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Zhou J, Tian M, Zhang X, Xiong L, Huang J, Xu M, Xu H, Yin Z, Wu F, Hu J, Liang X, Wei S. Suicide among lymphoma patients. J Affect Disord 2024; 360:97-107. [PMID: 38821367 DOI: 10.1016/j.jad.2024.05.158] [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: 12/06/2023] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 06/02/2024]
Abstract
BACKGROUND Higher suicide rates were observed in patients diagnosed with lymphoma. In this study, we accurately identified patients with high-risk lymphoma for suicide by constructing a nomogram with a view to effective interventions and reducing the risk of suicide. METHODS 235,806 patients diagnosed with lymphoma between 2000 and 2020 were picked from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training (N = 165,064) and validation set (N = 70,742). A combination of the Least absolute shrinkage and selection operator (LASSO) and Cox proportional hazards regression identified the predictors that constructed the nomogram. To assess the discrimination, calibration, clinical applicability, and generalization of this nomogram, we implemented receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA), and internal validation. The robustness of the results was assessed by the competing risks regression model. RESULTS Age at diagnosis, gender, ethnicity, marital status, stage, surgery, radiotherapy, and annual household income were key predictors of suicide in lymphoma patients. A nomogram was created to visualize the risk of suicide after a lymphoma diagnosis. The c-index for the training set was 0.773, and the validation set was 0.777. The calibration curve for the nomogram fitted well with the diagonal and the clinical decision curve indicated its clinical benefit. LIMITATION The effects of unmeasured and unnoticed biases and confounders were difficult to eliminate due to retrospective studies. CONCLUSION A convenient and reliable model has been constructed that will help to individualize and accurately quantify the risk of suicide in patients diagnosed with lymphoma.
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Affiliation(s)
- Jie Zhou
- Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China
| | - Mengjie Tian
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Xiangchen Zhang
- Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China
| | - Lingyi Xiong
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Jinlong Huang
- Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China
| | - Mengfan Xu
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Hongli Xu
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Zhucheng Yin
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Fengyang Wu
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China
| | - Junjie Hu
- Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China
| | - Xinjun Liang
- Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China; Department of Abdominal Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China.
| | - Shaozhong Wei
- Department of Gastrointestinal Oncology Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Hubei Province, Wuhan 430079, Hubei, China; Colorectal Cancer Clinical Research Center of Wuhan, Wuhan 430079, Hubei, China.
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2
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Calcote MJ, Mann JR, Adcock KG, Duckworth S, Donald MC. Big Data in Health Care: An Interprofessional Course. Nurse Educ 2024; 49:E187-E191. [PMID: 37994454 DOI: 10.1097/nne.0000000000001571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
BACKGROUND The widespread adoption of the electronic health record (EHR) has resulted in vast repositories of EHR big data that are being used to identify patterns and correlations that translate into data-informed health care decision making. PROBLEM Health care professionals need the skills necessary to navigate a digitized, data-rich health care environment as big data plays an increasingly integral role in health care. APPROACH Faculty incorporated the concept of big data in an asynchronous online course allowing an interprofessional mix of students to analyze EHR big data on over a million patients. OUTCOMES Students conducted a descriptive analysis of cohorts of patients with selected diagnoses and presented their findings. CONCLUSIONS Students collaborated with an interprofessional team to analyze EHR big data on selected variables. The teams used data visualization tools to describe an assigned diagnosis patient population.
