The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis

Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review

OBJECTIVES

This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive effects on preventing, diagnosing, and treating depression. The systematic review establishes an evidence base for AI in depression management, offering directions for effective interventions.

METHODS

This systematic literature review investigates the effectiveness of AI in depression management by analyzing studies from January 1, 2017, to May 31, 2022. Utilizing search engines like IEEE Xplore, PubMed, and Web of Science, the review focused on keywords such as Depression/Mental Health, Machine Learning/Artificial Intelligence, and Prediction/Diagnosis. The analysis of 95 documents involved classification based on use, data type, and algorithm type.

RESULTS

The study revealed that AI in depression management excelled in accuracy, particularly in monitoring and prediction. Biomarker-derived data demonstrated the highest accuracy, with the CNN algorithm proving most effective. The findings affirm the therapeutic benefits of AI, including treatment, detection, and disease prediction, highlighting its potential in analyzing monitored data for depression management.

LIMITATIONS

This study exclusively examined the application of AI in individuals with depressive disorders. Interpretation should be cautious due to the limited scope of subjects to this specific population.

CONCLUSIONS

To introduce digital healthcare and therapies for ongoing depression management, it's crucial to present empirical evidence on the medical fee payment system, safety, and efficacy. These findings support enhanced medical accessibility through digital healthcare, offering personalized disease management for patients seeking non-face-to-face treatment.

The ethics of artificial intelligence systems in healthcare and medicine: from a local to a global perspective, and back

Artificial intelligence systems (ai-systems) (e.g. machine learning, generative artificial intelligence), in healthcare and medicine, have been received with hopes of better care quality, more efficiency, lower care costs, etc. Simultaneously, these systems have been met with reservations regarding their impacts on stakeholders' privacy, on changing power dynamics, on systemic biases, etc. Fortunately, healthcare and medicine have been guided by a multitude of ethical principles, frameworks, or approaches, which also guide the use of ai-systems in healthcare and medicine, in one form or another. Nevertheless, in this article, I argue that most of these approaches are inspired by a local isolationist view on ai-systems, here exemplified by the principlist approach. Despite positive contributions to laying out the ethical landscape of ai-systems in healthcare and medicine, such ethics approaches are too focused on a specific local healthcare and medical setting, be it a particular care relationship, a particular care organisation, or a particular society or region. By doing so, they lose sight of the global impacts ai-systems have, especially environmental impacts and related social impacts, such as increased health risks. To meet this gap, this article presents a global approach to the ethics of ai-systems in healthcare and medicine which consists of five levels of ethical impacts and analysis: individual-relational, organisational, societal, global, and historical. As such, this global approach incorporates the local isolationist view by integrating it in a wider landscape of ethical consideration so to ensure ai-systems meet the needs of everyone everywhere.

Artificial Intelligence in Malnutrition: A Systematic Literature Review

Malnutrition among the population of the world is a frequent yet underdiagnosed problem in both children and adults. Development of malnutrition screening and diagnostic tools for early detection of malnutrition is necessary to prevent long-term complications to patients' health and well-being. Most of these tools are based on predefined questionnaires and consensus guidelines. The use of artificial intelligence (AI) allows for automated tools to detect malnutrition in an earlier stage to prevent long-term consequences. In this study, a systematic literature review was carried out with the goal of providing detailed information on what patient groups, screening tools, machine learning algorithms, data types, and variables are being used, as well as the current limitations and implementation stage of these AI-based tools. The results showed that a staggering majority exceeding 90% of all AI models go unused in day-to-day clinical practice. Furthermore, supervised learning models seemed to be the most popular type of learning. Alongside this, disease-related malnutrition was the most common category of malnutrition found in the analysis of all primary studies. This research provides a resource for researchers to identify directions for their research on the use of AI in malnutrition.

The 100 most cited articles in artificial intelligence related to orthopedics

Background

This bibliometric study aimed to identify and analyze the top 100 articles related to artificial intelligence in the field of orthopedics.

Methods

The articles were assessed based on their number of citations, publication years, countries, journals, authors, affiliations, and funding agencies. Additionally, they were analyzed in terms of their themes and objectives. Keyword co-occurrence, co-citation of authors, and co-citation of references analyses were conducted using VOSviewer (version 1.6.19).

