of Oxford
| | - Willem Kuyken
- Department of Psychiatry, Medical Sciences Division, University of Oxford
| | - Tim Dalgleish
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, England
| | - Susanne Schweizer
- Department of Psychology, University of Cambridge
- School of Psychology, University of New South Wales
- Susanne Schweizer, Department of Psychology, University of Cambridge
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15
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Smits JAJ, Monfils MH, Otto MW, Telch MJ, Shumake J, Feinstein JS, Khalsa SS, Cobb AR, Parsons EM, Long LJ, McSpadden B, Johnson D, Greenberg A. CO 2 reactivity as a biomarker of exposure-based therapy non-response: study protocol. BMC Psychiatry 2022; 22:831. [PMID: 36575425 PMCID: PMC9793569 DOI: 10.1186/s12888-022-04478-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 12/15/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Exposure-based therapy is an effective first-line treatment for anxiety-, obsessive-compulsive, and trauma- and stressor-related disorders; however, many patients do not improve, resulting in prolonged suffering and poorly used resources. Basic research on fear extinction may inform the development of a biomarker for the selection of exposure-based therapy. Growing evidence links orexin system activity to deficits in fear extinction and we have demonstrated that reactivity to an inhaled carbon dioxide (CO2) challenge-a safe, affordable, and easy-to-implement procedure-can serve as a proxy for orexin system activity and predicts fear extinction deficits in rodents. Building upon this basic research, the goal for the proposed study is to validate CO2 reactivity as a biomarker of exposure-based therapy non-response. METHODS We will assess CO2 reactivity in 600 adults meeting criteria for one or more fear- or anxiety-related disorders prior to providing open exposure-based therapy. By incorporating CO2 reactivity into a multivariate model predicting treatment non-response that also includes reactivity to hyperventilation as well as a number of related predictor variables, we will establish the mechanistic specificity and the additive predictive utility of the potential CO2 reactivity biomarker. By developing models independently within two study sites (University of Texas at Austin and Boston University) and predicting the other site's data, we will validate that the results are likely to generalize to future clinical samples. DISCUSSION Representing a necessary stage in translating basic research, this investigation addresses an important public health issue by testing an accessible clinical assessment strategy that may lead to a more effective treatment selection (personalized medicine) for patients with anxiety- and fear-related disorders, and enhanced understanding of the mechanisms governing exposure-based therapy. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT05467683 (20/07/2022).
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Affiliation(s)
- Jasper A. J. Smits
- grid.89336.370000 0004 1936 9924Department of Psychology and Institute for Mental Health Research, University of Texas at Austin, 1 University Station, Austin, TX 78712 USA
| | - Marie-H. Monfils
- grid.89336.370000 0004 1936 9924Department of Psychology and Institute for Mental Health Research, University of Texas at Austin, 1 University Station, Austin, TX 78712 USA
| | - Michael W. Otto
- grid.189504.10000 0004 1936 7558Department of Psychological and Brain Sciences, Boston University, 900 Commonwealth Avenue, Floor 2, Boston, MA 02215 USA
| | - Michael J. Telch
- grid.89336.370000 0004 1936 9924Department of Psychology and Institute for Mental Health Research, University of Texas at Austin, 1 University Station, Austin, TX 78712 USA
| | - Jason Shumake
- grid.89336.370000 0004 1936 9924Department of Psychology and Institute for Mental Health Research, University of Texas at Austin, 1 University Station, Austin, TX 78712 USA
| | - Justin S. Feinstein
- grid.417423.70000 0004 0512 88633The Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, Oklahoma 74136 USA
| | - Sahib S. Khalsa
- grid.417423.70000 0004 0512 88633The Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, Oklahoma 74136 USA
| | - Adam R. Cobb
- grid.89336.370000 0004 1936 9924Department of Psychology and Institute for Mental Health Research, University of Texas at Austin, 1 University Station, Austin, TX 78712 USA ,grid.259828.c0000 0001 2189 3475Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina and Ralph H. Johnson VAHCS, 67 President Street MSC 862, Charleston, SC 29425 USA
