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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.
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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.
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Somé NH, Noormohammadpour P, Lange S. The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review. Front Psychiatry 2024; 15:1291362. [PMID: 38501090 PMCID: PMC10944962 DOI: 10.3389/fpsyt.2024.1291362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 02/12/2024] [Indexed: 03/20/2024] Open
Abstract
Background Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature. Objective Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified. Methods A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed. Results The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study. Conclusion The predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.
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Affiliation(s)
- Nibene H. Somé
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Pardis Noormohammadpour
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Shannon Lange
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Puig-Amores I, Cuadrado-Gordillo I, Martín-Mora-Parra G. Health Service Protection vis-à-vis the Detection of Psychosocial Risks of Suicide during the Years 2019-2021. Healthcare (Basel) 2023; 11:healthcare11101505. [PMID: 37239791 DOI: 10.3390/healthcare11101505] [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: 04/18/2023] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 05/28/2023] Open
Abstract
Health services are especially relevant in suicide prevention and intervention, representing a favourable environment in which to implement specific strategies to detect and address suicidal behaviours. Indeed, a significant proportion of people who die by suicide (DBS) present at primary care and mental health services during the last year, month, or even days before committing suicide. The objective of this descriptive and cross-sectional study of all registered cases of death by suicide (N = 265) in Extremadura (Spain) was to determine which of those people who died by suicide had mental health problems (MHP) and what type of assistance they had requested. Diagnoses, previous suicide attempts, type of health service, and last visit before death were explored with univariate analyses and logistic regressions. The proportion of people without MHP was found to be high, and these people had hardly visited the health services at all in their last year. People with MHP, between the ages of 40 and 69, and with previous suicide attempts were more likely to have visited the mental health service in the three months prior to their death. It is, thus, necessary to provide health professionals with tools and training in the prevention of and approach to suicide. Efforts must be directed towards effectively assessing mental health and the risk of suicide since a large proportion of people who die by suicide may go unnoticed.
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Affiliation(s)
- Ismael Puig-Amores
- Department of Psychology and Anthropology, Faculty of Education and Psychology, University of Extremadura, 06071 Badajoz, Spain
| | - Isabel Cuadrado-Gordillo
- Department of Psychology and Anthropology, Faculty of Education and Psychology, University of Extremadura, 06071 Badajoz, Spain
| | - Guadalupe Martín-Mora-Parra
- Department of Psychology and Anthropology, Faculty of Education and Psychology, University of Extremadura, 06071 Badajoz, Spain
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Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:1102715. [PMID: 36909972 PMCID: PMC9995206 DOI: 10.1155/2023/1102715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/29/2022] [Accepted: 09/27/2022] [Indexed: 03/05/2023]
Abstract
Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.
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Majcherek D, Kowalski AM, Lewandowska MS. Lifestyle, Demographic and Socio-Economic Determinants of Mental Health Disorders of Employees in the European Countries. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11913. [PMID: 36231214 PMCID: PMC9565551 DOI: 10.3390/ijerph191911913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
Ensuring the health and well-being of workers should be a top priority for employers and governments. The aim of the article is to evaluate and rank the importance of mental health determinants: lifestyle, demographic factors and socio-economic status. The research study is based on EHIS 2013-2015 data for a sample of N = 140,791 employees from 30 European countries. The results obtained using machine learning techniques such as gradient-boosted trees and SHAPley values show that the mental health of European employees is strongly determined by the BMI, age and social support from close people. The next vital features are alcohol consumption, an unmet need for health care and sports activity, followed by the affordability of medicine or treatment, income and occupation. The wide range of variables clearly indicates that there is an important role for governments to play in order to minimize the risk of mental disorders across various socio-economic groups. It is also a signal for businesses to help boost the mental health of their employees by creating holistic, mentally friendly working conditions, such as offering time-management training, implementing morning briefings, offering quiet areas, making employees feel valued, educating them about depression and burnout symptoms, and promoting a healthy lifestyle.
