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Fernandes JRN, Teles AS, Fernandes TRS, Lima LDB, Balhara S, Gupta N, Teixeira S. Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review. J Clin Med 2023; 13:180. [PMID: 38202187 PMCID: PMC10779723 DOI: 10.3390/jcm13010180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/20/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.
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Affiliation(s)
- Jacks Renan Neves Fernandes
- PhD Program in Biotechnology—Northeast Biotechnology Network, Federal University of Piauí, Teresina 64049-550, Brazil;
| | - Ariel Soares Teles
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
- Federal Institute of Maranhão, Araioses 65570-000, Brazil
| | - Thayaná Ribeiro Silva Fernandes
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
| | - Lucas Daniel Batista Lima
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
| | - Surjeet Balhara
- Department of Electronics & Communication Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India;
| | - Nishu Gupta
- Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway;
| | - Silmar Teixeira
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
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Silva RPD, Pollettini JT, Pazin Filho A. Unsupervised natural language processing in the identification of patients with suspected COVID-19 infection. CAD SAUDE PUBLICA 2023; 39:e00243722. [PMID: 38055548 DOI: 10.1590/0102-311xpt243722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/04/2023] [Indexed: 12/08/2023] Open
Abstract
Patients with post-COVID-19 syndrome benefit from health promotion programs. Their rapid identification is important for the cost-effective use of these programs. Traditional identification techniques perform poorly especially in pandemics. A descriptive observational study was carried out using 105,008 prior authorizations paid by a private health care provider with the application of an unsupervised natural language processing method by topic modeling to identify patients suspected of being infected by COVID-19. A total of 6 models were generated: 3 using the BERTopic algorithm and 3 Word2Vec models. The BERTopic model automatically creates disease groups. In the Word2Vec model, manual analysis of the first 100 cases of each topic was necessary to define the topics related to COVID-19. The BERTopic model with more than 1,000 authorizations per topic without word treatment selected more severe patients - average cost per prior authorizations paid of BRL 10,206 and total expenditure of BRL 20.3 million (5.4%) in 1,987 prior authorizations (1.9%). It had 70% accuracy compared to human analysis and 20% of cases with potential interest, all subject to analysis for inclusion in a health promotion program. It had an important loss of cases when compared to the traditional research model with structured language and identified other groups of diseases - orthopedic, mental and cancer. The BERTopic model served as an exploratory method to be used in case labeling and subsequent application in supervised models. The automatic identification of other diseases raises ethical questions about the treatment of health information by machine learning.
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Affiliation(s)
- Rildo Pinto da Silva
- Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brasil
| | | | - Antonio Pazin Filho
- Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brasil
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Parsapoor (Mah Parsa) M, Koudys JW, Ruocco AC. Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk. Front Psychiatry 2023; 14:1186569. [PMID: 37564247 PMCID: PMC10411603 DOI: 10.3389/fpsyt.2023.1186569] [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: 03/15/2023] [Accepted: 06/14/2023] [Indexed: 08/12/2023] Open
Abstract
Suicide is a leading cause of death that demands cross-disciplinary research efforts to develop and deploy suicide risk screening tools. Such tools, partly informed by influential suicide theories, can help identify individuals at the greatest risk of suicide and should be able to predict the transition from suicidal thoughts to suicide attempts. Advances in artificial intelligence have revolutionized the development of suicide screening tools and suicide risk detection systems. Thus, various types of AI systems, including text-based systems, have been proposed to identify individuals at risk of suicide. Although these systems have shown acceptable performance, most of them have not incorporated suicide theories in their design. Furthermore, directly applying suicide theories may be difficult because of the diversity and complexity of these theories. To address these challenges, we propose an approach to develop speech- and language-based suicide risk detection systems. We highlight the promise of establishing a benchmark textual and vocal dataset using a standardized speech and language assessment procedure, and research designs that distinguish between the risk factors for suicide attempt above and beyond those for suicidal ideation alone. The benchmark dataset could be used to develop trustworthy machine learning or deep learning-based suicide risk detection systems, ultimately constructing a foundation for vocal and textual-based suicide risk detection systems.
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Affiliation(s)
| | - Jacob W. Koudys
- Department of Psychological Clinical Science, University of Toronto, Toronto, ON, Canada
| | - Anthony C. Ruocco
- Department of Psychological Clinical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto Scarborough Toronto, Toronto, ON, Canada
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Rodante DE, Chiapella LC, Olivera Fedi R, Papávero EB, Lavoie KL, Daray FM. A randomized 3-month, parallel-group, controlled trial of CALMA m-health app as an adjunct to therapy to reduce suicidal and non-suicidal self-injurious behaviors in adolescents: study protocol. Front Psychiatry 2023; 14:1087097. [PMID: 37547219 PMCID: PMC10397405 DOI: 10.3389/fpsyt.2023.1087097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 06/07/2023] [Indexed: 08/08/2023] Open
Abstract
Background Suicidal and non-suicidal self-injurious behaviors are among the leading causes of death and injury in adolescents and youth worldwide. Mobile app development could help people at risk and provide resources to deliver evidence-based interventions. There is no specific application for adolescents and young people available in Spanish. Our group developed CALMA, the first interactive mobile application with the user in Spanish, which provides tools based on Dialectical Behavioral Therapy to manage a crisis of suicidal or non-suicidal self-directed violence with the aim of preventing suicide in adolescents and youth. Methods To test the effectiveness, safety and level of engagement of the CALMA app in people aged 10 to 19 who are treated in mental health services of two public hospitals, we will conduct a parallel-group, two-arm randomized controlled trial. Participants will be assessed face-to-face and via video call at four timepoints: day-0 (baseline), day-30, day-60, and day-90. A total of 29 participants per group will be included. Change in the frequency of suicidal and non-suicidal self-injurious behaviors will be compared between groups, as well as the level of emotional dysregulation, level of app engagement and time of psychiatric admission during the follow-up period. Discussion This study is particularly relevant to young people given their widespread use of mobile technology, while there are currently no available smartphone app-based self-guided psychological strategies in Spanish that attempt to reduce suicidal behavior in adolescents who are assisted in the public health sector from low and middle-income countries in Latin America. Clinical trial registration https://clinicaltrials.gov/, NCT05453370.