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Affiliation(s)
- Margaret J Calcote
- Author Affiliations: Assistant Professor (Dr Calcote), The University of Mississippi Medical Center School of Nursing, Jackson; Professor and Chair (Dr Mann), Department of Preventive Medicine, The University of Mississippi Medical Center School of Medicine, Jackson; Professor (Dr Adcock), Pharmacy Division, The University of Mississippi Medical Center School of Pharmacy, Jackson; Professor (Dr Duckworth), The University of Mississippi Medical Center Division of Internal Medicine, Jackson; and Medical Student M3 (Mr Donald), The University of Mississippi Medical Center School of Medicine, Jackson
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3
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Ellis SG, Kattan MW. Optimizing the Use of Artificial Intelligence in Cardiology in 2024. JACC Cardiovasc Interv 2024:S1936-8798(24)00798-2. [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] [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
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4
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Bagge CL, Himes KP, Cohen SM, Barbour EV, Comtois KA, Littlefield AK. Can profiles of behaviors occurring within 48 h of a suicide attempt predict future severity of suicidal thoughts and reattempt?: An examination of hospitalized patients 12 Months post-discharge. J Psychiatr Res 2024; 176:259-264. [PMID: 38901390 DOI: 10.1016/j.jpsychires.2024.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/22/2024]
Abstract
Behavioral warning signs (WS) are near-term changes within individuals, which aid in determining imminent risk for suicide attempts. However, those who attempt suicide differ in their engagement of WS, and it is unclear if these differences relate to future risk of suicidal behavior. Using a sample of 132 adults presenting to a hospital following a suicide attempt, the current study sought to determine if differences in engagement in WS for the index attempt prospectively predicted suicide attempt, frequency of ideation, and intensity of suicide ideation 12 months post discharge. Latent class analyses (LCAs) conducted on 6 behaviors (i.e., alcohol use, nightmares, interpersonal negative life events, suicide communication, risky behavior, low sleep, and high sleep) found a 5-class solution optimally fit the data. One identified class, characterized by engagement in risky behaviors the hours before an attempt differed from other identified classes in terms of risk for future suicidal ideation and behaviors. More specifically, participants in "High Risky Behavior" class had higher rates of 12-month suicide reattempt, significantly more frequent suicide ideation, and significantly worse intensity of suicide ideation during the 12 months following their index attempt compared to participants endorsing typical patterns of WS. These results held when adjusting for various traditional baseline covariates (e.g., depressive symptoms). The current study demonstrates that patterns of behavioral WS may be utilized as their own prognostic indicator of future suicidal ideation and behaviors among high-risk individuals reporting a recent suicide attempt, which can inform post-discharge clinical intervention and prevention efforts.
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Affiliation(s)
- Courtney L Bagge
- Department of Psychiatry, University of Michigan Medical Center, 4250 Plymouth Road, Ann Arbor, MI, 48109-2800, USA; VA Center for Clinical Management Research Ann Arbor Department of Veteran Affairs, 2800 Plymouth Rd., North Campus Research Center, B16, Ann Arbor, MI, 48109-2800, USA.
| | - Katie P Himes
- Texas Tech University, Department of Psychology, 2500 Broadway, Lubbock, TX, 79409, USA
| | - Sarah M Cohen
- Department of Psychiatry, University of Michigan Medical Center, 4250 Plymouth Road, Ann Arbor, MI, 48109-2800, USA; VA Center for Clinical Management Research Ann Arbor Department of Veteran Affairs, 2800 Plymouth Rd., North Campus Research Center, B16, Ann Arbor, MI, 48109-2800, USA
| | - Elizabeth V Barbour
- Department of Psychiatry, University of Michigan Medical Center, 4250 Plymouth Road, Ann Arbor, MI, 48109-2800, USA; VA Center for Clinical Management Research Ann Arbor Department of Veteran Affairs, 2800 Plymouth Rd., North Campus Research Center, B16, Ann Arbor, MI, 48109-2800, USA
| | - Katherine A Comtois
- University of Washington, Box 359911, Harborview Medical Center, Seattle, WA, 98195, USA
| | - Andrew K Littlefield
- Texas Tech University, Department of Psychology, 2500 Broadway, Lubbock, TX, 79409, USA
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5
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Yip PSF, Caine ED, Yeung CY, Law YW, Ho RTH. Suicide prevention in Hong Kong: pushing boundaries while building bridges. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 46:101061. [PMID: 38616984 PMCID: PMC11011221 DOI: 10.1016/j.lanwpc.2024.101061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/15/2024] [Accepted: 03/26/2024] [Indexed: 04/16/2024]
Abstract
Hong Kong is a natural laboratory for studying suicides-small geographic footprint, bustling economic activity, rapidly changing socio-demographic transitions, and cultural crossroads. Its qualities also intensify the challenges posed when seeking to prevent them. In this viewpoint, we showed the research and practices of suicide prevention efforts made by the Hong Kong Jockey Club Centre for Suicide Research and Prevention (CSRP), which provide the theoretical underpinning of suicide prevention and empirical evidence. CSRP adopted a multi-level public health approach (universal, selective and indicated), and has collaboratively designed, implemented, and evaluated numerous programs that have demonstrated effectiveness in suicide prevention and mental well-being promotion. The center serves as a hub and a catalyst for creating, identifying, deploying, and evaluating suicide prevention initiatives, which have the potential to reduce regional suicides rates when taken to scale and sustained.
