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Objective: Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts., Method: A systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach., Results: Forty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing. The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction. Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts., Conclusions: The effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification., (© 2024. The Author(s).)

Background and Aim: Schizophrenia and bipolar disorder (BD) are critical and high‐risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods: A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results: In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full‐text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion: ML can precisely predict results and assist in making clinical decisions‐concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research. [ABSTRACT FROM AUTHOR]

Introduction: Epidemic diseases have always caused considerable physical and financial casualties for governments. By the end of the year 2019, the Covid19 pandemic emerged for the first time in China and rapidly infected the globe. Objectives: As information technology plays a significant role in the current healthcare system, the aim of the present study was to conduct a systematic review to determine the role of electronic health in the Covid19 crisis. Material and Methods: This review was carried out on articles published from December 2019 until March 17th 2020 by searching keywords and their equivalents in "MeSH" in PubMed, Web of Science, and Scopus databases and Google search engine. Results: In total, from 72 found articles, 28 were recognized based on their research topic. After imposing inclusion and exclusion criteria, eventually 6 original articles and 8 reports were selected for further analysis. Results showed that reviewed articles had mentioned the effective role of IT in: diagnosing Corona patients, addressing the spread of the disease, providing sufficient education for the public to prevent the disease, and recognizing high-risk areas. Telemedicine, machine learning algorithms, deep learning, Augmented intelligence, neural networks, Global positioning system, and geographical information system have been the most widely used technologies. Conclusion: It was shown that defeating the Covid19 is impossible without the help of technology. Experiences with the effectivity of using electronic health in controlling and monitoring the prevalence of Covid19 can be used to deal with other pandemic diseases in the future as well; and to avoid possible casualties and economic regressions while rapidly providing solutions for similar critical situations. [ABSTRACT FROM AUTHOR]

As someone who has been following the development of hyper-automation technologies in healthcare, I wanted to write to you about the many optimistic outcomes that these technologies have already produced. I am writing to express my excitement about many potential and benefits of hyper-automation technologies in healthcare. Hyper-automation, which includes the use of smart technologies such as artificial intelligence, lowcode/no-code (LCNC) platforms, machine learning, robotics and other technologies to automate and optimize processes, has the possibility to transform healthcare in many ways. [ABSTRACT FROM AUTHOR]