1
|
Ting LH, Gick B, Kesar TM, Xu J. Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230485. [PMID: 39155720 DOI: 10.1098/rstb.2023.0485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 05/15/2024] [Accepted: 06/18/2024] [Indexed: 08/20/2024] Open
Abstract
Each individual's movements are sculpted by constant interactions between sensorimotor and sociocultural factors. A theoretical framework grounded in motor control mechanisms articulating how sociocultural and biological signals converge to shape movement is currently missing. Here, we propose a framework for the emerging field of ethnokinesiology aiming to provide a conceptual space and vocabulary to help bring together researchers at this intersection. We offer a first-level schema for generating and testing hypotheses about cultural differences in movement to bridge gaps between the rich observations of cross-cultural movement variations and neurophysiological and biomechanical accounts of movement. We explicitly dissociate two interacting feedback loops that determine culturally relevant movement: one governing sensorimotor tasks regulated by neural signals internal to the body, the other governing ecological tasks generated through actions in the environment producing ecological consequences. A key idea is the emergence of individual-specific and culturally influenced motor concepts in the nervous system, low-dimensional functional mappings between sensorimotor and ecological task spaces. Motor accents arise from perceived differences in motor concept topologies across cultural contexts. We apply the framework to three examples: speech, gait and grasp. Finally, we discuss how ethnokinesiological studies may inform personalized motor skill training and rehabilitation, and challenges moving forward.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.
Collapse
Affiliation(s)
- Lena H Ting
- Coulter Department of Biomedical Engineering at Georgia Tech and Emory, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, GA 30322, USA
| | - Bryan Gick
- Department of Linguistics, The University British Columbia, Vancouver, BC V6T 1Z4, Canada
- Haskins Laboratories, Yale University, New Haven, CT 06520, USA
| | - Trisha M Kesar
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, GA 30322, USA
| | - Jing Xu
- Department of Kinesiology, The University of Georgia, Athens, GA 30602, USA
| |
Collapse
|
2
|
Calderon A, Baik SY, Ng MHS, Fitzsimmons-Craft EE, Eisenberg D, Wilfley DE, Taylor CB, Newman MG. Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students. BMC Psychiatry 2024; 24:656. [PMID: 39367432 PMCID: PMC11452987 DOI: 10.1186/s12888-024-06074-7] [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: 02/09/2024] [Accepted: 09/11/2024] [Indexed: 10/06/2024] Open
Abstract
BACKGROUND A better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated. METHODS The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD). RESULTS Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes. CONCLUSION These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression. TRIAL REGISTRATION Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).
Collapse
Affiliation(s)
- Adam Calderon
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA.
| | - Seung Yeon Baik
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| | - Matthew H S Ng
- Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
| | | | - Daniel Eisenberg
- Department of Health Policy and Management, University of California-Los Angeles, Los Angeles, CA, USA
| | - Denise E Wilfley
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - C Barr Taylor
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Center for m2Health, Palo Alto University, Los Altos, CA, USA
| | - Michelle G Newman
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| |
Collapse
|
3
|
Muñoz Duque LA, Estrada Acuña RA, Munn T, Bañol Muñoz LC, Johnson S, Gilbert MR, Hayes-Conroy A. What matters beyond particle matter?: Examining air pollution's synergistic effects on bodies and health through Bio 3Science in Medellin. Soc Sci Med 2024; 361:117331. [PMID: 39368407 DOI: 10.1016/j.socscimed.2024.117331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 08/03/2024] [Accepted: 09/10/2024] [Indexed: 10/07/2024]
Abstract
Scientific literature on the health effects of air pollution is diverse, and broadly acknowledges the importance of human experience and social and economic precarity as modifying factors. Still, the inclusion of the embodied experience of air pollution has been limited. Also, the health effects of pollution are often studied at the group or population level, without adequately considering individual difference. This paper uses a Bio3Science framework, which integrates biology, biography, and biosphere, to explore how air pollution affects residents in Medellín, Colombia. By using qualitative research on individual experiences of air pollution (biography) to probe the intersection of individual health (biology) and environment (biosphere), we illustrate how pollution shapes lived rhythms at multiple scales. Our findings emphasize that air pollution's health impacts extend beyond measurable pollutants to include the complex synergies of smoke, noise, stress, and disruptions to daily life. This comprehensive approach provides a nuanced understanding of how air pollution materially shapes the lives of individuals and communities, advocating for research models that capture the subtle, everyday experiences often overlooked by traditional group or population-level analyses.
Collapse
Affiliation(s)
| | | | - Tyler Munn
- Temple University, Philadelphia, PA, USA
| | | | | | | | | |
Collapse
|
4
|
Myers CE, Dave CV, Chesin MS, Marx BP, St Hill LM, Reddy V, Miller RB, King A, Interian A. Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention. Behav Res Ther 2024; 183:104637. [PMID: 39306938 DOI: 10.1016/j.brat.2024.104637] [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] [Received: 03/08/2024] [Revised: 08/09/2024] [Accepted: 09/16/2024] [Indexed: 09/26/2024]
Abstract
OBJECTIVE Develop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT-S) versus enhanced treatment-as-usual (eTAU). METHODS Analyses used data from Veterans at high-risk for suicide assigned to either MBCT-S (n = 71) or eTAU (n = 69) in a randomized clinical trial. Potential predictors (n = 55) included available demographic, clinical, and neurocognitive variables. Random forest models were used to predict risk of suicidal event (suicidal behaviors, or ideation resulting in hospitalization or emergency department visit) within 12 months following randomization, characterize the prediction, and develop a Personalized Advantage Index (PAI). RESULTS A slightly better prediction model emerged for MBCT-S (AUC = 0.70) than eTAU (AUC = 0.63). Important outcome predictors for participants in the MBCT-S arm included PTSD diagnosis, decisional efficiency on a neurocognitive task (Go/No-Go), prior-year mental health residential treatment, and non-suicidal self-injury. Significant predictors for participants in the eTAU arm included past-year acute psychiatric hospitalizations, past-year outpatient psychotherapy visits, past-year suicidal ideation severity, and attentional control (indexed by Stroop task). A moderation analysis showed that fewer suicidal events occurred among those randomized to their PAI-indicated optimal treatment. CONCLUSIONS PAI-guided treatment assignment may enhance suicide prevention outcomes. However, prior to real-world application, additional research is required to improve model accuracy and evaluate model generalization.