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Affiliation(s)
- Paul Siu Fai Yip
- Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Pofulam, Hong Kong SAR, China
- Department of Social Work and Social Administration, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Eric D. Caine
- Center for the Study and Prevention of Suicide, University of Rochester Medical Center, Rochester, NY, USA
- Canandaigua VA Center of Excellence for Suicide Prevention, Canandaigua, NY, USA
| | - Cheuk Yui Yeung
- Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Pofulam, Hong Kong SAR, China
- School of Social Work, University of Maryland, Baltimore, MD, USA
| | - Yik Wa Law
- Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Pofulam, Hong Kong SAR, China
- Department of Social Work and Social Administration, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Rainbow Tin Hung Ho
- Department of Social Work and Social Administration, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Centre on Behavioral Health, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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Rahayu DS, Khairi AM, Islami CC, Nafi A, Yuliastini NKS. 'Unleashing the guardians: the dynamic triad of AI, social media and school counsellors safeguarding teenage lives from the abyss'. J Public Health (Oxf) 2024; 46:e167-e168. [PMID: 37533218 DOI: 10.1093/pubmed/fdad139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Indexed: 08/04/2023] Open
Affiliation(s)
- Dwi Sri Rahayu
- Department of Guidance and Counseling, Faculty of Education, Universitas Negeri Malang, Malang, Jawa Timur 65145, Indonesia
- Department of Guidance and Counseling, Faculty of Training and Education, Universitas Katolik Widya Mandala Surabaya, Madiun, Jawa Timur 63131, Indonesia
| | - Alfin Miftahul Khairi
- Department of Islamic Guidance and Counseling, Faculty of Ushuluddin and Da'wa, UIN Raden Mas Said Surakarta, Solo, Jawa Tengah 57168, Indonesia
| | - Chitra Charisma Islami
- Department of teacher education for early childhood education, STKIP Muhammadiyah Kuningan, Kuningan, Jawa Barat, 45511, Indonesia
| | - Ahmad Nafi
- Department of Islamic Guidance and Counseling, Faculty of Da'wa and Islamic Communication, IAIN Kudus, Kudus, Jawa Tengah 59322, Indonesia
| | - Ni Komang Sri Yuliastini
- Department of Guidance and Counseling, Faculty of Training and Education, Universitas PGRI Mahadewa Indonesia, Denpasar, Provinsi Bali 80239, Indonesia
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7
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Li X, Chen F, Ma L. Exploring the Potential of Artificial Intelligence in Adolescent Suicide Prevention: Current Applications, Challenges, and Future Directions. Psychiatry 2024; 87:7-20. [PMID: 38227496 DOI: 10.1080/00332747.2023.2291945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
ObjectiveThe global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care.