Results

The number of citations of these articles ranged from 32 to 272, with six papers having more than 200 citations The years of 2019 (n: 37) and 2020 (n: 19) together constituted 56% of the list. The USA was the leading contributor country to this field (n: 61). The most frequently used keywords were "machine learning" (n: 26), "classification" (n: 18), "deep learning" (n: 16), "artificial intelligence" (n: 14), respectively. The most common themes were decision support (n: 25), fracture detection (n: 24), and osteoarthrtitis staging (n: 21). The majority of the studies were diagnostic in nature (n: 85), with only two articles focused on treatment.

Conclusions

This study provides valuable insights and presents the historical perspective of scientific development on artificial intelligence in the field of orthopedics. The literature in this field is expanding rapidly. Currently, research is generally done for diagnostic purposes and predominantly focused on decision support systems, fracture detection, and osteoarthritis classification.

Bibliometric analysis of the global scientific production on machine learning applied to different cancer types

Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.

Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector

Artificial Intelligent (AI) applications in e-health have evolved considerably in the last 25 years. To track the current research progress in this field, there is a need to analyze the most recent trend of adopting AI applications in e-health. This bibliometric analysis study covers AI applications in e-health. It differs from the existing literature review as the journal articles are obtained from the Scopus database from its beginning to late 2021 (25 years), which depicts the most recent trend of AI in e-health. The bibliometric analysis is employed to find the statistical and quantitative analysis of available literature of a specific field of study for a particular period. An extensive global literature review is performed to identify the significant research area, authors, or their relationship through published articles. It also provides the researchers with an overview of the work evolution of specific research fields. The study's main contribution highlights the essential authors, journals, institutes, keywords, and states in developing the AI field in e-health.

Plant-Based Meat Analogues from Alternative Protein: A Systematic Literature Review

This study aimed to conduct a systematic literature review (SLR) of the research performed in the plant-based meat analogues area. Historical, current, and future tendencies are discussed. The paper offers a comprehensive SLR coupled with a bibliometric analysis of the publication from 1972 to January 2022. The articles were obtained using a research string and precise inclusion and exclusion criteria from two prominent databases, Scopus and Web of Science (WoS). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow technique was used to describe the data screening and selection. In total, 84 publications were selected for further analysis after a thorough literature assessment. From this study, six main themes were identified: (1) objectives of the study; (2) type of plant protein; (3) product type; (4) added ingredients; (5) texturization technique; and (6) quality assessment considered in the studies. Recent trends in publication imply that meat analogue technology is gaining prominence. This review revealed significant research on improving meat analogues via texturization. Even though extrusion is used industrially, the technique is still in its infancy and needs improvement. Future studies should focus more on fiber and protein-protein interactions, macromolecule conformation and mechanisms, diversifying or improving current methods, sensory attributes, and gastrointestinal absorption rate of each novel protein ingredient.

Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students’ Diagnostic Skills

Knowing how to diagnose effectively and efficiently is a fundamental skill that a good dental professional should acquire. If students perform a greater number of clinical cases, they will improve their performance with patients. In this sense, virtual patients with artificial intelligence offer a controlled, stimulating, and safe environment for students. To assess student satisfaction after interaction with an artificially intelligent chatbot that recreates a virtual patient, a descriptive cross-sectional study was carried out in which a virtual patient was created with artificial intelligence in the form of a chatbot and presented to fourth and fifth year dental students. After several weeks interacting with the AI, they were given a survey to find out their assessment. A total of 193 students participated. A large majority of the students were satisfied with the interaction (mean 4.36), the fifth year students rated the interaction better and showed higher satisfaction values. The students who reached a correct diagnosis rated this technology more positively. Our research suggests that the incorporation of this technology in dental curricula would be positively valued by students and would also ensure their training and adaptation to new technological developments.

An insight into diagnosis of depression using machine learning techniques: a systematic review

BACKGROUND

In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis.

METHODS

This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis.

RESULTS

A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test.

CONCLUSION

The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.

Burnout amongst radiologists: A bibliometric study from 1993 to 2020

BACKGROUND

Burnout amongst radiologists is common in many different institutions and is increasing day by day. To battle burnout, we have to address the root causes already recognized in published literature. Therefore, it is crucial to examine and discern important publications.

AIM

To provide evidence-based data and trends related to burnout in radiologists so that researchers can work on it further and develop preventive strategies to overcome this problem.