Collapse
Affiliation(s)
- Catherine E Myers
- Research and Development Service, VA New Jersey Health Care System, East Orange, NJ, USA; Department of Pharmacology, Physiology & Neuroscience, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, USA
| | - Chintan V Dave
- Center for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, USA
| | - Megan S Chesin
- Department of Psychology, William Paterson University, USA
| | - Brian P Marx
- National Center for PTSD, Behavioral Sciences Division at the VA Boston Health Care System, Boston, MA, USA; Boston University School of Medicine, Boston, MA, USA
| | - Lauren M St Hill
- Mental Health and Behavioral Sciences, VA New Jersey Health Care System, Lyons, NJ, USA
| | - Vibha Reddy
- Research and Development Service, VA New Jersey Health Care System, East Orange, NJ, USA
| | - Rachael B Miller
- Mental Health and Behavioral Sciences, VA New Jersey Health Care System, Lyons, NJ, USA
| | - Arlene King
- Mental Health and Behavioral Sciences, VA New Jersey Health Care System, Lyons, NJ, USA
| | - Alejandro Interian
- Mental Health and Behavioral Sciences, VA New Jersey Health Care System, Lyons, NJ, USA; Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
| |
Collapse
|
5
|
Scott I, Nelson B. Are Psychiatric Nosologies Limiting the Success of Clinical Prediction Models? JAMA Psychiatry 2024:2823667. [PMID: 39292481 DOI: 10.1001/jamapsychiatry.2024.2662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/19/2024]
Abstract
This Viewpoint discusses the limitations of clinical prediction models in psychiatric research.
Collapse
Affiliation(s)
- Isabelle Scott
- Centre for Youth Mental Health, The University of Melbourne, Parkville, Melbourne, Victoria, Australia
- Orygen, Parkville, Melbourne, Victoria, Australia
| | - Barnaby Nelson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, Melbourne, Victoria, Australia
- Orygen, Parkville, Melbourne, Victoria, Australia
| |
Collapse
|
6
|
Morgan G, Nau AN, Wong S, Spencer BH, Shen Y, Hua A, Bullard MJ, Sanchorawala V, Prokaeva T. An updated AL-Base reveals ranked enrichment of immunoglobulin light chain variable genes in AL amyloidosis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.11.612490. [PMID: 39314448 PMCID: PMC11419035 DOI: 10.1101/2024.09.11.612490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Background Each monoclonal antibody light chain associated with AL amyloidosis has a unique sequence. Defining how these sequences lead to amyloid deposition could facilitate faster diagnosis and lead to new treatments. Methods Light chain sequences are collected in the Boston University AL-Base repository. Monoclonal sequences from AL amyloidosis, multiple myeloma and the healthy polyclonal immune repertoire were compared to identify differences in precursor gene use, mutation frequency and physicochemical properties. Results AL-Base now contains 2,193 monoclonal light chain sequences from plasma cell dyscrasias. Sixteen germline precursor genes were enriched in AL amyloidosis, relative to multiple myeloma and the polyclonal repertoire. Two genes, IGKV1-16 and IGLV1-36, were infrequently observed but highly enriched in AL amyloidosis. The number of mutations varied widely between light chains. AL-associated κ light chains harbored significantly more mutations compared to multiple myeloma and polyclonal sequences, whereas AL-associated λ light chains had fewer mutations. Machine learning tools designed to predict amyloid propensity were less accurate for new sequences than their original training data. Conclusions Rarely-observed light chain variable genes may carry a high risk of AL amyloidosis. New approaches are needed to define sequence-associated risk factors for AL amyloidosis. AL-Base is a foundational resource for such studies.
Collapse
Affiliation(s)
- Gareth Morgan
- Boston University Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
- Section of Hematology and Medical Oncology, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
| | - Allison N Nau
- Boston University Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
| | - Sherry Wong
- Boston University Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
| | - Brian H Spencer
- Boston University Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
| | - Yun Shen
- Boston University Research Computing Services, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
| | - Axin Hua
- Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
| | - Matthew J Bullard
- Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
| | - Vaishali Sanchorawala
- Boston University Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
- Section of Hematology and Medical Oncology, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
| | - Tatiana Prokaeva
- Boston University Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Campus, 72 E. Concord St, Boston, MA 02118, USA
| |
Collapse
|
7
|
Miller C, Portlock T, Nyaga DM, O'Sullivan JM. A review of model evaluation metrics for machine learning in genetics and genomics. FRONTIERS IN BIOINFORMATICS 2024; 4:1457619. [PMID: 39318760 PMCID: PMC11420621 DOI: 10.3389/fbinf.2024.1457619] [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: 07/01/2024] [Accepted: 08/27/2024] [Indexed: 09/26/2024] Open
Abstract
Machine learning (ML) has shown great promise in genetics and genomics where large and complex datasets have the potential to provide insight into many aspects of disease risk, pathogenesis of genetic disorders, and prediction of health and wellbeing. However, with this possibility there is a responsibility to exercise caution against biases and inflation of results that can have harmful unintended impacts. Therefore, researchers must understand the metrics used to evaluate ML models which can influence the critical interpretation of results. In this review we provide an overview of ML metrics for clustering, classification, and regression and highlight the advantages and disadvantages of each. We also detail common pitfalls that occur during model evaluation. Finally, we provide examples of how researchers can assess and utilise the results of ML models, specifically from a genomics perspective.