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8
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Gürcan A, Pereira-Sanchez V, Costa MPD, Ransing R, Ramalho R. Artificial Intelligence Innovatıons In Psychiatry: Global Perspective From Early Career Psychiatrists. TURK PSIKIYATRI DERGISI = TURKISH JOURNAL OF PSYCHIATRY 2024; 35:83-84. [PMID: 38556941 PMCID: PMC11003371 DOI: 10.5080/u27384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 10/06/2023] [Indexed: 04/02/2024]
Affiliation(s)
- Ahmet Gürcan
- Dr., Başkent University Medical Faculty, Department of Psychiatry, Ankara, Turkey
| | - Victor Pereira-Sanchez
- Dr., Stavros Niarchos Foundation (SNF) Global Center for Child and Adolescent Mental Health at the Child Mind Institute, New York, USA
| | | | - Ramdas Ransing
- Dr., All India Institute of Medical Sciences, Guwahati, Assam, India, Department of Psychiatry Clinical Neurosciences, and Addiction medicine, Guwahati, İndia
| | - Rodrigo Ramalho
- Dr., The University of Auckland, Auckland, New Zealand, Department of Social and Community Health, Auckland, New Zeland
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Wilhelmy S, Giupponi G, Groß D, Eisendle K, Conca A. A shift in psychiatry through AI? Ethical challenges. Ann Gen Psychiatry 2023; 22:43. [PMID: 37919759 PMCID: PMC10623776 DOI: 10.1186/s12991-023-00476-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/24/2023] [Indexed: 11/04/2023] Open
Abstract
The digital transformation has made its way into many areas of society, including medicine. While AI-based systems are widespread in medical disciplines, their use in psychiatry is progressing more slowly. However, they promise to revolutionize psychiatric practice in terms of prevention options, diagnostics, or even therapy. Psychiatry is in the midst of this digital transformation, so the question is no longer "whether" to use technology, but "how" we can use it to achieve goals of progress or improvement. The aim of this article is to argue that this revolution brings not only new opportunities but also new ethical challenges for psychiatry, especially with regard to safety, responsibility, autonomy, or transparency. As an example, the relationship between doctor and patient in psychiatry will be addressed, in which digitization is also leading to ethically relevant changes. Ethical reflection on the use of AI systems offers the opportunity to accompany these changes carefully in order to take advantage of the benefits that this change brings. The focus should therefore always be on balancing what is technically possible with what is ethically necessary.
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Affiliation(s)
- Saskia Wilhelmy
- Institute for History, Theory and Ethics in Medicine, University Hospital, RWTH Aachen University, Wendlingweg 2, 5074, Aachen, Germany.
| | - Giancarlo Giupponi
- Academic Teaching Department of Psychiatry, Central Hospital, Sanitary Agency of South Tyrol, Via Lorenz Böhler 5, 39100, Bolzano, Italy
| | - Dominik Groß
- Institute for History, Theory and Ethics in Medicine, University Hospital, RWTH Aachen University, Wendlingweg 2, 5074, Aachen, Germany
| | - Klaus Eisendle
- Institute of General Practice and Public Health, Provincial College for Health Professions Claudiana, Lorenz-Böhler-Straße 13, 39100, Bolzano, Italy
| | - Andreas Conca
- Academic Teaching Department of Psychiatry, Central Hospital, Sanitary Agency of South Tyrol, Via Lorenz Böhler 5, 39100, Bolzano, Italy
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Candon M, Fox K, Jager-Hyman S, Jang M, Augustin R, Cantiello H, Colton L, Drake R, Futterer A, Kessel P, Kwon N, Levin S, Maddox B, Parrish C, Robbins H, Shen S, Smith JL, Ware N, Shoyinka S, Lim S. Building an Integrated Data Infrastructure to Examine the Spectrum of Suicide Risk Factors in Philadelphia Medicaid. ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH 2023; 50:999-1009. [PMID: 37689586 DOI: 10.1007/s10488-023-01299-2] [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] [Accepted: 08/25/2023] [Indexed: 09/11/2023]
Abstract
While there are many data-driven approaches to identifying individuals at risk of suicide, they tend to focus on clinical risk factors, such as previous psychiatric hospitalizations, and rarely include risk factors that occur in nonclinical settings, such as jails or emergency shelters. A better understanding of system-level encounters by individuals at risk of suicide could help inform suicide prevention efforts. In Philadelphia, we built a community-level data infrastructure that encompassed suicide death records, behavioral health claims, incarceration episodes, emergency housing episodes, and involuntary commitment petitions to examine a broader spectrum of suicide risk factors. Here, we describe the development of the data infrastructure, present key trends in suicide deaths in Philadelphia, and, for the Medicaid-eligible population, determine whether suicide decedents were more likely to interact with the behavioral health, carceral, and housing service systems compared to Medicaid-eligible Philadelphians who did not die by suicide. Between 2003 and 2018, there was an increase in the number of annual suicide deaths among Medicaid-eligible individuals, in part due to changes in Medicaid eligibility. There were disproportionately more suicide deaths among Black and Hispanic individuals who were Medicaid-eligible, who were younger on average, compared to suicide decedents who were never Medicaid-eligible. However, when we accounted for the racial and ethnic composition of the Medicaid population at large, we found that White individuals were four times as likely to die by suicide, while Asian, Black, Hispanic, and individuals of other races were less likely to die by suicide. Overall, 58% of individuals who were Medicaid-eligible and died by suicide had at least one Medicaid-funded behavioral health claim, 10% had at least one emergency housing episode, 25% had at least one incarceration episode, and 22% had at least one involuntary commitment. By developing a data infrastructure that can incorporate a broader spectrum of risk factors for suicide, we demonstrate how communities can harness administrative data to inform suicide prevention efforts. Our findings point to the need for suicide prevention in nonclinical settings such as jails and emergency shelters, and demonstrate important trends in suicide deaths in the Medicaid population.