| | - E. Marie Parsons
- grid.189504.10000 0004 1936 7558Department of Psychological and Brain Sciences, Boston University, 900 Commonwealth Avenue, Floor 2, Boston, MA 02215 USA
| | - Laura J. Long
- grid.189504.10000 0004 1936 7558Department of Psychological and Brain Sciences, Boston University, 900 Commonwealth Avenue, Floor 2, Boston, MA 02215 USA
| | - Bryan McSpadden
- grid.89336.370000 0004 1936 9924Department of Psychology and Institute for Mental Health Research, University of Texas at Austin, 1 University Station, Austin, TX 78712 USA
| | - David Johnson
- grid.89336.370000 0004 1936 9924Department of Psychology and Institute for Mental Health Research, University of Texas at Austin, 1 University Station, Austin, TX 78712 USA
| | - Alma Greenberg
- grid.189504.10000 0004 1936 7558Department of Psychological and Brain Sciences, Boston University, 900 Commonwealth Avenue, Floor 2, Boston, MA 02215 USA
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16
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Ali FZ, Wengler K, He X, Nguyen MH, Parsey RV, DeLorenzo C. Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression. NEUROSCIENCE INFORMATICS 2022; 2:100110. [PMID: 36699194 PMCID: PMC9873411 DOI: 10.1016/j.neuri.2022.100110] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Introduction Pretreatment positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-D-glucose (FDG) and magnetic resonance spectroscopy (MRS) may identify biomarkers for predicting remission (absence of depression). Yet, no such image-based biomarkers have achieved clinical validity. The purpose of this study was to identify biomarkers of remission using machine learning (ML) with pretreatment FDG-PET/MRS neuroimaging, to reduce patient suffering and economic burden from ineffective trials. Methods This study used simultaneous PET/MRS neuroimaging from a double-blind, placebo-controlled, randomized antidepressant trial on 60 participants with major depressive disorder (MDD) before initiating treatment. After eight weeks of treatment, those with ≤ 7 on 17-item Hamilton Depression Rating Scale were designated a priori as remitters (free of depression, 37%). Metabolic rate of glucose uptake (metabolism) from 22 brain regions were acquired from PET. Concentrations (mM) of glutamine and glutamate and gamma-aminobutyric acid (GABA) in anterior cingulate cortex were quantified from MRS. The data were randomly split into 67% train and cross-validation (n = 40), and 33% test (n = 20) sets. The imaging features, along with age, sex, handedness, and treatment assignment (selective serotonin reuptake inhibitor or SSRI vs. placebo) were entered into the eXtreme Gradient Boosting (XGBoost) classifier for training. Results In test data, the model showed 62% sensitivity, 92% specificity, and 77% weighted accuracy. Pretreatment metabolism of left hippocampus from PET was the most predictive of remission. Conclusions The pretreatment neuroimaging takes around 60 minutes but has potential to prevent weeks of failed treatment trials. This study effectively addresses common issues for neuroimaging analysis, such as small sample size, high dimensionality, and class imbalance.
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Affiliation(s)
- Farzana Z. Ali
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Kenneth Wengler
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Xiang He
- Department of Radiology, Stony Brook Medicine, Stony Brook, NY, USA
- Department of Radiology, Northshore University Hospital, Manhasset, NY, USA
| | - Minh Hoai Nguyen
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Ramin V. Parsey
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
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17
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Lee KS, Ham BJ. Machine Learning on Early Diagnosis of Depression. Psychiatry Investig 2022; 19:597-605. [PMID: 36059048 PMCID: PMC9441463 DOI: 10.30773/pi.2022.0075] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/23/2022] [Indexed: 11/27/2022] Open
Abstract
To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were "depression" (title) and "random forest" (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1-100.0 for accuracy and 64.0-96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression.