Collapse
Affiliation(s)
- Catriona Miller
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Theo Portlock
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Denis M Nyaga
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
| |
Collapse
|
8
|
Guerreiro J, Garriga R, Lozano Bagén T, Sharma B, Karnik NS, Matić A. Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises. NPJ Digit Med 2024; 7:227. [PMID: 39251868 PMCID: PMC11384787 DOI: 10.1038/s41746-024-01203-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 07/29/2024] [Indexed: 09/11/2024] Open
Abstract
Transferring and replicating predictive algorithms across healthcare systems constitutes a unique yet crucial challenge that needs to be addressed to enable the widespread adoption of machine learning in healthcare. In this study, we explored the impact of important differences across healthcare systems and the associated Electronic Health Records (EHRs) on machine-learning algorithms to predict mental health crises, up to 28 days in advance. We evaluated both the transferability and replicability of such machine learning models, and for this purpose, we trained six models using features and methods developed on EHR data from the Birmingham and Solihull Mental Health NHS Foundation Trust in the UK. These machine learning models were then used to predict the mental health crises of 2907 patients seen at the Rush University System for Health in the US between 2018 and 2020. The best one was trained on a combination of US-specific structured features and frequency features from anonymized patient notes and achieved an AUROC of 0.837. A model with comparable performance, originally trained using UK structured data, was transferred and then tuned using US data, achieving an AUROC of 0.826. Our findings establish the feasibility of transferring and replicating machine learning models to predict mental health crises across diverse hospital systems.
Collapse
Affiliation(s)
| | - Roger Garriga
- Koa Health, Barcelona, Spain
- Universitat Pompeu Fabra, Department of Information and Communication Technologies, Barcelona, Spain
| | | | | | | | | |
Collapse
|
9
|
Etkin A, Mathalon DH. Bringing Imaging Biomarkers Into Clinical Reality in Psychiatry. JAMA Psychiatry 2024:2822966. [PMID: 39230917 DOI: 10.1001/jamapsychiatry.2024.2553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Importance Advancing precision psychiatry, where treatments are based on an individual's biology rather than solely their clinical presentation, requires attention to several key attributes for any candidate biomarker. These include test-retest reliability, sensitivity to relevant neurophysiology, cost-effectiveness, and scalability. Unfortunately, these issues have not been systematically addressed by biomarker development efforts that use common neuroimaging tools like magnetic resonance imaging (MRI) and electroencephalography (EEG). Here, the critical barriers that neuroimaging methods will need to overcome to achieve clinical relevance in the near to intermediate term are examined. Observations Reliability is often overlooked, which together with sensitivity to key aspects of neurophysiology and replicated predictive utility, favors EEG-based methods. The principal barrier for EEG has been the lack of large-scale data collection among multisite psychiatric consortia. By contrast, despite its high reliability, structural MRI has not demonstrated clinical utility in psychiatry, which may be due to its limited sensitivity to psychiatry-relevant neurophysiology. Given the prevalence of structural MRIs, establishment of a compelling clinical use case remains its principal barrier. By contrast, low reliability and difficulty in standardizing collection are the principal barriers for functional MRI, along with the need for demonstration that its superior spatial resolution over EEG and ability to directly image subcortical regions in fact provide unique clinical value. Often missing, moreover, is consideration of how these various scientific issues can be balanced against practical economic realities of psychiatric health care delivery today, for which embedding economic modeling into biomarker development efforts may help direct research efforts. Conclusions and Relevance EEG seems most ripe for near- to intermediate-term clinical impact, especially considering its scalability and cost-effectiveness. Recent efforts to broaden its collection, as well as development of low-cost turnkey systems, suggest a promising pathway by which neuroimaging can impact clinical care. Continued MRI research focused on its key barriers may hold promise for longer-horizon utility.
Collapse
Affiliation(s)
- Amit Etkin
- Alto Neuroscience Inc, Los Altos, California
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
- Veterans Affairs San Francisco Health Care System, San Francisco, California
| |
Collapse
|
10
|
Goerigk S, Elsaesser M, Reinhard MA, Kriston L, Härter M, Hautzinger M, Klein JP, McCullough JP, Schramm E, Padberg F. Childhood Trauma Questionnaire-based child maltreatment profiles to predict efficacy of the Cognitive Behavioral Analysis System of Psychotherapy versus non-specific psychotherapy in adults with early-onset chronic depression: cluster analysis of data from a randomised controlled trial. Lancet Psychiatry 2024; 11:709-719. [PMID: 39147459 DOI: 10.1016/s2215-0366(24)00209-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 08/17/2024]
Abstract
BACKGROUND Child maltreatment is a broadly confirmed risk factor for mental and physical illness. Some psychological treatments specifically target mental health conditions associated with child maltreatment. For example, the Cognitive Behavioral Analysis System of Psychotherapy (CBASP) focuses on maladaptive interpersonal behaviours in chronic depression. However, how the assessment of child maltreatment could inform personalised treatment is unclear. We used data from a previously published clinical trial to investigate whether a pre-established child maltreatment clustering approach predicts differential outcomes after CBASP versus non-specific supportive psychotherapy in patients with early-onset chronic depression. METHODS We did a cluster analysis of data from a previous randomised controlled trial of unmedicated adult outpatients with early-onset chronic depression who were treated at eight university clinics and psychological institutes in Germany with 32 sessions of CBASP or non-specific supportive psychotherapy. Participants were eligible for the original trial if they were aged 18-65 years; had major depressive disorder (MDD) with an early onset and duration of at least 2 years, current MDD superimposed on a pre-existing dysthymic disorder, or recurrent MDD with incomplete remission between episodes as defined by DSM-IV; and had a score of at least 20 points on the 24-item Hamilton Rating Scale for Depression (HRSD-24). Participants were included in the current study if they had completed the short form of the Childhood Trauma Questionnaire (CTQ) at trial baseline. We used an agglomerative hierarchical clustering approach to derive child maltreatment clusters from individual patterns across the five domains of the CTQ. We used linear mixed models to investigate whether clustering could predict differential clinical outcomes (change in symptom severity on the HRSD-24) up to 2 years after treatment onset. People with lived experience were involved in the current study. FINDINGS 253 patients (129 [51%] treated with CBASP and 124 [49%] with supportive psychotherapy) had complete CTQ records and were included in the analysis. 169 (67%) participants were women, 84 (33%) were men, and the mean age was 45·9 years (SD 11·7). We identified seven child maltreatment clusters and found significant differences in treatment effects of CBASP and supportive psychotherapy between the clusters (F(6,948·76)=2·47; p=0·023); differences were maintained over the 2-year follow-up. CBASP was superior in distinct clusters of co-occurring child maltreatment: predominant emotional neglect (change in β -6·02 [95% CI -11·9 to -0·13]; Cohen's d=-0·98 [95% CI -1·94 to -0·02]; p=0·045), predominant emotional neglect and abuse (-6·39 [-10·22 to -2·56]; -1·04 [-1·67 to -0·42]; p=0·0011), and emotional neglect and emotional and physical abuse (-9·41 [-15·91 to -2·91]; -1·54 [-2·6 to -0·47]; p=0·0046). INTERPRETATION CTQ-based cluster analysis can facilitate identification of patients with early-onset chronic depression who would specifically benefit from CBASP. Child maltreatment clusters could be implemented in clinical assessments and serve to develop and personalise trauma-informed care in mental health. FUNDING The German Research Foundation and the German Federal Ministry of Education and Research.