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Affiliation(s)
- Molly Candon
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Kathleen Fox
- Department of Behavioral Health and Intellectual disability Services, City of Philadelphia, Philadelphia, PA, USA
| | - Shari Jager-Hyman
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Min Jang
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rachel Augustin
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- School of Public Health, Drexel University, Philadelphia, PA, USA
| | - Hilary Cantiello
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa Colton
- Department of Behavioral Health and Intellectual disability Services, City of Philadelphia, Philadelphia, PA, USA
| | - Rebecca Drake
- Philadelphia Department of Public Health, Philadelphia, PA, USA
| | - Anne Futterer
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Patrick Kessel
- Department of Behavioral Health and Intellectual disability Services, City of Philadelphia, Philadelphia, PA, USA
| | - Nayoung Kwon
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Serge Levin
- Department of Behavioral Health and Intellectual disability Services, City of Philadelphia, Philadelphia, PA, USA
| | - Brenna Maddox
- University of North Carolina Chapel Hill, Chapel Hill, NC, USA
| | - Charles Parrish
- Department of Behavioral Health and Intellectual disability Services, City of Philadelphia, Philadelphia, PA, USA
| | - Hunter Robbins
- Department of Behavioral Health and Intellectual disability Services, City of Philadelphia, Philadelphia, PA, USA
| | - Siyuan Shen
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph L Smith
- Jefferson College of Population Health, Philadelphia, PA, USA
| | - Naima Ware
- Department of Behavioral Health and Intellectual disability Services, City of Philadelphia, Philadelphia, PA, USA
| | - Sosunmolu Shoyinka
- Department of Behavioral Health and Intellectual disability Services, City of Philadelphia, Philadelphia, PA, USA
| | - Suet Lim
- Department of Behavioral Health and Intellectual disability Services, City of Philadelphia, Philadelphia, PA, USA
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11
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Menon V, Vijayakumar L. Artificial intelligence-based approaches for suicide prediction: Hope or hype? Asian J Psychiatr 2023; 88:103728. [PMID: 37573803 DOI: 10.1016/j.ajp.2023.103728] [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: 07/20/2023] [Accepted: 08/08/2023] [Indexed: 08/15/2023]
Abstract
Accurate prediction of suicide risk is important because it allows evidence-based interventions to be targeted to at-risk populations. Conventional approaches to prediction of suicide risk have shown suboptimal accuracy. In this context, artificial intelligence (AI)-based models for suicide prediction, with their ability to handle big data collected through low-burden methods, have gained research traction in the recent past. Preliminary evidence suggests the promise of AI-driven methods for suicide prediction and prevention. These methods may hold particular relevance for India and other low-and-middle-income countries because they may be more feasible, scalable, less stigmatizing, and potentially more resource-effective.
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Affiliation(s)
- Vikas Menon
- Department of Psychiatry, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry 605006, India.
| | - Lakshmi Vijayakumar
- SNEHA, Chennai, India; Dept of Psychiatry,Voluntary Health Services, Chennai, India; University of Melbourne, Australia; University of Griffith, Australia
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12
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Thornton J, Tandon R. Does machine-learning-based prediction of suicide risk actually reduce rates of suicide: A critical examination. Asian J Psychiatr 2023; 88:103769. [PMID: 37741111 DOI: 10.1016/j.ajp.2023.103769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/25/2023]
Affiliation(s)
- Joseph Thornton
- Department of Psychiatry, University of Florida College of Medicine, Gainesville, FL 32608, USA.