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Affiliation(s)
- Kwang-Sig Lee
- AI Center, Korea University Anam Hospital, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Mental Health, Korea University Anam Hospital, Seoul, Republic of Korea
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18
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Pearson R, Carl E, Creech SK. Computerized Psychological Interventions in Veterans and Service Members: Systematic Review of Randomized Controlled Trials. J Med Internet Res 2022; 24:e30065. [PMID: 35657663 PMCID: PMC9206197 DOI: 10.2196/30065] [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: 05/03/2021] [Revised: 10/07/2021] [Accepted: 12/20/2021] [Indexed: 11/16/2022] Open
Abstract
Background Computerized psychological interventions can overcome logistical and psychosocial barriers to the use of mental health care in the Veterans Affairs and Department of Defense settings. Objective In this systematic review, we aim to outline the existing literature, with the goal of describing: the scope and quality of the available literature, intervention characteristics, study methods, study efficacy, and study limitations and potential directions for future research. Methods Systematic searches of two databases (PsycINFO and PubMed) using PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines were conducted from inception until November 15, 2020. The following inclusion criteria were used: the study was published in an English language peer-reviewed journal, participants were randomly allocated to a computerized psychological intervention or a control group (non–computerized psychological intervention active treatment or nonactive control group), an intervention in at least one treatment arm was primarily delivered through the computer or internet with or without additional support, participants were veterans or service members, and the study used validated measures to examine the effect of treatment on psychological outcomes. Results This review included 23 studies that met the predefined inclusion criteria. Most studies were at a high risk of bias. Targeted outcomes, participant characteristics, type of support delivered, adherence, and participant satisfaction were described. Most of the examined interventions (19/24, 79%) yielded positive results. Study limitations included participant characteristics limiting study inference, high rates of attrition, and an overreliance on self-reported outcomes. Conclusions Relatively few high-quality studies were identified, and more rigorous investigations are needed. Several recommendations for future research are discussed, including the adoption of methods that minimize attrition, optimize use, and allow for personalization of treatment.
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Affiliation(s)
- Rahel Pearson
- Veterans Health Administration Veterans Integrated Service Network 17, Center of Excellence for Research on Returning War Veterans, Central Texas Veterans Affairs Healthcare System, Waco, TX, United States
| | - Emily Carl
- Department of Psychology, University of Texas, Austin, TX, United States
| | - Suzannah K Creech
- Veterans Health Administration Veterans Integrated Service Network 17, Center of Excellence for Research on Returning War Veterans, Central Texas Veterans Affairs Healthcare System, Waco, TX, United States.,Department of Psychiatry and Behavioral Sciences, Dell Medical School of the University of Texas, Austin, TX, United States
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19
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Linardon J, Fuller‐Tyszkiewicz M, Shatte A, Greenwood CJ. An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms. Int J Eat Disord 2022; 55:845-850. [PMID: 35560256 PMCID: PMC9544906 DOI: 10.1002/eat.23733] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/03/2022] [Accepted: 05/01/2022] [Indexed: 11/13/2022]
Abstract
OBJECTIVE Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms. METHOD Data were aggregated from three RCTs (n = 826) of self-guided digital interventions for EDs. Predictive models were developed for four key outcomes: uptake, adherence, drop-out, and symptom-level change. Seven ML techniques for classification were tested and compared against the generalized linear model (GLM). RESULTS The seven ML methods used to predict outcomes from 36 baseline variables were poor for the three engagement outcomes (AUCs = 0.48-0.52), but adequate for symptom-level change (R2 = .15-.40). ML did not offer an added benefit to the GLM. Incorporating intervention usage pattern data improved ML prediction accuracy for drop-out (AUC = 0.75-0.93) and adherence (AUC = 0.92-0.99). Age, motivation, symptom severity, and anxiety emerged as influential outcome predictors. CONCLUSION A limited set of routinely measured baseline variables was not sufficient to detect a performance benefit of ML over traditional approaches. The benefits of ML may emerge when numerous usage pattern variables are modeled, although this validation in larger datasets before stronger conclusions can be made.