Collapse
Affiliation(s)
- Stephan Goerigk
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany; Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany; Department of Psychology, Charlotte Fresenius Hochschule, Munich, Germany; DZPG (German Center for Mental Health), Partner Site Munich-Augsburg, Munich, Germany
| | - Moritz Elsaesser
- Department of Psychiatry and Psychotherapy, University Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias A Reinhard
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany; DZPG (German Center for Mental Health), Partner Site Munich-Augsburg, Munich, Germany
| | - Levente Kriston
- Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martin Härter
- Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martin Hautzinger
- Department of Psychology, Clinical Psychology, and Psychotherapy, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Jan Philipp Klein
- Department of Psychiatry and Psychotherapy, Lübeck University, Lübeck, Germany
| | | | - Elisabeth Schramm
- Department of Psychiatry and Psychotherapy, University Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Frank Padberg
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany; DZPG (German Center for Mental Health), Partner Site Munich-Augsburg, Munich, Germany.
| |
Collapse
|
11
|
Berisha V, Liss JM. Responsible development of clinical speech AI: Bridging the gap between clinical research and technology. NPJ Digit Med 2024; 7:208. [PMID: 39122889 PMCID: PMC11316053 DOI: 10.1038/s41746-024-01199-1] [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] [Received: 11/23/2023] [Accepted: 07/19/2024] [Indexed: 08/12/2024] Open
Abstract
This perspective article explores the challenges and potential of using speech as a biomarker in clinical settings, particularly when constrained by the small clinical datasets typically available in such contexts. We contend that by integrating insights from speech science and clinical research, we can reduce sample complexity in clinical speech AI models with the potential to decrease timelines to translation. Most existing models are based on high-dimensional feature representations trained with limited sample sizes and often do not leverage insights from speech science and clinical research. This approach can lead to overfitting, where the models perform exceptionally well on training data but fail to generalize to new, unseen data. Additionally, without incorporating theoretical knowledge, these models may lack interpretability and robustness, making them challenging to troubleshoot or improve post-deployment. We propose a framework for organizing health conditions based on their impact on speech and promote the use of speech analytics in diverse clinical contexts beyond cross-sectional classification. For high-stakes clinical use cases, we advocate for a focus on explainable and individually-validated measures and stress the importance of rigorous validation frameworks and ethical considerations for responsible deployment. Bridging the gap between AI research and clinical speech research presents new opportunities for more efficient translation of speech-based AI tools and advancement of scientific discoveries in this interdisciplinary space, particularly if limited to small or retrospective datasets.
Collapse
Affiliation(s)
- Visar Berisha
- School of Electrical Computer and Energy Engineering and College of Health Solutions, Arizona State University, Tempe, AZ, USA.
| | - Julie M Liss
- College of Health Solutions, Arizona State University, Tempe, AZ, USA
| |
Collapse
|
12
|
Jahanshad N, Lenzini P, Bijsterbosch J. Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01938-8. [PMID: 39117903 DOI: 10.1038/s41386-024-01938-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/05/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024]
Abstract
Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.
Collapse
Affiliation(s)
- Neda Jahanshad
- Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90292, USA.
| | - Petra Lenzini
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Janine Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
| |
Collapse
|
13
|
Falkai P, Koutsouleris N. Why is it so difficult to implement precision psychiatry into clinical care? THE LANCET REGIONAL HEALTH. EUROPE 2024; 43:100952. [PMID: 38883308 PMCID: PMC11169462 DOI: 10.1016/j.lanepe.2024.100952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/18/2024]
Affiliation(s)
- Peter Falkai
- Department of Psychiatry, Munich University Hospital, Munich, Germany
- Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry, Munich University Hospital, Munich, Germany
- Max-Planck-Institute of Psychiatry, Munich, Germany
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| |
Collapse
|
14
|
McLoughlin A. Psychiatry and psychotherapy: the science of sitting with uncertainty. Ir J Med Sci 2024; 193:1945-1947. [PMID: 38652409 PMCID: PMC11294434 DOI: 10.1007/s11845-024-03687-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 04/14/2024] [Indexed: 04/25/2024]
Abstract
OBJECTIVE This perspective piece aims to delve into the challenges and possibilities arising from uncertainty in psychiatry and psychotherapy. METHOD This is a perspective piece. RESULTS Historical considerations, polarised conceptual frameworks, interacting systems, limited randomised controlled research, and varying practice approaches, coalesce to form an exoskeleton of ambiguity and uncertainty in psychiatry and psychotherapy that seems almost impenetrable. Yet it is these very things that allow psychiatry to challenge accepted norms, avoid complacency, and shed its skin. CONCLUSION Ambiguity and uncertainty in the setting of partial knowledge presents psychiatry with a challenge, yes; but it also enables psychiatry to be innovative in its approach, and to address the complexities of the human condition in a way that is unique and unparalleled within the field of medicine.