| | - Rajiv Tandon
- Department of Psychiatry, Western Michigan University Homer Stryker MD School of Medicine, Kalamazoo, MI 49048, USA
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13
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Roca M, Gili M. [Suicide prevention]. Med Clin (Barc) 2023; 161:158-159. [PMID: 37045670 DOI: 10.1016/j.medcli.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/03/2023] [Accepted: 03/07/2023] [Indexed: 04/14/2023]
Affiliation(s)
- Miquel Roca
- Departamento de Medicina, Instituto Universitario de Investigación en Ciencias de la Salud/Idisba, Universitat de les Illes Balears, Palma, España.
| | - Margalida Gili
- Departamento de Psicología, Instituto Universitario de Investigación en Ciencias de la Salud/Idisba, Universitat de les Illes Balears, Palma, España
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14
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Gellert GA, Rasławska-Socha J, Marcjasz N, Price T, Heyduk A, Mlodawska A, Kuszczyński K, Jędruch A, Orzechowski P. The Role of Virtual Triage in Improving Clinician Experience and Satisfaction: A Narrative Review. TELEMEDICINE REPORTS 2023; 4:180-191. [PMID: 37529770 PMCID: PMC10389257 DOI: 10.1089/tmr.2023.0020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 08/03/2023]
Abstract
Objective This review examines the literature on improving clinician satisfaction with a focus on what has been most effective in improving experience from the perspective of clinicians, and the potential role that virtual triage (VT) technology can play in delivering positive clinician experiences that improve clinical care, and bring value to health care delivery organizations (HDOs). Methods Review and synthesis of evidence on clinician satisfaction indicating a potential for VT to favorably impact clinician experience, sense of effectiveness, efficiency, and reduction of administrative task burden. Analysis considers how to conceptualize and the value of improving clinician experience, leading clinician dissatisfiers, and the potential role of VT in improving clinician experience/satisfaction. Results Contributors to poor clinician experience/satisfaction where VT could have a beneficial impact include better managing resource limitations, administrative workload, lack of care coordination, information overload, and payer interactions. VT can improve clinician experience through the technology's ability to leverage real-time actionable data clinicians can use, streamlining patient-clinician communications, personalizing care delivery, optimizing care coordination, and better aligning digital/virtual services with clinical practice. From an organizational perspective, improvements in clinician experience and satisfaction derive from establishing an effective digital back door, increasing the clinical impact of and satisfaction derived from telemedicine and virtual care, and enhancing clinician centricity. Conclusions By embracing digital transformation and implementing solutions such as VT that focus on improving patient and clinician experience, HDOs can address barriers to delivery of high-quality, efficient, and cost-effective care. VT is a digital health tool that can create a more streamlined and satisfying experience for clinicians and the patients they care for. VT is a technology solution that can help clinicians make faster more informed decisions, reduces avoidable care, improves communication with patients and within care teams, and lowers their administrative burden so they have more quality time to care for patients.
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Affiliation(s)
- George A. Gellert
- Evidence-Based Impact and Value Demonstration, Infermedica Inc., San Antonio, Texas, USA
| | - Joanna Rasławska-Socha
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
| | - Natalia Marcjasz
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
| | - Tim Price
- Product Development, Infermedica Inc., London, United Kingdom
| | - Alicja Heyduk
- Implementation and Customer Success, Infermedica Inc., Wrocław, Poland
| | - Agata Mlodawska
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
| | - Kacper Kuszczyński
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
| | - Aleksandra Jędruch
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
| | - Piotr Orzechowski
- Clinical Validation and Evidence-Based Impact and Value Demonstration, Infermedica Inc., Wrocław, Poland
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15
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Kessler RC, Bauer MS, Bishop TM, Bossarte RM, Castro VM, Demler OV, Gildea SM, Goulet JL, King AJ, Kennedy CJ, Landes SJ, Liu H, Luedtke A, Mair P, Marx BP, Nock MK, Petukhova MV, Pigeon WR, Sampson NA, Smoller JW, Miller A, Haas G, Benware J, Bradley J, Owen RR, House S, Urosevic S, Weinstock LM. Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention. JAMA Psychiatry 2023; 80:230-240. [PMID: 36652267 PMCID: PMC9857842 DOI: 10.1001/jamapsychiatry.2022.4634] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 11/09/2022] [Indexed: 01/19/2023]
Abstract
Importance The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information. Objective To determine whether model prediction could be improved by adding information extracted from clinical notes and public records. Design, Setting, and Participants Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022. Main Outcomes and Measures Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database. Results The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%). Conclusions and Relevance In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.