METHODS

Bibliometric analysis conducted by two independent reviewers separately used Scopus Library for data extraction by using medical subject heading and International Classification of Diseases keywords. Forty-nine articles were selected for analysis after an extensive scrutiny. Statistical Package for the Social Sciences version 20 was used for analysis. Pearson correlation coefficient, Kruskal Wallis test, and Mann-Whitney U test were applied.

RESULTS

The most productive period with regards to the number of publications was between 2017 and 2019. A total of 160 authors contributed to the topic burnout among radiologists, with an average of 3.26 authors per paper. About 41.68% of the authors were female, whilst 35% of them were first authors. The co-citation analysis by author involved 188 cited authors, 13 of whom were cited at least 70 times. Only six out of forty-nine studies were funded by various government institutions and non-governmental organizations.

CONCLUSION

Current analysis casts a spotlight on important trends being witnessed in regard to the mental health of radiologists, including lack of funding for mental health research, narrowing of female vs male citation gap, as well as authorship and citation trends.

Translational Informatics for Natural Products as Antidepressant Agents

Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

Changes of adrenocorticotropic hormone rhythm and cortisol circadian rhythm in patients with depression complicated with anxiety and their effects on the psychological state of patients

Objective: This work was to explore the rhythm of adrenocorticotropic hormone (ACTH) and cortisol in patients with depression and anxiety and their effects on mental state. In this work, with depression complicated with anxiety patients as the A-MDD group (n = 21), and depression without anxiety symptoms as the NA-MDD group (n = 21). Firstly, data features were extracted according to the electroencephalo-graph (EEG) data of different patients, and a DR model was constructed for diagnosis. The Hamilton Depression Scale 24 (HAMD-24) was employed to evaluate the severity, and the ACTH and cortisol levels were detected and compared for patients in the A-MDD group and NA-MDD group. In addition, the psychological status of the patients was assessed using the Toronto Alexithymia Scale (TAS). As a result, the AI-based DR model showed a high recognition accuracy for depression. The HAMD-24 score in the A-MDD group (31.81 ± 5.39 points) was statistically higher than the score in the NA-MDD group (25.25 ± 5.02 points) (P < 0.05). No visible difference was found in ACTH levels of patients in different groups (P > 0.05). The incidence of cortisol rhythm disorder (CRD) in the A-MDD group was much higher (P < 0.05). The differences in TAS scores between the two groups were significantly statistically significant (P < 0.01). In conclusion, the AI-based DR Model achieves a more accurate identification of depression; depression with or without anxiety has different effects on the mental state of patients. CRD may be one of the biological markers of depression combined with anxiety.

Tracking developments in artificial intelligence research: constructing and applying a new search strategy

Artificial intelligence, as an emerging and multidisciplinary domain of research and innovation, has attracted growing attention in recent years. Delineating the domain composition of artificial intelligence is central to profiling and tracking its development and trajectories. This paper puts forward a bibliometric definition for artificial intelligence which can be readily applied, including by researchers, managers, and policy analysts. Our approach starts with benchmark records of artificial intelligence captured by using a core keyword and specialized journal search. We then extract candidate terms from high frequency keywords of benchmark records, refine keywords and complement with the subject category “artificial intelligence”. We assess our search approach by comparing it with other three recent search strategies of artificial intelligence, using a common source of articles from the Web of Science. Using this source, we then profile patterns of growth and international diffusion of scientific research in artificial intelligence in recent years, identify top research sponsors in funding artificial intelligence and demonstrate how diverse disciplines contribute to the multidisciplinary development of artificial intelligence. We conclude with implications for search strategy development and suggestions of lines for further research.

Leveraging the trust of nurses to advance a digital agenda in Europe: a critical review of health policy literature

This article is a critical and integrative review of health policy literature examining artificial intelligence (AI) and its implications for healthcare systems and the frontline nursing workforce. A key focus is on co-creation as essential for the deployment and adoption of AI. Our review hinges on the European Commission's White Paper on Artificial Intelligence from 2020, which provides a useful roadmap. The value of health data spaces and electronic health records (EHRs) is considered; and the role of advanced nurse practitioners in harnessing the potential of AI tools in their practice is articulated. Finally, this paper examines "trust" as a precondition for the successful deployment and adoption of AI in Europe. AI applications in healthcare can enhance safety and quality, and mitigate against common risks and challenges, once the necessary level of trust is achieved among all stakeholders. Such an approach can enable effective preventative care across healthcare settings, particularly community and primary care. However, the acceptance of AI tools in healthcare is dependent on the robustness, validity and reliability of data collected and donated from EHRs. Nurse stakeholders have a key role to play in this regard, since trust can only be fostered through engaging frontline end-users in the co-design of EHRs and new AI tools. Nurses hold an intimate understanding of the direct benefits of such technology, such as releasing valuable nursing time for essential patient care, and empowering patients and their family members as recipients of nursing care. This article brings together insights from a unique group of stakeholders to explore the interaction between AI, the co-creation of data spaces and EHRs, and the role of the frontline nursing workforce. We identify the pre-conditions needed for successful deployment of AI and offer insights regarding the importance of co-creating the future European Health Data Space.

Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression

BACKGROUND

Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression, basic probabilistic machine learning (ML) methods, and automated ML (Auto-sklearn).

METHODS

Data were derived from the Netherlands Study of Depression and Anxiety. We compared how well multinomial logistic regression, a naïve Bayes classifier, and Auto-sklearn predicted depression and anxiety diagnoses at a 2-, 4-, 6-, and 9-year follow up, operationalized as binary or categorical variables. Predictor sets included demographic and self-report data, which can be easily collected in clinical practice at two initial time points (baseline and 1-year follow up).

RESULTS

At baseline, participants were 42.2 years old, 66.5% were women, and 53.6% had a current mood or anxiety disorder. The three methods were similarly successful in predicting (mental) health status, with correct predictions for up to 79% (95% CI 75-81%). However, Auto-sklearn was superior when assessing a more complex dataset with individual item scores.

CONCLUSIONS

Automated ML methods added only limited value, compared to traditional data modelling when predicting the onset and course of depression and anxiety. However, they hold potential for automatization and may be better suited for complex datasets.

Mental health disorders research in the countries of the Organisation of Islamic Cooperation (OIC), 2008-17, and the disease burden: Bibliometric study

The 57 countries of the Organisation of Islamic Cooperation are suffering from an increasing burden from mental health disorders. We investigated their research outputs during 2008–17 in the Web of Science in order to compare them with the burden from different mental health disorders and in different countries. The papers were identified with a complex filter based on title words and journals. Their addresses were parsed to give fractional country counts, show international collaboration, and also reveal country concentration on individual disorders and types of research. We found 17,920 papers in the decade, with output quadrupling. Foreign contributions accounted for 15% of addresses; they were from Europe (7%), Canada + USA (5%) and elsewhere (3%). They were much greater for Qatar and Uganda (> 60%), but less than 10% for Iran and Turkey. Schizophrenia and bipolar disorder were over-researched, but suicide and self-harm were seriously neglected, relative to their mental health disorder burdens. Although OIC research has been expanding rapidly, some countries have published little on this subject, perhaps because of stigma. Turkey collaborates relatively little internationally and as a result its papers received few citations. Among the large OIC countries, it has almost the highest relative mental health disorders burden, which is also growing rapidly.

Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review

BACKGROUND

Despite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research.

METHODS

The OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria.

RESEARCH

3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face.

CONCLUSION

Research widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.

Machine Learning for Mental Health in Social Media: Bibliometric Study

Background

Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention.

Objective

We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media.

Methods

Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described.

Results

We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated.

Conclusions

The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

Mapping the Worldwide Trends on Energy Poverty Research: A Bibliometric Analysis (1999-2019)

Energy poverty is one of the main challenges facing humanity in the 21st century. Research on energy poverty is becoming a common focus of scholars in many areas. Bibliometrics can help researchers dig deep into the information of specific research fields from a quantitative perspective. In this study, we collected 1018 research papers in the field of energy poverty published in the period 1999–2019 from the Web of Science databases and conducted a bibliometric analysis on them. Cleaning and screening of sample papers, matrix construction, and visualization were performed using Bibliometrix, VOSviewer, and HistCite, summarizing the internal and external characteristics of the papers. With regard to external characteristics, a total of 982 research institutions in 80 regions conducted research in this field. There is extensive cooperation between the countries, and the UK, the USA, Australia, and Italy play the most active role in the cooperation network. With regard to internal characteristics, we found the two most representative citation paths: one path starts from the concerns of energy-poor groups and stops at an ethical discussion on energy poverty; the second path is based on the existing technological path, continuously developing coping policies, evaluation methods, and a conceptual framework for dealing with energy poverty. Furthermore, through coupling analysis, we discovered four focuses of energy poverty research: improvement of definition, improvement of evaluation methods, effects of coping policy, and energy justice. Through a comprehensive analysis of existing papers, this paper reveals some limitations of previous studies and recommends some promising directions for future research on energy poverty.