Collapse
|
15
|
Zang C, Hou Y, Lyu D, Jin J, Sacco S, Chen K, Aseltine R, Wang F. Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records. Transl Psychiatry 2024; 14:316. [PMID: 39085206 PMCID: PMC11291985 DOI: 10.1038/s41398-024-03034-3] [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: 04/07/2023] [Revised: 07/15/2024] [Accepted: 07/23/2024] [Indexed: 08/02/2024] Open
Abstract
Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transportability, namely the performance of a model trained on one dataset and applied to different data, is largely unknown, hindering the clinical adoption of these models. Here we developed different machine learning-based suicide prediction models based on real-world data collected in different contexts (inpatient, outpatient, and all encounters) with varying purposes (administrative claims and electronic health records), and compared their cross-data performance. The three datasets used were the All-Payer Claims Database in Connecticut, the Hospital Inpatient Discharge Database in Connecticut, and the Electronic Health Records data provided by the Kansas Health Information Network. We included 285,320 patients among whom we identified 3389 (1.2%) suicide attempters and 66% of the suicide attempters were female. Different machine learning models were evaluated on source datasets where models were trained and then applied to target datasets. More complex models, particularly deep long short-term memory neural network models, did not outperform simpler regularized logistic regression models in terms of both local and transported performance. Transported models exhibited varying performance, showing drops or even improvements compared to their source performance. While they can achieve satisfactory transported performance, they are usually upper-bounded by the best performance of locally developed models, and they can identify additional new cases in target data. Our study uncovers complex transportability patterns and could facilitate the development of suicide prediction models with better performance and generalizability.
Collapse
Affiliation(s)
- Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, Cornell, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, Cornell, USA
| | - Yu Hou
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, Cornell, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, Cornell, USA
| | - Daoming Lyu
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, Cornell, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, Cornell, USA
| | - Jun Jin
- Department of Statistics, University of Connecticut, Connecticut, USA
| | - Shane Sacco
- Department of Statistics, University of Connecticut, Connecticut, USA
| | - Kun Chen
- Department of Statistics, University of Connecticut, Connecticut, USA.
| | | | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, Cornell, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, Cornell, USA.
| |
Collapse
|
16
|
Ellis SG, Kattan MW. Optimizing the Use of Artificial Intelligence in Cardiology in 2024. JACC Cardiovasc Interv 2024; 17:1717-1718. [PMID: 38970582 DOI: 10.1016/j.jcin.2024.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 05/10/2024] [Indexed: 07/08/2024]
Affiliation(s)
- Stephen G Ellis
- Heart, Thoracic and Vascular Institute, Cleveland Clinic, Cleveland, Ohio, USA.
| | - Michael W Kattan
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
| |
Collapse
|
17
|
Nan J, Grennan G, Ravichandran S, Ramanathan D, Mishra J. Neural activity during inhibitory control predicts suicidal ideation with machine learning. NPP-DIGITAL PSYCHIATRY AND NEUROSCIENCE 2024; 2:10. [PMID: 38988507 PMCID: PMC11230903 DOI: 10.1038/s44277-024-00012-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/04/2024] [Accepted: 06/04/2024] [Indexed: 07/12/2024]
Abstract
Suicide is a leading cause of death in the US and worldwide. Current strategies for preventing suicide are often focused on the identification and treatment of risk factors, especially suicidal ideation (SI). Hence, developing data-driven biomarkers of SI may be key for suicide prevention and intervention. Prior attempts at biomarker-based prediction models for SI have primarily used expensive neuroimaging technologies, yet clinically scalable and affordable biomarkers remain elusive. Here, we investigated the classification of SI using machine learning (ML) on a dataset of 76 subjects with and without SI(+/-) (n = 38 each), who completed a neuro-cognitive assessment session synchronized with electroencephalography (EEG). SI+/- groups were matched for age, sex, and mental health symptoms of depression and anxiety. EEG was recorded at rest and while subjects engaged in four cognitive tasks of inhibitory control, interference processing, working memory, and emotion bias. We parsed EEG signals in physiologically relevant theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequencies and performed cortical source imaging on the neural signals. These data served as SI predictors in ML models. The best ML model was obtained for beta band power during the inhibitory control (IC) task, demonstrating high sensitivity (89%), specificity (98%). Shapley explainer plots further showed top neural predictors as feedback-related power in the visual and posterior default mode networks and response-related power in the ventral attention, fronto-parietal, and sensory-motor networks. We further tested the external validity of the model in an independent clinically depressed sample (n = 35, 12 SI+) that engaged in an adaptive test version of the IC task, demonstrating 50% sensitivity and 61% specificity in this sample. Overall, the study suggests a promising, scalable EEG-based biomarker approach to predict SI that may serve as a target for risk identification and intervention.