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Affiliation(s)
- Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Mark S. Bauer
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- VA Boston Healthcare System, Boston, Massachusetts
| | - Todd M. Bishop
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, New York
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York
| | - Robert M. Bossarte
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, New York
- Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa
| | - Victor M. Castro
- Research Information Science and Computing, Mass General Brigham, Somerville, Massachusetts
| | - Olga V. Demler
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Sarah M. Gildea
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Joseph L. Goulet
- Pain, Research, Informatics, Multi-morbidities and Education Center, VA Connecticut Healthcare System, West Haven
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Andrew J. King
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Chris J. Kennedy
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Sara J. Landes
- Behavioral Health Quality Enhancement Research Initiative (QUERI), Central Arkansas Veterans Healthcare System, North Little Rock
- Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock
| | - Howard Liu
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, New York
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Patrick Mair
- Department of Psychology, Harvard University, Cambridge, Massachusetts
| | - Brian P. Marx
- National Center for PTSD, VA Boston Healthcare System, Boston, Massachusetts
- Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts
| | - Matthew K. Nock
- Department of Psychology, Harvard University, Cambridge, Massachusetts
| | - Maria V. Petukhova
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Wilfred R. Pigeon
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, New York
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York
| | - Nancy A. Sampson
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Jordan W. Smoller
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- Department of Psychiatry, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | | | - Gretchen Haas
- VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | | | - John Bradley
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
- VA Boston Healthcare System, Boston, Massachusetts
| | - Richard R. Owen
- Central Arkansas Veterans Healthcare System, Little Rock
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock
| | - Samuel House
- Central Arkansas Veterans Healthcare System, Little Rock
- Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock
| | - Snezana Urosevic
- Minneapolis VA Healthcare System, Minneapolis, Minnesota
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis
| | - Lauren M. Weinstock
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island
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16
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Simpson SA, Takeshita J. Better Science for Better Emergency Psychiatry: A New Section for JACLP. J Acad Consult Liaison Psychiatry 2023; 64:103-105. [PMID: 36764485 DOI: 10.1016/j.jaclp.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
| | - Junji Takeshita
- Department of Psychiatry, University of Hawaii John A. Burns School of Medicine and The Queen's Medical Center, Honolulu, HI
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Yan J, Liu Y, Yu J, Liao L, Wang H. Establishment and validation of a nomogram for suicidality in Chinese secondary school students. J Affect Disord 2023; 330:148-157. [PMID: 36801419 DOI: 10.1016/j.jad.2023.02.062] [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: 12/13/2022] [Revised: 02/11/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023]
Abstract
BACKGROUND Accurately identifying high-risk of suicide groups and conducting appropriate interventions are important to reduce the risk of suicide. In this study, a nomogram technique was used to develop a predictive model for the suicidality of secondary school students based on four aspects: individual characteristics; health risk behaviors; family factors; and school factors. METHODS A total of 9338 secondary school students were surveyed using the stratified cluster sampling method, and subjects were randomly divided into a training set (n = 6366) and a validation set (n = 2728). In the former, the results of the lasso regression and random forest were combined, from which 7 optimal predictors of suicidality were determined. These were used to construct a nomogram. This nomogram's discrimination, calibration, clinical applicability, and generalization were assessed using receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA), and internal validation. RESULTS Gender, depression symptoms, self-injury, running away from home, parents' relationship, relationship with father, and academic stress were found to be significant predictors of suicidality. The area under the curve (AUC) of the training set was 0.806, while that of the validation data was 0.792. The calibration curve of the nomogram was close to the diagonal, and the DCA showed the nomogram was clinically beneficial across a range of thresholds of 9-89 %. LIMITATIONS Causal inference is limited due to the cross-sectional design. CONCLUSION An effective tool was constructed for predicting suicidality among secondary school students, which should help school healthcare personnel assess this information about students and also identify high-risk groups.
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Affiliation(s)
- Jie Yan
- School of Public Health, Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
| | - Yang Liu
- School of Public Health, Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
| | - Junjie Yu
- School of Public Health, Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
| | - Lipin Liao
- School of Public Health, Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China
| | - Hong Wang
- School of Public Health, Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China.