Personalized Medicine Using Neuroimmunological Biomarkers in Depressive Disorders

Major depressive disorder (MDD) is associated with increased suicidal risk and reduced productivity at work. Neuroimmunology, the study of the immune system and nervous system, provides further insight into the pathogenesis and outcome of MDD. Cytokines are the main modulators of neuroimmunology, and their levels are somewhat entangled in depressive disorders as they affect depressive symptoms and are affected by antidepressant treatment. The use of cytokine-derived medication as a treatment option for MDD is currently a topic of interest. Although not very promising, cytokines are also considered as possible prognostic or diagnostic markers for depression. The machine learning approach is a powerful tool for pattern recognition and has been used in psychiatry for finding useful patterns in data that have translational meaning and can be incorporated in daily clinical practice. This review focuses on the current knowledge of neuroimmunology and depression and the possible use of machine learning to widen our understanding of the topic.

A bibliometric analysis of top 50-most cited articles on repetitive trans-cranial magnetic stimulation (rTMS) for treatment of depression

Background

Citation count can be used as a key tool to assess the quality of the published literature and because of its immense advantages it is now widely used in ranking the articles on specific topics.

Objective/hypothesis

To extract and assess the top cited work on repetitive transcranial magnetic stimulation (rTMS) for depression treatment.

Methods

Scopus Library Database was searched and two independent authors produced a list of 50 most cited articles on repetitive transcranial magnetic stimulation (rTMS) for treatment of depression. All the relevant articles having key-terms within their titles, abstract and keywords were included in our search. Our list was categorized into two categories, “mixed” and “focused”.

Results

The articles in the produced list of top 50 most cited articles on rTMS for treatment of depression belong to the time period 1993–2012 with total citation count 12078. George MS was prominent in the list. ‘Biological Psychiatry’ published most number of articles (n = 13) among the list. Articles were categorized on the basis of primary population and intervention into ‘Focused’ and ‘Mixed’ categories.

Limitations

Articles that were published before 1993 and after 2012 on rTMS for depression couldn't made it to the final list of top-50 most cited article.

Conclusion

We attempted to conduct a topic-specific citation analysis considering the paucity of specified bibliometrics in medical literature. Our research provides an insight on emerging trends in rTMS for depression and highlights the characteristics, quality and dynamics of frequently cited articles in the field.

Published Research on Burnout in Nursing in Spain in the Last Decade: Bibliometric Analysis

Scientific production in the last decades has evidenced an increase in burnout syndrome in healthcare professionals. The objective of this bibliometric study was to analyze scientific productions on burnout in nurses in 2009–2019. A search was made on the Web of Science database on burnout in nursing. The variables evaluated were number of publications per year, productivity based on the journal and relationships between authors. Data were analyzed using Bibexcel software, and Pajek was used to visualize the co-authorship network map. A total of 1528 publications related to burnout in nurses were identified. The years with the most productivity were 2016 to 2017, when the publication rate increased noticeably over previous years. The Spanish journal with the most production on the subject was Atención Primaria. The co-authorship network analyzed illustrated collaboration patterns among the researchers. Scientific publications on the subject have increased in recent years due to problems in the healthcare system, which is in need of prevention and intervention programs for healthcare professionals.

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics

There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.

Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects

Artificial intelligence (AI)-aided decision support has developed rapidly to meet the needs for effective analysis of substantial data sets from electronic medical records and medical images generated daily, and computer-assisted intelligent drug design. In clinical practice, paediatricians make medical decisions after obtaining a large amount of information about symptoms, physical examinations, laboratory test indicators, special examinations and treatments. This information is used in combination with paediatricians' knowledge and experience to form the basis of clinical decisions. This diagnosis and therapeutic strategy development based on large amounts of information storage can be applied to both large clinical databases and data for individual patients. To date, AI applications have been of great value in intelligent diagnosis and treatment, intelligent image recognition, research and development of intelligent drugs and intelligent health management. This review aims to summarize recent advances in the research and clinical use of AI in paediatrics.

Artificial Intelligence in Health Care: Bibliometric Analysis

Background

As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly.

Objective

The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications.

Methods

The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software.

Results

The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019.