Collapse
Affiliation(s)
- Jason Nan
- Neural Engineering and Translation Labs, University of California, San Diego, La Jolla, CA USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
| | - Gillian Grennan
- Neural Engineering and Translation Labs, University of California, San Diego, La Jolla, CA USA
| | - Soumya Ravichandran
- Neural Engineering and Translation Labs, University of California, San Diego, La Jolla, CA USA
| | - Dhakshin Ramanathan
- Neural Engineering and Translation Labs, University of California, San Diego, La Jolla, CA USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA USA
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA USA
- Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, San Diego, CA USA
| | - Jyoti Mishra
- Neural Engineering and Translation Labs, University of California, San Diego, La Jolla, CA USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA USA
- Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, San Diego, CA USA
| |
Collapse
|
18
|
Baumeister H, Vogel JW, Insel PS, Kleineidam L, Wolfsgruber S, Stark M, Gellersen HM, Yakupov R, Schmid MC, Lüsebrink F, Brosseron F, Ziegler G, Freiesleben SD, Preis L, Schneider LS, Spruth EJ, Altenstein S, Lohse A, Fliessbach K, Vogt IR, Bartels C, Schott BH, Rostamzadeh A, Glanz W, Incesoy EI, Butryn M, Janowitz D, Rauchmann BS, Kilimann I, Goerss D, Munk MH, Hetzer S, Dechent P, Ewers M, Scheffler K, Wuestefeld A, Strandberg O, van Westen D, Mattsson-Carlgren N, Janelidze S, Stomrud E, Palmqvist S, Spottke A, Laske C, Teipel S, Perneczky R, Buerger K, Schneider A, Priller J, Peters O, Ramirez A, Wiltfang J, Heneka MT, Wagner M, Düzel E, Jessen F, Hansson O, Berron D. A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings. Brain 2024; 147:2400-2413. [PMID: 38654513 PMCID: PMC11224599 DOI: 10.1093/brain/awae118] [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: 09/07/2023] [Revised: 02/02/2024] [Accepted: 03/03/2024] [Indexed: 04/26/2024] Open
Abstract
Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease.
Collapse
Affiliation(s)
- Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
| | - Philip S Insel
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Melina Stark
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Matthias C Schmid
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Institute for Medical Biometry, University Hospital Bonn, 53127 Bonn, Germany
| | - Falk Lüsebrink
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Gabriel Ziegler
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Silka D Freiesleben
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Lukas Preis
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Luisa-Sophie Schneider
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Eike J Spruth
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Andrea Lohse
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Ina R Vogt
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Björn H Schott
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 37075 Göttingen, Germany
- Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Ayda Rostamzadeh
- Department of Psychiatry, Medical Faculty, University of Cologne, 50937 Cologne, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Enise I Incesoy
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120 Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Michaela Butryn
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität, 81377 Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität, 80336 Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield S10 2HQ, UK
- Department of Neuroradiology, Ludwig-Maximilians-Universität, 81377 Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), 18147 Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147 Rostock, Germany
| | - Doreen Goerss
- German Center for Neurodegenerative Diseases (DZNE), 18147 Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147 Rostock, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Göttingen, 37075 Göttingen, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität, 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, 72076 Tübingen, Germany
| | - Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
| | - Danielle van Westen
- Diagnostic Radiology, Institution of Clinical Sciences Lund, Lund University, 211 84 Lund, Sweden
- Image and Function, Skåne University Hospital, 211 84 Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, 211 84 Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 22184 Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurology, University of Bonn, 53127 Bonn, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research, 72076 Tübingen, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), 18147 Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147 Rostock, Germany
| | - Robert Perneczky
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität, 80336 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Katharina Buerger
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität, 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Department of Psychiatry and Psychotherapy, Technical University of Munich, 81675 Munich, Germany
- Centre for Clinical Brain Sciences, University of Edinburgh and UK DRI, Edinburgh EH16 4SB, UK
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Alfredo Ramirez
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, 50931 Cologne, Germany
- Department of Psychiatry & Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, The University of Texas at San Antonio, San Antonio, TX 78229, USA
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 37075 Göttingen, Germany
- Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Michael T Heneka
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 4362, Belvaux, Luxembourg
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120 Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, 50937 Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany
| |
Collapse
|
19
|
Strodthoff N, Lopez Alcaraz JM, Haverkamp W. Prospects for artificial intelligence-enhanced electrocardiogram as a unified screening tool for cardiac and non-cardiac conditions: an explorative study in emergency care. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:454-460. [PMID: 39081937 PMCID: PMC11284007 DOI: 10.1093/ehjdh/ztae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/11/2024] [Accepted: 05/07/2024] [Indexed: 08/02/2024]
Abstract
Aims Current deep learning algorithms for automatic ECG analysis have shown notable accuracy but are typically narrowly focused on singular diagnostic conditions. This exploratory study aims to investigate the capability of a single deep learning model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a single ECG collected in the emergency department. Methods and results In this study, we assess the performance of a model trained to predict a broad spectrum of diagnoses. We find that the model can reliably predict 253 ICD codes (81 cardiac and 172 non-cardiac) in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner. Conclusion The model demonstrates proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios, indicating its potential as a comprehensive screening tool for diverse medical encounters.
Collapse
Affiliation(s)
- Nils Strodthoff
- Carl von Ossietzky Universität Oldenburg, School VI Medicine and Health Services, Department of Health Services Research, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
| | - Juan Miguel Lopez Alcaraz
- Carl von Ossietzky Universität Oldenburg, School VI Medicine and Health Services, Department of Health Services Research, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
| | - Wilhelm Haverkamp
- Charité Universitätsmedizin Berlin, Department of Cardiology and Metabolism, Clinic for Cardiology, Angiology, and Intensive Care Medicine, Berlin, Germany
| |
Collapse
|
20
|
Makowski C, Nichols TE, Dale AM. Quality over quantity: powering neuroimaging samples in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01893-4. [PMID: 38902353 DOI: 10.1038/s41386-024-01893-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/06/2024] [Accepted: 05/24/2024] [Indexed: 06/22/2024]
Abstract
Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.
