Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review

Castillo-Sánchez, Gema; Marques, Gonçalo; Dorronzoro, Enrique; Rivera-Romero, Octavio; Franco-Martín, Manuel; Díez, Isabel de la Torre

doi:10.1007/s10916-020-01669-5

Cited by 63 publications

(53 citation statements)

References 60 publications

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“…On the one hand, they cover most of the scientific information in fields such as engineering or telemedicine. On the other hand, these databases have been used by several recent systematic reviews on health informatics [ 16 – 20 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning in Medical Emergencies: a Systematic Review and Analysis

et al. 2021

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Despite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

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Section: Methodsmentioning

confidence: 99%

Machine Learning in Medical Emergencies: a Systematic Review and Analysis

et al. 2021

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“…Included studies reported privacy and confidentiality barriers showing that there are still unresolved ethical, legal, and technological questions. Therefore, new models of responsible and transparent data collection and treatment addressing these questions are needed, especially in public health emergencies [ 74 ]. Limitations in data collection from social media sources is an issue that is commonly reported in the scientific literature [ 75 , 76 ].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…PHI is a multidisciplinary field that uses information technology as provided through the web, smartphones, or wearables to increase participation of individuals in their care process and to enable them in practicing self-care and shared decision-making [ 5 ]. PHI deals with the resources, devices, and methods required to support active participation and engagement of the stakeholders, such as social media [ 5 ]. Goals to be achieved through PHI include improving and maintaining health and well-being; improving the healthcare system and health outcomes; sharing experiences; achieving life goals; and gaining self-education [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review

et al. 2021

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Objectives: Using participatory health informatics (PHI) to detect disease outbreaks or learn about pandemics has gained interest in recent years. However, the role of PHI in understanding and managing pandemics, citizens’ role in this context, and which methods are relevant for collecting and processing data are still unclear, as is which types of data are relevant. This paper aims to clarify these issues and explore the role of PHI in managing and detecting pandemics. Methods: Through a literature review we identified studies that explore the role of PHI in detecting and managing pandemics. Studies from five databases were screened: PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), IEEE Xplore, ACM (Association for Computing Machinery) Digital Library, and Cochrane Library. Data from studies fulfilling the eligibility criteria were extracted and synthesized narratively. Results: Out of 417 citations retrieved, 53 studies were included in this review. Most research focused on influenza-like illnesses or COVID-19 with at least three papers on other epidemics (Ebola, Zika or measles). The geographic scope ranged from global to concentrating on specific countries. Multiple processing and analysis methods were reported, although often missing relevant information. The majority of outcomes are reported for two application areas: crisis communication and detection of disease outbreaks. Conclusions: For most diseases, the small number of studies prevented reaching firm conclusions about the utility of PHI in detecting and monitoring these disease outbreaks. For others, e.g., COVID-19, social media and online search patterns corresponded to disease patterns, and detected disease outbreak earlier than conventional public health methods, thereby suggesting that PHI can contribute to disease and pandemic monitoring.

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“…Teenagers and young adults often share their suicidal thoughts with the public on social media platforms such as Facebook and Twitter [206]. Thus, ML can be used to predict suicide risk based on information posted on social networks [207]. This has led a few platforms such as Facebook and Twitter to set up teams that contact people whose posts are overtly suicidal and provide them with support and resources [206].…”

Section: For the Assessment Of Suicide Riskmentioning

confidence: 99%

Psychiatry in the Digital Age: A Blessing or a Curse?

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Papassotiropoulos

Brühl

et al. 2021

IJERPH

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Social distancing and the shortage of healthcare professionals during the COVID-19 pandemic, the impact of population aging on the healthcare system, as well as the rapid pace of digital innovation are catalyzing the development and implementation of new technologies and digital services in psychiatry. Is this transformation a blessing or a curse for psychiatry? To answer this question, we conducted a literature review covering a broad range of new technologies and eHealth services, including telepsychiatry; computer-, internet-, and app-based cognitive behavioral therapy; virtual reality; digital applied games; a digital medicine system; omics; neuroimaging; machine learning; precision psychiatry; clinical decision support; electronic health records; physician charting; digital language translators; and online mental health resources for patients. We found that eHealth services provide effective, scalable, and cost-efficient options for the treatment of people with limited or no access to mental health care. This review highlights innovative technologies spearheading the way to more effective and safer treatments. We identified artificially intelligent tools that relieve physicians from routine tasks, allowing them to focus on collaborative doctor–patient relationships. The transformation of traditional clinics into digital ones is outlined, and the challenges associated with the successful deployment of digitalization in psychiatry are highlighted.

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Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review

Cited by 63 publications

References 60 publications

Machine Learning in Medical Emergencies: a Systematic Review and Analysis

Machine Learning in Medical Emergencies: a Systematic Review and Analysis

Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review

Psychiatry in the Digital Age: A Blessing or a Curse?

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