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Affiliation(s)
- Dawid Majcherek
- Department of International Management, Collegium of World Economy, SGH Warsaw School of Economics, al. Niepodległości 162, 02-554 Warsaw, Poland
| | - Arkadiusz Michał Kowalski
- World Economy Research Institute, Collegium of World Economy, SGH Warsaw School of Economics, al. Niepodległości 162, 02-554 Warsaw, Poland
| | - Małgorzata Stefania Lewandowska
- Department of International Management, Collegium of World Economy, SGH Warsaw School of Economics, al. Niepodległości 162, 02-554 Warsaw, Poland
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Kirtley OJ, van Mens K, Hoogendoorn M, Kapur N, de Beurs D. Translating promise into practice: a review of machine learning in suicide research and prevention. Lancet Psychiatry 2022; 9:243-252. [PMID: 35183281 DOI: 10.1016/s2215-0366(21)00254-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/02/2021] [Accepted: 07/07/2021] [Indexed: 02/06/2023]
Abstract
In ever more pressured health-care systems, technological solutions offering scalability of care and better resource targeting are appealing. Research on machine learning as a technique for identifying individuals at risk of suicidal ideation, suicide attempts, and death has grown rapidly. This research often places great emphasis on the promise of machine learning for preventing suicide, but overlooks the practical, clinical implementation issues that might preclude delivering on such a promise. In this Review, we synthesise the broad empirical and review literature on electronic health record-based machine learning in suicide research, and focus on matters of crucial importance for implementation of machine learning in clinical practice. The challenge of preventing statistically rare outcomes is well known; progress requires tackling data quality, transparency, and ethical issues. In the future, machine learning models might be explored as methods to enable targeting of interventions to specific individuals depending upon their level of need-ie, for precision medicine. Primarily, however, the promise of machine learning for suicide prevention is limited by the scarcity of high-quality scalable interventions available to individuals identified by machine learning as being at risk of suicide.
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Affiliation(s)
| | | | - Mark Hoogendoorn
- Department of Computer Science, Vrij Universiteit Amsterdam, Amsterdam, Netherlands
| | - Navneet Kapur
- Centre for Mental Health and Safety and Greater Manchester National Institute for Health Research Patient Safety Translational Research Centre, University of Manchester, Manchester, UK; Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Derek de Beurs
- Department of Epidemiology, Trimbos Institute, Utrecht, Netherlands
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Lejeune A, Le Glaz A, Perron PA, Sebti J, Baca-Garcia E, Walter M, Lemey C, Berrouiguet S. Artificial intelligence and suicide prevention: a systematic review. Eur Psychiatry 2022; 65:1-22. [PMID: 35166203 PMCID: PMC8988272 DOI: 10.1192/j.eurpsy.2022.8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/13/2021] [Accepted: 12/20/2021] [Indexed: 11/23/2022] Open
Abstract
Background Suicide is one of the main preventable causes of death. Artificial intelligence (AI) could improve methods for assessing suicide risk. The objective of this review is to assess the potential of AI in identifying patients who are at risk of attempting suicide. Methods A systematic review of the literature was conducted on PubMed, EMBASE, and SCOPUS databases, using relevant keywords. Results Thanks to this research, 296 studies were identified. Seventeen studies, published between 2014 and 2020 and matching inclusion criteria, were selected as relevant. Included studies aimed at predicting individual suicide risk or identifying at-risk individuals in a specific population. The AI performance was overall good, although variable across different algorithms and application settings. Conclusions AI appears to have a high potential for identifying patients at risk of suicide. The precise use of these algorithms in clinical situations, as well as the ethical issues it raises, remain to be clarified.