Collapse
Affiliation(s)
- Carolina Makowski
- Department of Radiology, University of California San Diego, San Diego, CA, USA.
| | - Thomas E Nichols
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anders M Dale
- Departments of Radiology and Neurosciences, University of California San Diego, San Diego, CA, USA
| |
Collapse
|
21
|
Hazenbroek M, Pengel LHM, Sassen SDT, Massey EK, Reinders MEJ, de Winter BCM, Hesselink DA. Removing the physician from the equation: Patient-controlled, home-based therapeutic drug self-monitoring of tacrolimus. Br J Clin Pharmacol 2024. [PMID: 38830672 DOI: 10.1111/bcp.16121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/22/2024] [Accepted: 04/25/2024] [Indexed: 06/05/2024] Open
Abstract
The dosing of tacrolimus, which forms the backbone of immunosuppressive therapy after kidney transplantation, is complex. This is due to its variable pharmacokinetics (both between and within individual patients), narrow therapeutic index, and the severe consequences of over- and underexposure, which may cause toxicity and rejection, respectively. Tacrolimus is, therefore, routinely dosed by means of therapeutic drug monitoring (TDM). TDM is performed for as long as the transplant functions and frequent and often lifelong sampling is therefore the rule. This puts a significant burden on patients and transplant professionals and is associated with high healthcare-associated costs. Furthermore, by its very nature, TDM is reactive and has no predictive power. Finally, the current practice of TDM does not foresee in an active role for patients themselves. Rather, the physician or pharmacist prescribes the next tacrolimus dose after obtaining the concentration measurement test results. In this article, we propose a strategy of patient-controlled, home-based, self-TDM of the immunosuppressant tacrolimus after transplantation. We argue that with the combined use of population tacrolimus pharmacokinetic models, home-based sampling by means of dried blood spotting and implementation of telemedicine, this may become a feasible approach in the near future.
Collapse
Affiliation(s)
- Marinus Hazenbroek
- Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Liset H M Pengel
- Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sebastiaan D T Sassen
- Erasmus MC, Rotterdam Clinical Pharmacometrics Group, Rotterdam, the Netherlands
- Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Emma K Massey
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marlies E J Reinders
- Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Brenda C M de Winter
- Erasmus MC, Rotterdam Clinical Pharmacometrics Group, Rotterdam, the Netherlands
- Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Dennis A Hesselink
- Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| |
Collapse
|
22
|
Hardenberg JHB. [Data-driven intensive care: a lack of comprehensive datasets]. Med Klin Intensivmed Notfmed 2024; 119:352-357. [PMID: 38668882 DOI: 10.1007/s00063-024-01141-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/26/2024] [Indexed: 05/28/2024]
Abstract
Intensive care units provide a data-rich environment with the potential to generate datasets in the realm of big data, which could be utilized to train powerful machine learning (ML) models. However, the currently available datasets are too small and exhibit too little diversity due to their limitation to individual hospitals. This lack of extensive and varied datasets is a primary reason for the limited generalizability and resulting low clinical utility of current ML models. Often, these models are based on data from single centers and suffer from poor external validity. There is an urgent need for the development of large-scale, multicentric, and multinational datasets. Ensuring data protection and minimizing re-identification risks pose central challenges in this process. The "Amsterdam University Medical Center database (AmsterdamUMCdb)" and the "Salzburg Intensive Care database (SICdb)" demonstrate that open access datasets are possible in Europe while complying with the data protection regulations of the General Data Protection Regulation (GDPR). Another challenge in building intensive care datasets is the absence of semantic definitions in the source data and the heterogeneity of data formats. Establishing binding industry standards for the semantic definition is crucial to ensure seamless semantic interoperability between datasets.
Collapse
Affiliation(s)
- Jan-Hendrik B Hardenberg
- Medizinische Klinik mit Schwerpunkt Nephrologie und internistische Intensivmedizin, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Deutschland.
| |
Collapse
|
23
|
Vano LJ, Veronese M, McCutcheon RA, Howes OD. Neuromelanin-Sensitive MRI: A Biomarker for Treatment-Resistant Schizophrenia? Am J Psychiatry 2024; 181:468-470. [PMID: 38822586 DOI: 10.1176/appi.ajp.20240278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/03/2024]
Affiliation(s)
- Luke J Vano
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Vano, McCutcheon, Howes); Psychiatric Imaging Group, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London (Vano, Howes); Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London (Vano, Howes); South London and Maudsley NHS Foundation Trust, London (Vano, Howes); Department of Neuroimaging, King's College London (Veronese); Department of Information Engineering, University of Padua, Padua, Italy (Veronese); Department of Psychiatry, University of Oxford, Oxford, U.K. (McCutcheon); Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, U.K. (McCutcheon)
| | - Mattia Veronese
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Vano, McCutcheon, Howes); Psychiatric Imaging Group, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London (Vano, Howes); Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London (Vano, Howes); South London and Maudsley NHS Foundation Trust, London (Vano, Howes); Department of Neuroimaging, King's College London (Veronese); Department of Information Engineering, University of Padua, Padua, Italy (Veronese); Department of Psychiatry, University of Oxford, Oxford, U.K. (McCutcheon); Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, U.K. (McCutcheon)
| | - Robert A McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Vano, McCutcheon, Howes); Psychiatric Imaging Group, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London (Vano, Howes); Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London (Vano, Howes); South London and Maudsley NHS Foundation Trust, London (Vano, Howes); Department of Neuroimaging, King's College London (Veronese); Department of Information Engineering, University of Padua, Padua, Italy (Veronese); Department of Psychiatry, University of Oxford, Oxford, U.K. (McCutcheon); Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, U.K. (McCutcheon)
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Vano, McCutcheon, Howes); Psychiatric Imaging Group, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London (Vano, Howes); Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London (Vano, Howes); South London and Maudsley NHS Foundation Trust, London (Vano, Howes); Department of Neuroimaging, King's College London (Veronese); Department of Information Engineering, University of Padua, Padua, Italy (Veronese); Department of Psychiatry, University of Oxford, Oxford, U.K. (McCutcheon); Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, U.K. (McCutcheon)
| |
Collapse
|
24
|
Nan J, Herbert MS, Purpura S, Henneken AN, Ramanathan D, Mishra J. Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals. SENSORS (BASEL, SWITZERLAND) 2024; 24:2640. [PMID: 38676258 PMCID: PMC11053570 DOI: 10.3390/s24082640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/09/2024] [Accepted: 04/19/2024] [Indexed: 04/28/2024]
Abstract
Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care.