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Affiliation(s)
- Jake Linardon
- Centre for Social and Early Emotional Development and School of PsychologyDeakin UniversityGeelongVictoria
| | - Matthew Fuller‐Tyszkiewicz
- Centre for Social and Early Emotional Development and School of PsychologyDeakin UniversityGeelongVictoria
| | - Adrian Shatte
- Federation University, School of Engineering, Information Technology & Physical SciencesMelbourneVictoriaAustralia
| | - Christopher J. Greenwood
- Centre for Social and Early Emotional Development and School of PsychologyDeakin UniversityGeelongVictoria,Centre for Adolescent Health, Murdoch Children's Research InstituteParkvilleVictoriaAustralia
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20
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Kambeitz-Ilankovic L, Koutsouleris N, Upthegrove R. The potential of precision psychiatry: what is in reach? Br J Psychiatry 2022; 220:175-178. [PMID: 35354501 DOI: 10.1192/bjp.2022.23] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.
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Affiliation(s)
- Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Germany; and Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Max-Planck Institute of Psychiatry, Munich, Germany; and Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Rachel Upthegrove
- Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, UK; and Institute for Mental Health, University of Birmingham, UK
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21
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Using Smartphone App Use and Lagged-Ensemble Machine Learning for the Prediction of Work Fatigue and Boredom. COMPUTERS IN HUMAN BEHAVIOR 2022; 127:107029. [PMID: 34776600 PMCID: PMC8589273 DOI: 10.1016/j.chb.2021.107029] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
INTRO As smartphone usage becomes increasingly prevalent in the workplace, the physical and psychological implications of this behavior warrant consideration. Recent research has investigated associations between workplace smartphone use and fatigue and boredom, yet findings are not conclusive. METHODS To build off recent efforts, we applied an ensemble machine learning model on a previously published dataset of N = 83 graduate students in the Netherlands to predict work boredom and fatigue from passively collected smartphone app use information. Using time-based feature engineering and lagged variations of the data to train, validate, and test idiographic models, we evaluated the efficacy of a lagged-ensemble predictive paradigm on sparse temporal data. Moreover, we probed the relative importance of both derived app use variables and lags within this predictive framework. RESULTS The ability to predict fatigue and boredom trajectories from app use information was heterogeneous and highly person-specific. Idiographic modeling reflected moderate to high correlative capacity (r > 0.4) in 47% of participants for fatigue and 24% for boredom, with better overall performance in the fatigue prediction task. App use relating to duration, communication, and patterns of use frequency were among the most important features driving predictions across lags, with longer lags contributing more heavily to final ensemble predictions compared with shorter ones. CONCLUSION A lag- specific ensemble predictive paradigm is a promising approach to leveraging high-dimensional app use behavioral data for the prediction of work fatigue and boredom. Future research will benefit from evaluating associations on densely collected data across longer time scales.
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22
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Myklebost SB, Gjestad R, Inal Y, Hammar Å, Nordgreen T. Predictors of Treatment Response to an Internet-Delivered Intervention Targeting Residual Cognitive Symptoms After Major Depressive Disorder. Front Psychiatry 2022; 13:795698. [PMID: 35418884 PMCID: PMC8995427 DOI: 10.3389/fpsyt.2022.795698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Residual cognitive symptoms after depression are common and associated with reduced daily life functioning and an increased risk of depression relapse. There is a lack of knowledge on treatments targeting residual cognitive symptoms after major depressive disorder (MDD), including the factors associated with treatment response. The aim of the current study is to explore factors of treatment response to a guided internet-delivered intervention for former depressed adults experiencing residual cognitive symptoms. METHOD Forty-three former depressed adults with residual cognitive symptoms were included. Linear mixed model analyses were used to investigate the impact of pre-treatment demographic-, illness, and symptom variables, and therapy process variables, such as credibility, expectancy, and user behavior, on reduction in residual cognitive symptoms from pre-treatment to 6-month follow-up. RESULTS Having had MDD for a year or less predicted more reductions in residual cognitive symptoms from pre- to 6-month follow-up. Higher levels of perceived treatment credibility and expectancy evaluated in the early course of treatment did also predict a positive treatment response. No demographic-, symptom-variables, previous number of episodes with MDD, and user behavior were associated with change in residual cognitive symptoms. CONCLUSION This study suggests that individuals with shorter duration of previous depressions might have larger reductions in residual cognitive symptoms at 6-month follow-up compared to those with a longer duration of depression. Treatment credibility and expectancy also predicted treatment response and effort should also be made to ensure interventions credibility. Results should be interpreted with caution due to the study having a low sample size. Further investigation of predictors should be conducted in a full scale randomized controlled trial.