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Affiliation(s)
- Demián Emanuel Rodante
- Facultad de Medicina, Universidad de Buenos Aires, Instituto de Farmacología, Buenos Aires, Argentina
- “Dr. Braulio A. Moyano” Neuropsychiatric Hospital, Buenos Aires, Argentina
- FORO Foundation for Mental Health, Buenos Aires, Argentina
| | - Luciana Carla Chiapella
- Pharmacology Area, Faculty of Biochemical and Pharmaceutical Sciences, National University of Rosario, Rosario, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
| | - Ramiro Olivera Fedi
- Facultad de Medicina, Universidad de Buenos Aires, Instituto de Farmacología, Buenos Aires, Argentina
| | - Eliana Belén Papávero
- Facultad de Medicina, Universidad de Buenos Aires, Instituto de Farmacología, Buenos Aires, Argentina
- FORO Foundation for Mental Health, Buenos Aires, Argentina
- Pedro de Elizalde Children’s General Hospital, Buenos Aires, Argentina
| | - Kim L. Lavoie
- Montreal Behavioural Medicine Centre, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’Île-de-Montréal, Montreal, QC, Canada
- Department of Psychology, Université du Québec à Montréal, Montreal, QC, Canada
| | - Federico Manuel Daray
- Facultad de Medicina, Universidad de Buenos Aires, Instituto de Farmacología, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
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Kleiman EM, Glenn CR, Liu RT. The use of advanced technology and statistical methods to predict and prevent suicide. NATURE REVIEWS PSYCHOLOGY 2023; 2:347-359. [PMID: 37588775 PMCID: PMC10426769 DOI: 10.1038/s44159-023-00175-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 08/18/2023]
Abstract
In the past decade, two themes have emerged across suicide research. First, according to meta-analyses, the ability to predict and prevent suicidal thoughts and behaviours is weaker than would be expected for the size of the field. Second, review and commentary papers propose that technological and statistical methods (such as smartphones, wearables, digital phenotyping and machine learning) might become solutions to this problem. In this Review, we aim to strike a balance between the pessimistic picture presented by these meta-analyses and the optimistic picture presented by review and commentary papers about the promise of advanced technological and statistical methods to improve the ability to understand, predict and prevent suicide. We divide our discussion into two broad categories. First, we discuss the research aimed at assessment, with the goal of better understanding or more accurately predicting suicidal thoughts and behaviours. Second, we discuss the literature that focuses on prevention of suicidal thoughts and behaviours. Ecological momentary assessment, wearables and other technological and statistical advances hold great promise for predicting and preventing suicide, but there is much yet to do.
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Affiliation(s)
- Evan M. Kleiman
- Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | | | - Richard T. Liu
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
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A review of natural language processing in the identification of suicidal behavior. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2023. [DOI: 10.1016/j.jadr.2023.100507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
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The comfort of adolescent patients and their parents with mobile sensing and digital phenotyping. COMPUTERS IN HUMAN BEHAVIOR 2023. [DOI: 10.1016/j.chb.2022.107603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Arowosegbe A, Oyelade T. Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1514. [PMID: 36674270 PMCID: PMC9859480 DOI: 10.3390/ijerph20021514] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/04/2023] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
(1) Introduction: Around a million people are reported to die by suicide every year, and due to the stigma associated with the nature of the death, this figure is usually assumed to be an underestimate. Machine learning and artificial intelligence such as natural language processing has the potential to become a major technique for the detection, diagnosis, and treatment of people. (2) Methods: PubMed, EMBASE, MEDLINE, PsycInfo, and Global Health databases were searched for studies that reported use of NLP for suicide ideation or self-harm. (3) Result: The preliminary search of 5 databases generated 387 results. Removal of duplicates resulted in 158 potentially suitable studies. Twenty papers were finally included in this review. (4) Discussion: Studies show that combining structured and unstructured data in NLP data modelling yielded more accurate results than utilizing either alone. Additionally, to reduce suicides, people with mental problems must be continuously and passively monitored. (5) Conclusions: The use of AI&ML opens new avenues for considerably guiding risk prediction and advancing suicide prevention frameworks. The review's analysis of the included research revealed that the use of NLP may result in low-cost and effective alternatives to existing resource-intensive methods of suicide prevention.
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Affiliation(s)
- Abayomi Arowosegbe
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester M13 9PL, UK
| | - Tope Oyelade
- Division of Medicine, University College London, London NW3 2PF, UK
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Pap IA, Oniga S. A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11413. [PMID: 36141685 PMCID: PMC9517043 DOI: 10.3390/ijerph191811413] [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: 07/23/2022] [Revised: 08/31/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.
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Affiliation(s)
- Iuliu Alexandru Pap
- Department of Electric, Electronic and Computer Engineering, Technical University of Cluj-Napoca, North University Center of Baia Mare, 430083 Baia Mare, Romania
| | - Stefan Oniga
- Department of Electric, Electronic and Computer Engineering, Technical University of Cluj-Napoca, North University Center of Baia Mare, 430083 Baia Mare, Romania
- Department of IT Systems and Networks, Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary
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