Conclusions

This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

Evidence Regarding Automatic Processing Computerized Tasks Designed For Health Interventions in Real-World Settings Among Adults: Systematic Scoping Review

Background

Dual process theories propose that the brain uses 2 types of thinking to influence behavior: automatic processing and reflective processing. Automatic processing is fast, immediate, nonconscious, and unintentional, whereas reflective processing focuses on logical reasoning, and it is slow, step by step, and intentional. Most digital psychological health interventions tend to solely target the reflective system, although the automatic processing pathway can have strong influences on behavior. Laboratory-based research has highlighted that automatic processing tasks can create behavior change; however, there are substantial gaps in the field on the design, implementation, and delivery of automatic processing tasks in real-world settings. It is important to identify and summarize the existing literature in this area to inform the translation of laboratory-based research to real-world settings.

Objective

This scoping review aims to explore the effectiveness of automatic training tasks, types of training tasks commonly used, mode of delivery, and impacts of gamification on automatic processing tasks designed for digital psychological health interventions in real-world settings among adults.

Methods

The scoping review methodology proposed by Arskey and O’Malley and Colquhoun was applied. A scoping review was chosen because of the novelty of the digital automatic processing field and to encompass a broad review of the existing evidence base. Electronic databases and gray literature databases were searched with the search terms “automatic processing,” “computerised technologies,” “health intervention,” “real-world,” and “adults” and synonyms of these words. The search was up to date until September 2018. A manual search was also completed on the reference lists of the included studies.

Results

A total of 14 studies met all inclusion criteria. There was a wide variety of health conditions targeted, with the most prevalent being alcohol abuse followed by social anxiety. Attention bias modification tasks were the most prevalent type of automatic processing task, and the majority of tasks were most commonly delivered over the web via a personal computer. Of the 14 studies included in the review, 8 demonstrated significant changes to automatic processes and 4 demonstrated significant behavioral changes as a result of changed automatic processes.

Conclusions

This is the first review to synthesize the evidence on automatic processing tasks in real-world settings targeting adults. This review has highlighted promising, albeit limited, research demonstrating that automatic processing tasks may be used effectively in a real-world setting to influence behavior change.

Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

Background

Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis.

Objective

Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges.

Methods

We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work.

Results

The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements.

Conclusions

Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.

Meta-analysis of 5-hydroxytryptamine transporter gene promoter region polymorphism and post-stroke depression

OBJECTIVE

To investigate the relationship between 5-hydroxytryptamine transporter gene promoter region (5-HTTLPR) gene polymorphism and post-stroke depression (PSD).

METHODS

We searched the CNKI, China Science and Technology Journal, China WanFang, PubMed, Embase, and Web of Science databases for studies of the relationship between 5-HTTLPR polymorphism and PSD. Data were evaluated using Stata software.

RESULTS

The L allele was significantly related to the S allele (OR = 0.57, 95% confidence interval (CI) 0.49-0.65). The dominant genotype LL + LS was related to SS (OR = 0.48, 95%CI 0.39-0.59), the recessive genotype LL was related to LS + SS (OR = 0.39, 95%CI: 0.30-0.51), the homozygous genotype LL was related to SS (OR = 0.24, 95%CI 0.18-0.33), and the heterozygous genotype LS was related to SS (OR = 0.55, 95 CI 0.44-0.68). All the differences were significant. Ethnicity subgroup analysis showed significant differences among the five genotypes in both Asians and Caucasians. Hardy-Weinberg equilibrium (HWE) subgroup analysis showed that, after removal of a non-HWE-conforming control group, all five genotypes were significant and genotypes LL, LS + LL, and LS and L allele had beneficial effects on recovery from PSD.

CONCLUSION

5-HTTLPR gene polymorphism is strongly associated with PSD, and the LL, LS + LL, and LS genotypes and L allele may protect against this condition.

Metastatic castration-resistant prostate cancer: Academic insights and perspectives through bibliometric analysis

BACKGROUND

In recent years, metastatic castration-resistant prostate cancer (MCRPC) and studies related to MCRPC have drawn global attention. The main objective of this bibliometric study was to provide an overview of MCRPC, explore clusters and trends in research and investigate the future direction of MCRPC research.

METHODS

A total of 4089 publications published between 1979 and 2018 were retrieved from the Web of Science (WoS) Core Collection database. Different aspects of MCRPC research, including the countries/territories, institutions, journals, authors, research areas, funding agencies and author keywords, were analyzed.