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Affiliation(s)
- Sunniva Brurok Myklebost
- Department of Biological and Medical Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway
| | - Rolf Gjestad
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Yavuz Inal
- Department of Design, Faculty of Architecture and Design, Norwegian University of Science and Technology, Gjøvik, Norway
| | - Åsa Hammar
- Department of Biological and Medical Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway.,Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Tine Nordgreen
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway.,Department of Global Public Health and Primary Care, Faculty of Medicine, University of Bergen, Bergen, Norway
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Gasslander N, Alfonsson S, Jackalin A, Tengberg C, Håkansson J, Huotari L, Buhrman M. Predictors of adherence to an internet-based cognitive behavioral therapy program for individuals with chronic pain and comorbid psychological distress. BMC Psychol 2021; 9:156. [PMID: 34641946 PMCID: PMC8507117 DOI: 10.1186/s40359-021-00663-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 09/22/2021] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND The burden caused by chronic pain is significant, affecting at least 10 percent of the world´s population. While internet-based treatments based on cognitive behavioral therapy (CBT) have been shown to be promising in this area, attrition levels vary significantly. The purpose of this study was to investigate predictor variables for participants' adherence to an internet-based CBT treatment for individuals with chronic pain as well as to investigate associations between adherence and treatment outcome. METHODS Data for this study was retrieved from a randomized controlled trial including 95 individuals with chronic pain who received internet-based CBT. Treatment adherence was studied through three outcome variables: treatment progress, treatment completion and exercise completion. The predictor variables were grouped into four clusters: background variables (age, gender, marital status, level of education, and typical computer usage); the second cluster included health status variables (sick leave, current psychiatric diagnosis, previous psychotherapy for pain, current pharmacological treatment, previous depression, current depression, and current depressive symptoms); the third cluster included pain-related variables (opioid medication, history of pain, and pain symptoms) and the fourth cluster included motivation variables (measured with treatment preference, treatment credibility, compliance to the treatment schedule and contact with the therapists). RESULTS Findings showed that treatment progress was predicted by higher treatment credibility at baseline, whereas participants who were behind schedule in the second week of the program finished fewer treatment modules. When analyzing each cluster of predictor variables separately, current depressive symptoms also predicted fewer completed treatment modules. Among the pain-related variables, higher pain acceptance was the only predictor for completing more treatment modules. Treatment completion (which in this study was defined as having completed at least 75% of treatment modules) was predicted by higher treatment credibility and fewer depressive symptoms at baseline, and was thus similar to the results regarding treatment progress. Finally, all adherence variables predicted the treatment outcome pain interference. CONCLUSIONS Low treatment credibility, depressive symptoms and falling behind the treatment schedule early on were the most important predictor variables for low treatment adherence, while a number of demographical and pain-related variables were not related to adherence. The results from this study may help clinicians identify patients who are less likely to complete, and thus benefit from, their pain treatment. Trial registration ClinicalTrials.gov NTC03316846.