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Affiliation(s)
- Alban Lejeune
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
| | - Aziliz Le Glaz
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
| | | | - Johan Sebti
- Mental Health Department, French Polynesia Hospital, FFC3+H9G, Pirae, French Polynesia
| | | | - Michel Walter
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France
| | - Christophe Lemey
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France
- SPURBO, IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238, Brest, France
| | - Sofian Berrouiguet
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Brest, France
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Wei YX, Liu BP, Zhang J, Wang XT, Chu J, Jia CX. Prediction of recurrent suicidal behavior among suicide attempters with Cox regression and machine learning: a 10-year prospective cohort study. J Psychiatr Res 2021; 144:217-224. [PMID: 34700209 DOI: 10.1016/j.jpsychires.2021.10.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/28/2021] [Accepted: 10/18/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Research on predictors and risk of recurrence after suicide attempt from China is lacking. This study aims to identify risk factors and develop prediction models for recurrent suicidal behavior among suicide attempters using Cox proportional hazard (CPH) and machine learning methods. METHODS The prospective cohort study included 1103 suicide attempters with a maximum follow-up of 10 years from rural China. Baseline characteristics, collected by face-to-face interviews at least 1 month later after index suicide attempt, were used to predict recurrent suicidal behavior. CPH and 3 machine learning algorithms, namely, the least absolute shrinkage and selection operator, random survival forest, and gradient boosting decision tree, were used to construct prediction models. Model performance was accessed by concordance index (C-index) and the time-dependent area under the receiver operating characteristic curve (AUC) value for discrimination, and time-dependent calibration curve along with Brier score for calibration. RESULTS The median follow-up time was 7.79 years, and 49 suicide attempters had recurrent suicidal behavior during the study period. Four models achieved comparably good discrimination and calibration performance, with all C-indexes larger than 0.70, AUC values larger than 0.65, and Brier scores smaller than 0.06. Mental disorder emerged as the most important predictor across all four models. Suicide attempters with mental disorders had a 3 times higher risk of recurrence than those without. History of suicide attempt (HR = 2.84, 95% CI: 1.34-6.02), unstable marital status (HR = 2.81, 95% CI: 1.38-5.71), and older age (HR = 1.51, 95% CI: 1.14-2.01) were also identified as independent predictors of recurrent suicidal behavior by CPH model. CONCLUSIONS We developed four models to predict recurrent suicidal behavior with comparable good prediction performance. Our findings potentially provided benefits in screening vulnerable individuals on a more precise scale.
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Affiliation(s)
- Yan-Xin Wei
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China
| | - Bao-Peng Liu
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China
| | - Jie Zhang
- Shandong University Center for Suicide Prevention Research, China; Department of Sociology, State University of New York College at Buffalo, Buffalo, NY, 14222, USA
| | - Xin-Ting Wang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China
| | - Jie Chu
- Shandong Center for Disease Prevention and Control, Jinan, 250014, Shandong, China
| | - Cun-Xian Jia
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China.
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Souza Filho EMD, Veiga Rey HC, Frajtag RM, Arrowsmith Cook DM, Dalbonio de Carvalho LN, Pinho Ribeiro AL, Amaral J. Can machine learning be useful as a screening tool for depression in primary care? J Psychiatr Res 2021; 132:1-6. [PMID: 33035759 DOI: 10.1016/j.jpsychires.2020.09.025] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 08/07/2020] [Accepted: 09/25/2020] [Indexed: 12/20/2022]
Abstract
Depression is a widespread disease with a high economic burden and a complex pathophysiology disease that is still not wholly clarified, not to mention it usually is associated as a risk factor for absenteeism at work and suicide. Just 50% of patients with depression are diagnosed in primary care, and only 15% receive treatment. Stigmatization, the coexistence of somatic symptoms, and the need to remember signs in the past two weeks can contribute to explaining this situation. In this context, tools that can serve as diagnostic screening are of great value, as they can reduce the number of undiagnosed patients. Besides, Artificial Intelligence (AI) has enabled several fruitful applications in medicine, particularly in psychiatry. This study aims to evaluate the performance of Machine Learning (ML) algorithms in the detection of depressive patients from the clinical, laboratory, and sociodemographic data obtained from the Brazilian National Network for Research on Cardiovascular Diseases from June 2016 to July 2018. The results obtained are promising. In one of them, Random Forests, the accuracy, sensibility, and area under the receiver operating characteristic curve were, respectively, 0.89, 0.90, and 0.87.