RESULTS

The number of annual MCRPC publications increased rapidly after 2010. American researchers played a vital role in this increase, as they published the most publications. The most productive institution was Memorial Sloan Kettering Cancer Center. De Bono, JS (the United Kingdom [UK]) and Scher, HI (the United States of America [USA]) were the two most productive authors. The National Institutes of Health (NIH) funded the largest number of published papers. Analyses of keywords suggested that therapies (abiraterone, enzalutamide, etc.) would attract global attention after US Food and Drug Administration (FDA) approval.

CONCLUSIONS

Developed countries, especially the USA, were the leading nations for MCRPC research because of their abundant funding and frequent international collaborations. Therapy was one of the most vital aspects of MCRPC research. Therapies targeting DNA repair or the androgen receptor (AR) signing pathway and new therapies especially prostate-specific membrane antigen (PSMA)-based radioligand therapy (RLT) would be the next focus of MCRPC research.

A Scientometric Study on Depression among University Students in East Asia: Research and System Insufficiencies?

Given that mental health issues are acute in Asian countries, particularly Japan and Korea, and university students are more vulnerable to depression than the general population, this study aims to examine the landscapes of scientific research regarding depressive disorders among university students and evaluate the effectiveness of international collaboration and funding provision on the scientific impact in Korea, Japan, and China. Based on articles retrieved from the Web of Science database during the period 1992–2018, we found that the number of scientific publications, international collaborations, and allocated funds regarding depressive disorder among university students in China (97 articles, 43 international collaborations, and 52 funds provided, respectively) overwhelmingly surpassed the case of Korea (37 articles, 12 international collaborations, and 15 funds provided, respectively) and Japan (24 articles, 5 international collaborations, and 6 funds provided, respectively). The differences in collaboration patterns (p-value < 0.05) and the proportion of allocated funds (p-value < 0.05) among Korea, Japan, and China were also noted using Fisher’s exact test. Based on the Poisson regression analysis, China’s associations of scientific impact with international collaboration (β = −0.322, p-value < 0.01) and funding provision (β = −0.397, p-value < 0.01) are negative, while associations of the scientific impact and scientific quality with funding provision and international collaboration were statistically insignificant. These findings hint that Korea and Japan lacked scientific output, diversity in research targets, international collaboration, and funding provision, compared to China, but the quality of either China’s internationally collaborated or funded articles was contentious. As a result, policymakers in Korea and Japan are suggested to raise the importance of mental health problems in their future policy planning and resource distribution. Moreover, it would be advisable to establish a rigorous system of evaluation for the quality of internationally collaborated and funded studies in order to increase scientific impact and maintain public trust, especially in China.

What Does Personality Mean in the Context of Mental Health? A Topic Modeling Approach Based on Abstracts Published in Pubmed Over the Last 5 Years

Personality disorders (PDs) are one of the major problems for the organization of public health systems. Deepening the link between personality traits and psychopathological drifts, it seems increasingly essential for the often dramatic repercussions that PDs have on social contexts. Some of these disorders, such as borderline PD, antisocial PD, in their most tragic expression, are the basis of problems related to crime, sexual violence, abuse, and mistreatment of minors. Many authors propose a dimensional classification of personality pathology, which has received empirical support from numerous studies over the last 20 years based on more robust theoretical principles than those applied to current nosography. The present study investigates the nature of the research carried out in the last years on the personality in the clinical field exploring the contents of current research on personality relapses, evaluating, on the one hand, the emerging areas of greatest interest and others, those that they stopped generating sufficient motivations in scholars. This study evaluates text patterns regarding how the terms "personality" and "mental health" are used in titles and abstracts published in PubMed in the last 5 years. We use a topic analysis: Latent Dirichlet Allocation that expresses every report as a probabilistic distribution of latent topics that are represented as a probabilistic distribution of words. A total of 7,572 abstracts (from 2012 to 2017) were retrieved from PubMed for the query on "mental health" and "personality." The study found 30 topics organized in eight hierarchical clusters that describe the type of current research carried out on personality and its clinical relapse. The hierarchical clusters latent themes were the following: social dimensions, clinical aspects, biological issues, clinical history of PD, internalization and externalization symptoms, impulsive behaviors, comorbidities, criminal behaviors. The results indicate that the concept of personality is associated with a wide range of conditions. The study of personality and mental health still proceeds, mainly, according to a practical-clinical approach; too little moves, however, according to an innovative research approach, but the work shows the common commitment of scholars to a new way of dealing with the study of personality.