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Affiliation(s)
- Nils Gasslander
- Department of Psychology, Uppsala University, P.O. Box 1225, 751 42, Uppsala, Sweden.
| | - Sven Alfonsson
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institute & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Amanda Jackalin
- Department of Psychology, Uppsala University, P.O. Box 1225, 751 42, Uppsala, Sweden
| | - Cecilia Tengberg
- Department of Psychology, Uppsala University, P.O. Box 1225, 751 42, Uppsala, Sweden
| | - Johanna Håkansson
- Department of Psychology, Uppsala University, P.O. Box 1225, 751 42, Uppsala, Sweden
| | - Linda Huotari
- Department of Psychology, Uppsala University, P.O. Box 1225, 751 42, Uppsala, Sweden
| | - Monica Buhrman
- Department of Psychology, Uppsala University, P.O. Box 1225, 751 42, Uppsala, Sweden
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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 2021; 47:795-843. [PMID: 32715427 PMCID: PMC7382706 DOI: 10.1007/s10488-020-01065-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [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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25
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Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021; 20:154-170. [PMID: 34002503 PMCID: PMC8129866 DOI: 10.1002/wps.20882] [Citation(s) in RCA: 153] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.
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Affiliation(s)
- Adam M Chekroud
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Spring Health, New York City, NY, USA
| | | | - Jaime Delgadillo
- Clinical Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, UK
| | - Gavin Doherty
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Akash Wasil
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Marjolein Fokkema
- Department of Methods and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Zachary Cohen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Robert DeRubeis
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel Iniesta
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Karmel Choi
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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26
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Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer. ALGORITHMS 2021. [DOI: 10.3390/a14050147] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We used machine learning (ML) to predict individual responses to a two-year course of bicalutamide, a standard treatment for prostate cancer, based on data from three Phase III clinical trials (n = 3653). We developed models that used a merged dataset from all three studies. The best performing models using merged data from all three studies had an accuracy of 76%. The performance of these models was confirmed by further modeling using a merged dataset from two of the three studies, and a separate study for testing. Together, our results indicate the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and improving the efficiency of clinical-stage drug development.
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Sampson L, Jiang T, Gradus JL, Cabral HJ, Rosellini AJ, Calabrese JR, Cohen GH, Fink DS, King AP, Liberzon I, Galea S. A Machine Learning Approach to Predicting New-onset Depression in a Military Population. PSYCHIATRIC RESEARCH AND CLINICAL PRACTICE 2021; 3:115-122. [PMID: 34734165 PMCID: PMC8562467 DOI: 10.1176/appi.prcp.20200031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/04/2020] [Accepted: 12/05/2020] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Depression is one of the most common mental disorders in the United States in both civilian and military populations, but few prospective studies assess a wide range of predictors across multiple domains for new-onset (incident) depression in adulthood. Supervised machine learning methods can identify predictors of incident depression out of many different candidate variables, without some of the assumptions and constraints that underlie traditional regression analyses. The objectives of this study were to identify predictors of incident depression across 5 years of follow-up using machine learning, and to assess prediction accuracy of the algorithms. METHODS Data were from a cohort of Army National Guard members free of history of depression at baseline (n = 1951 men and 298 women), interviewed once per year for probable depression. Classification trees and random forests were constructed and cross-validated, using 84 candidate predictors from the baseline interviews. RESULTS Stressors and traumas such as emotional mistreatment and adverse childhood experiences, demographics such as being a parent or student, and military characteristics including paygrade and deployment location were predictive of probable depression. Cross-validated random forest algorithms were moderately accurate (68% for women and 73% for men). CONCLUSIONS Events and characteristics throughout the life course, both in and outside of deployment, predict incident depression in adulthood among military personnel. Although replication studies are needed, these results may help inform potential intervention targets to reduce depression incidence among military personnel. Future research should further refine and explore interactions between identified variables.