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18
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López-Ojeda W, Hurley RA. Medical Metaverse, Part 2: Artificial Intelligence Algorithms and Large Language Models in Psychiatry and Clinical Neurosciences. J Neuropsychiatry Clin Neurosci 2023; 35:316-320. [PMID: 37840258 DOI: 10.1176/appi.neuropsych.20230117] [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: 10/17/2023]
Affiliation(s)
- Wilfredo López-Ojeda
- Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center (MIRECC) and Research and Academic Affairs Service Line, W.G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Department of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Department of Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
| | - Robin A Hurley
- Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center (MIRECC) and Research and Academic Affairs Service Line, W.G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Department of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Department of Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
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19
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Ayhan Y. The Impact of Artificial Intelligence on Psychiatry: Benefits and Concerns-An assay from a disputed 'author'. TURK PSIKIYATRI DERGISI = TURKISH JOURNAL OF PSYCHIATRY 2023; 34:65-67. [PMID: 37357892 PMCID: PMC10552174 DOI: 10.5080/u27365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
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20
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Lejeune A, Robaglia BM, Walter M, Berrouiguet S, Lemey C. Use of social media data to diagnose and monitor psychotic disorders: systematic review and perspectives (Preprint). J Med Internet Res 2022; 24:e36986. [PMID: 36066938 PMCID: PMC9490531 DOI: 10.2196/36986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/30/2022] [Accepted: 06/07/2022] [Indexed: 01/10/2023] Open
Abstract
Background Schizophrenia is a disease associated with high burden, and improvement in care is necessary. Artificial intelligence (AI) has been used to diagnose several medical conditions as well as psychiatric disorders. However, this technology requires large amounts of data to be efficient. Social media data could be used to improve diagnostic capabilities. Objective The objective of our study is to analyze the current capabilities of AI to use social media data as a diagnostic tool for psychotic disorders. Methods A systematic review of the literature was conducted using several databases (PubMed, Embase, Cochrane, PsycInfo, and IEEE Xplore) using relevant keywords to search for articles published as of November 12, 2021. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria to identify, select, and critically assess the quality of the relevant studies while minimizing bias. We critically analyzed the methodology of the studies to detect any bias and presented the results. Results Among the 93 studies identified, 7 studies were included for analyses. The included studies presented encouraging results. Social media data could be used in several ways to care for patients with schizophrenia, including the monitoring of patients after the first episode of psychosis. We identified several limitations in the included studies, mainly lack of access to clinical diagnostic data, small sample size, and heterogeneity in study quality. We recommend using state-of-the-art natural language processing neural networks, called language models, to model social media activity. Combined with the synthetic minority oversampling technique, language models can tackle the imbalanced data set limitation, which is a necessary constraint to train unbiased classifiers. Furthermore, language models can be easily adapted to the classification task with a procedure called “fine-tuning.” Conclusions The use of social media data for the diagnosis of psychotic disorders is promising. However, most of the included studies had significant biases; we therefore could not draw conclusions about accuracy in clinical situations. Future studies need to use more accurate methodologies to obtain unbiased results.
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Affiliation(s)
- Alban Lejeune
- Unité de Recherche Clinique Intersectorielle, Hôpital de Bohars, Centre Hospitalier Régional Universitaire de Brest, Bohars, France
| | | | - Michel Walter
- Unité de Recherche Clinique Intersectorielle, Hôpital de Bohars, Centre Hospitalier Régional Universitaire de Brest, Bohars, France
- Faculté de Médecine et Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - Sofian Berrouiguet
- Unité de Recherche Clinique Intersectorielle, Hôpital de Bohars, Centre Hospitalier Régional Universitaire de Brest, Bohars, France
- Laboratoire de Traitement de l'Information Médicale, Unité Mixte de Recherche 1101, Institut National de la Santé et de la Recherche Médicale, Brest, France
| | - Christophe Lemey
- Unité de Recherche Clinique Intersectorielle, Hôpital de Bohars, Centre Hospitalier Régional Universitaire de Brest, Bohars, France
- Faculté de Médecine et Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
- Lab-STICC, Unité Mixte de Recherche, Centre National de la Recherche Scientifique 6285, F-29238, École Nationale Supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, Brest, France
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