Collapse
Affiliation(s)
- Jason Nan
- Neural Engineering and Translation Labs, University of California San Diego, La Jolla, CA 92093, USA; (S.P.); (D.R.); (J.M.)
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Matthew S. Herbert
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA;
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA;
- Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA
| | - Suzanna Purpura
- Neural Engineering and Translation Labs, University of California San Diego, La Jolla, CA 92093, USA; (S.P.); (D.R.); (J.M.)
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA;
| | - Andrea N. Henneken
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA;
- Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA
| | - Dhakshin Ramanathan
- Neural Engineering and Translation Labs, University of California San Diego, La Jolla, CA 92093, USA; (S.P.); (D.R.); (J.M.)
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA;
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA;
- Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA
| | - Jyoti Mishra
- Neural Engineering and Translation Labs, University of California San Diego, La Jolla, CA 92093, USA; (S.P.); (D.R.); (J.M.)
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA;
- Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA
| |
Collapse
|
25
|
Corponi F, Li BM, Anmella G, Mas A, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Garriga M, Vieta E, Lawrie SM, Whalley HC, Hidalgo-Mazzei D, Vergari A. Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number. Transl Psychiatry 2024; 14:161. [PMID: 38531865 DOI: 10.1038/s41398-024-02876-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 03/09/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.
Collapse
Affiliation(s)
- Filippo Corponi
- School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Bryan M Li
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Gerard Anmella
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Ariadna Mas
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Isabella Pacchiarotti
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Marc Valentí
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Iria Grande
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Antoni Benabarre
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Marina Garriga
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Stephen M Lawrie
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Generation Scotland, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Diego Hidalgo-Mazzei
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona (UB), c. Casanova, 143, 08036, Barcelona, Spain
| | - Antonio Vergari
- School of Informatics, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
26
|
Ektefaie Y, Shen A, Bykova D, Marin M, Zitnik M, Farhat M. Evaluating generalizability of artificial intelligence models for molecular datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581982. [PMID: 38464295 PMCID: PMC10925170 DOI: 10.1101/2024.02.25.581982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Deep learning has made rapid advances in modeling molecular sequencing data. Despite achieving high performance on benchmarks, it remains unclear to what extent deep learning models learn general principles and generalize to previously unseen sequences. Benchmarks traditionally interrogate model generalizability by generating metadata based (MB) or sequence-similarity based (SB) train and test splits of input data before assessing model performance. Here, we show that this approach mischaracterizes model generalizability by failing to consider the full spectrum of cross-split overlap, i.e., similarity between train and test splits. We introduce Spectra, a spectral framework for comprehensive model evaluation. For a given model and input data, Spectra plots model performance as a function of decreasing cross-split overlap and reports the area under this curve as a measure of generalizability. We apply Spectra to 18 sequencing datasets with associated phenotypes ranging from antibiotic resistance in tuberculosis to protein-ligand binding to evaluate the generalizability of 19 state-of-the-art deep learning models, including large language models, graph neural networks, diffusion models, and convolutional neural networks. We show that SB and MB splits provide an incomplete assessment of model generalizability. With Spectra, we find as cross-split overlap decreases, deep learning models consistently exhibit a reduction in performance in a task- and model-dependent manner. Although no model consistently achieved the highest performance across all tasks, we show that deep learning models can generalize to previously unseen sequences on specific tasks. Spectra paves the way toward a better understanding of how foundation models generalize in biology.
Collapse
Affiliation(s)
- Yasha Ektefaie
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew Shen
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Computer Science, Northwestern University, Evanston, IL, USA
| | - Daria Bykova
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Maximillian Marin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
| | - Maha Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
27
|
Calderon A, Baik SY, Ng MHS, Fitzsimmons-Craft EE, Eisenberg D, Wilfley DE, Taylor CB, Newman MG. Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students. RESEARCH SQUARE 2024:rs.3.rs-3944417. [PMID: 38464303 PMCID: PMC10925462 DOI: 10.21203/rs.3.rs-3944417/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Background A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated. Methods The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks. Results Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia. Conclusion These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder. Trial registration Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.
Collapse
Affiliation(s)
| | | | - Matthew H S Ng
- Nanyang Technological University, Rehabilitation Research Institute of Singapore
| | | | | | | | | | | |
Collapse
|
28
|
Albertini DF. Searching for answers to the problem of TIME in human ARTs. J Assist Reprod Genet 2024; 41:237-238. [PMID: 38372881 PMCID: PMC10894786 DOI: 10.1007/s10815-024-03063-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024] Open
|
29
|
Adkinson BD, Rosenblatt M, Dadashkarimi J, Tejavibulya L, Jiang R, Noble S, Scheinost D. Brain-phenotype predictions can survive across diverse real-world data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576916. [PMID: 38328100 PMCID: PMC10849571 DOI: 10.1101/2024.01.23.576916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.
Collapse
Affiliation(s)
- Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Javid Dadashkarimi
- Department of Radiology, Athinoula. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02129, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Bioengineering, Northeastern University, Boston, MA, 02120, USA
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06510, USA
| |
Collapse
|
30
|
Naddaf M. Medical AI falters when assessing patients it hasn't seen. Nature 2024:10.1038/d41586-024-00094-9. [PMID: 38212616 DOI: 10.1038/d41586-024-00094-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
|