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Affiliation(s)
- Laura Sampson
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
- Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | - Tammy Jiang
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
| | - Jaimie L. Gradus
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
| | - Howard J. Cabral
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Anthony J. Rosellini
- Department of Psychological and Brain Science, Center for Anxiety and Related DisordersBoston UniversityBostonMassachusettsUSA
| | - Joseph R. Calabrese
- Department of PsychiatrySchool of MedicineCase Western Reserve UniversityClevelandOhioUSA
| | - Gregory H. Cohen
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
- Department of EpidemiologyMailman School of Public HealthColumbia UniversityNew YorkNew YorkUSA
| | - David S. Fink
- Department of EpidemiologyMailman School of Public HealthColumbia UniversityNew YorkNew YorkUSA
| | - Anthony P. King
- Department of PsychiatryUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Israel Liberzon
- Department of PsychiatryTexas A&M College of MedicineCollege StationTexasUSA
| | - Sandro Galea
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
- Office of the DeanBoston University School of Public HealthBostonMassachusettsUSA
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Zhang G, Ashrafi RA, Juuti A, Pietilainen K, Marttinen P. Errors-in-Variables Modeling of Personalized Treatment-Response Trajectories. IEEE J Biomed Health Inform 2021; 25:201-208. [PMID: 32324579 DOI: 10.1109/jbhi.2020.2987323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Estimating the impact of a treatment on a given response is needed in many biomedical applications. However, methodology is lacking for the case when the response is a continuous temporal curve, treatment covariates suffer extensively from measurement error, and even the exact timing of the treatments is unknown. We introduce a novel method for this challenging scenario. We model personalized treatment-response curves as a combination of parametric response functions, hierarchically sharing information across individuals, and a sparse Gaussian process for the baseline trend. Importantly, our model accounts for errors not only in treatment covariates, but also in treatment timings, a problem arising in practice for example when data on treatments are based on user self-reporting. We validate our model with simulated and real patient data, and show that in a challenging application of estimating the impact of diet on continuous blood glucose measurements, accounting for measurement error significantly improves estimation and prediction accuracy.
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29
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Jacobson NC, Nemesure MD. Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial. Psychiatry Res 2021; 295:113618. [PMID: 33278743 PMCID: PMC7839310 DOI: 10.1016/j.psychres.2020.113618] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 11/26/2020] [Indexed: 11/28/2022]
Abstract
While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preemptively identify who is likely to benefit from these digital treatments in order to redirect those people to a higher level of care. The current manuscript used an ensemble of machine learning methods to predict changes in major depressive and generalized anxiety disorder symptoms from pre to 9-month follow-up in a randomized controlled trial of a transdiagnostic digital intervention based on participants' (N=632) pre-treatment data. The results suggested that baseline characteristics could accurately predict changes in depressive symptoms in both treatment groups (r=0.482, 95% CI[0.394, 0.561]; r=0.477, 95% CI[0.385, 0.560]) and anxiety symptoms in both treatment groups (r=0.569, 95% CI[0.491, 0.638]; r=0.548, 95% CI[0.464, 0.622]). These results suggest that machine learning models are capable of preemptively predicting a person's responsiveness to digital treatments, which would enable personalized decision-making about which persons should be directed towards standalone digital interventions or towards blended stepped-care.
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Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach. BMC Psychiatry 2020; 20:247. [PMID: 32429939 PMCID: PMC7238519 DOI: 10.1186/s12888-020-02655-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 05/05/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. METHODS This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. RESULTS Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68, 66 and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. CONCLUSIONS The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. TRIAL REGISTRATION ClinicalTrials.gov ID: NCT02010619.