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Affiliation(s)
- Erito Marques de Souza Filho
- Universidade Federal Rural do Rio de Janeiro, Rio de Janeiro, Brazil; Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil.
| | | | | | | | | | | | - Jorge Amaral
- Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
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Real-time neural network based predictor for cov19 virus spread. PLoS One 2020; 15:e0243189. [PMID: 33332363 PMCID: PMC7745974 DOI: 10.1371/journal.pone.0243189] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 11/17/2020] [Indexed: 01/08/2023] Open
Abstract
Since the epidemic outbreak in early months of 2020 the spread of COVID-19 has grown rapidly in most countries and regions across the World. Because of that, SARS-CoV-2 was declared as a Public Health Emergency of International Concern (PHEIC) on January 30, 2020, by The World Health Organization (WHO). That’s why many scientists are working on new methods to reduce further growth of new cases and, by intelligent patients allocation, reduce number of patients per doctor, what can lead to more successful treatments. However to properly manage the COVID-19 spread there is a need for real-time prediction models which can reliably support various decisions both at national and international level. The problem in developing such system is the lack of general knowledge how the virus spreads and what would be the number of cases each day. Therefore prediction model must be able to conclude the situation from past data in the way that results will show a future trend and will possibly closely relate to the real numbers. In our opinion Artificial Intelligence gives a possibility to do it. In this article we present a model which can work as a part of an online system as a real-time predictor to help in estimation of COVID-19 spread. This prediction model is developed using Artificial Neural Networks (ANN) to estimate the future situation by the use of geo-location and numerical data from past 2 weeks. The results of our model are confirmed by comparing them with real data and, during our research the model was correctly predicting the trend and very closely matching the numbers of new cases in each day.
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Sanderson M, Bulloch AGM, Wang J, Williams KG, Williamson T, Patten SB. Predicting death by suicide following an emergency department visit for parasuicide with administrative health care system data and machine learning. EClinicalMedicine 2020; 20:100281. [PMID: 32300738 PMCID: PMC7152812 DOI: 10.1016/j.eclinm.2020.100281] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/16/2020] [Accepted: 01/23/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Suicide is a leading cause of death worldwide and results in a large number of person years of life lost. There is an opportunity to evaluate whether administrative health care system data and machine learning can quantify suicide risk in a clinical setting. METHODS The objective was to compare the performance of prediction models that quantify the risk of death by suicide within 90 days of an ED visit for parasuicide with predictors available in administrative health care system data.The modeling dataset was assembled from 5 administrative health care data systems. The data systems contained nearly all of the physician visits, ambulatory care visits, inpatient hospitalizations, and community pharmacy dispenses, of nearly the entire 4.07 million persons in Alberta, Canada. 101 predictors were selected, and these were assembled for each of the 8 quarters (2 years) prior to the quarter of death, resulting in 808 predictors in total for each person. Prediction model performance was validated with 10-fold cross-validation. FINDINGS The optimal gradient boosted trees prediction model achieved promising discrimination (AUC: 0.88) and calibration that could lead to clinical applications. The 5 most important predictors in the optimal gradient boosted trees model each came from a different administrative health care data system. INTERPRETATION The combination of predictors from multiple administrative data systems and the combination of personal and ecologic predictors resulted in promising prediction performance. Further research is needed to develop prediction models optimized for implementation in clinical settings. FUNDING There was no funding for this study.
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Affiliation(s)
- Michael Sanderson
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Canada
- Corresponding author.
| | - Andrew GM Bulloch
- Hotchkiss Brain Institute, Department of Community Health Sciences, Department of Psychiatry, Cumming School of Medicine, University of Calgary, Canada
| | - JianLi Wang
- School of Epidemiology, Public Health and Preventive Medicine, Department of Psychiatry, Faculty of Medicine, University of Ottawa Institute of Mental Health Research, University of Ottawa, Canada
| | - Kimberly G Williams
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Canada
| | - Tyler Williamson
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Canada
| | - Scott B Patten
- Department of Community Health Sciences, Department of Psychiatry, Cumming School of Medicine, University of Calgary, Canada
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