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Mullarkey MC, Stein AT, Pearson R, Beevers CG. Network analyses reveal which symptoms improve (or not) following an Internet intervention (Deprexis) for depression. Depress Anxiety 2020; 37:115-124. [PMID: 31710772 PMCID: PMC6992506 DOI: 10.1002/da.22972] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 09/15/2019] [Accepted: 10/31/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Depression is a heterogeneous collection of symptoms. Prior meta-analyses using symptom sum scores have shown the Internet intervention, Deprexis, to be an efficacious treatment for depression. However, no prior research has investigated how Deprexis (or any other Internet intervention for depression) impacts specific symptoms of depression. The current study utilizes symptom-level analyses to examine which symptoms are directly, indirectly, or minimally influenced by treatment. METHODS Network analysis and mean-level approaches examined which symptoms, assessed by the Quick Inventory of Depression Symptoms, were affected by an 8-week course of Deprexis compared with a waitlist in a nationally recruited sample from the United States (N = 295). RESULTS Deprexis directly improved the symptoms of sadness and indecision. Changes in these symptoms, in turn, was associated with a change in early insomnia, middle insomnia, self-dislike, fatigue, anhedonia, suicidality, slowness, and agitation. All of these symptoms (except for agitation and early insomnia) show decreases with Deprexis compared with a waitlist after correcting for multiple comparisons. Six additional symptoms, particularly the somatic symptoms, were not impacted by Deprexis compared with a waitlist. CONCLUSIONS In this sample, the efficacy of Deprexis was due to its direct impact on sadness and indecision. Examining the treatment-related change in specific symptoms may facilitate a more nuanced understanding of how a treatment works compared with examining symptom sum scores. Symptom-level approaches may also identify symptoms that do not improve and provide important direction for future treatment development.
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Affiliation(s)
- Michael C. Mullarkey
- Department of PsychologyInstitute for Mental Health ResearchUniversity of Texas at Austin Austin Texas
| | - Aliza T. Stein
- Department of PsychologyInstitute for Mental Health ResearchUniversity of Texas at Austin Austin Texas
| | - Rahel Pearson
- Department of PsychologyInstitute for Mental Health ResearchUniversity of Texas at Austin Austin Texas
| | - Christopher G. Beevers
- Department of PsychologyInstitute for Mental Health ResearchUniversity of Texas at Austin Austin Texas
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32
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Shayesteh SP, Alikhassi A, Fard Esfahani A, Miraie M, Geramifar P, Bitarafan-Rajabi A, Haddad P. Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients. Phys Med 2019; 62:111-119. [PMID: 31153390 DOI: 10.1016/j.ejmp.2019.03.013] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 02/23/2019] [Accepted: 03/17/2019] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVES The aim of this study was to investigate and validate the performance of individual and ensemble machine learning models (EMLMs) based on magnetic resonance imaging (MRI) to predict neo-adjuvant chemoradiation therapy (nCRT) response in rectal cancer patients. We also aimed to study the effect of Laplacian of Gaussian (LOG) filter on EMLMs predictive performance. METHODS 98 rectal cancer patients were divided into a training (n = 53) and a validation set (n = 45). All patients underwent MRI a week before nCRT. Several features from intensity, shape and texture feature sets were extracted from MR images. SVM, Bayesian network, neural network and KNN classifiers were used individually and together for response prediction. Predictive performance was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC). RESULTS Patients' nCRT responses included 17 patients with Grade 0, 28 with Grade 1, 34 with Grade 2, and 19 with Grade 3 according to AJCC/CAP pathologic grading. In without preprocessing MR Image the best result was for Bayesian network classifier with AUC and accuracy of 75.2% and 80.9% respectively, which was confirmed in the validation set with an AUC and accuracy of 74% and 79% respectively. In EMLMs the best result was for 4 (SVM.NN.BN.KNN) classifier EMLM with AUC and accuracy of 97.8% and 92.8% in testing and 95% and 90% in validation set respectively. CONCLUSIONS In conclusion, we observed that machine learning methods can used to predict nCRT response in patients with rectal cancer. Preprocessing LOG filters and EL models can improve the prediction process.
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Affiliation(s)
- Sajad P Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Faculty of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Afsaneh Alikhassi
- Department of Radiology, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Armaghan Fard Esfahani
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - M Miraie
- Cancer Research Centre & Radiation Oncology Department, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmad Bitarafan-Rajabi
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran; Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Peiman Haddad
- Radiation Oncology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran.
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