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Artificial Intelligence is deployed in multiple areas, including healthcare. Utmost research is done in AI enabled healthcare industry because of the demands like accurate result, data security, exact prediction, huge volume of data, etc. In conventional deep learning models, the training happens with the dataset that are stored in a single device. This requires a huge storage space and highly efficient machines to train the data. Usage of big data, demands for innovative models that can be deployed and used in confined storage. Split learning is one such collaborative distributed deep learning model that allows the data to be stored in a split fashion. Split learning supports desirable features like less storage, more privacy to raw data, ability to work with resource constraints, etc., making it suitable for storing electronic medical record of patients. This paper discusses the advantages of using split learning for healthcare, the possible configurations of split learning that supports data privacy in healthcare and finally discusses the open research challenges in implementing split learning for healthcare. [ABSTRACT FROM AUTHOR]

With the rapid evolution and technology advancement, the healthcare sector is evolving day by day. It is taking advantage of different technologies such as Internet of things and Blockchain. Several applications related to daily healthcare activities are adopting the use of these technologies. In this paper, we present a review in which we group different healthcare applications that integrate the Internet of things and Blockchain in their systems. A review study about the integration of IoT and Blockchain in healthcare systems was conducted. We searched the databases ScienceDirect, IEEE Xplore, Google Scholar and ACM Digital Library. This review focuses on categorizing the use cases of IoT and Blockchain in the healthcare sector. The study listed 6 applications in medical services, namely, remote patient monitoring, electronic medical records management, disease prediction, patient tracking, drug traceability and fighting infectious disease especially COVID-19. The paper also investigates the challenges associated with the adoption of the Blockchain technology in healthcare IoT-based systems and some of the existing solutions. It also introduces some future research directions. The survey of the use cases of IoT and Blockchain in the healthcare sector will serve as a state of the art for future researches. In addition, the paper gives some directions to new possible researches that could help to revolutionize the healthcare sector by using other technologies such as artificial intelligence, big data, fog and cloud computing. [ABSTRACT FROM AUTHOR]

• Heterogeneous feature learning. Exploiting the complementarity of numerical data and text data to address the issue of data sparsity. • Novel multi-task model. A Shared-bottom layer is introduced between the MoE layer and the input layer of MMoE to reduce feature dimension. • Practical application. The output layer of Shared-MMOE is modified to multi-classification mode, enabling the diagnosis of 17 kinds of diseases and pests and the recommendation of 32 kinds of medicines. • Sequential dependence handling for cascaded task. In order to improve the recommendation effect of medication tasks, the output of diagnostic task is taken as the input of medication task in the form of probability weighting. Research on diagnosis of crop diseases and pests becomes a hot topic of the application of artificial intelligence technology in smart agriculture. Plant electronic medical records (PEMRs) formed by Beijing Plant Clinic provides a new idea for the diagnosis and prevention of crop diseases and pests. PEMRs are stored in the form of heterogeneous data, containing a wealth of plant information, disease and pest information, and environmental information. Therefore, it is urgent to mine the information in PEMRs and employ it to assist in intelligent prescription recommendation. This paper divides prescription recommendation into two sub-tasks, diagnosis and medication, and transforms this problem into a recommendation problem based on multi-task learning, with the goal of establishing a single model to realize learning multi-task simultaneously. Firstly, the correlation analysis of tasks and features is carried out using methods such as knowledge graph. Further, according to the sequential dependency between tasks, a novel cascaded multi-task crop prescription recommendation method based on Shared-Bottom and MMoE (Shared-MMoE) model is proposed, and each task is optimized by gating network. A PEMRs dataset containing 8 diseases, 9 pests and 32 medicines was constructed for model verification. Compared with the baseline model, the experiments showed that Shared-MMoE could significantly improve the quality and accuracy of prescription recommendation. The AUC of diagnosis task and medication task reached 96.33% and 95.36%, respectively. In conclusion, our study preliminarily explored the potential application of artificial intelligence in the research of crop diseases and pests based on PEMRs and multi-task learning. [ABSTRACT FROM AUTHOR]

Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients. This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis. A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included. The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development. There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods. • Systematic review of ML methods for predicting hospital-acquired pressure injuries. • Systematic review of Decision Support systems for predicting hospital-acquired pressure injuries. • Bibliometric review on HAPI prediction: trends in keywords, top-cited studies, and publishing countries. • Summary of the study's input data, time span, results, and key contributions. • Gaps and solutions for ML and DSS models in HAPI prediction. [ABSTRACT FROM AUTHOR]

Background: Early detection and prediction of suicidal behaviour are key factors in suicide control. In conjunction with recent advances in the field of artificial intelligence, there is increasing research into how machine learning can assist in the detection, prediction and treatment of suicidal behaviour. Therefore, this study aims to provide a comprehensive review of the literature exploring machine learning techniques in the study of suicidal behaviour prediction.Methods: A search of four databases was conducted: Web of Science, PubMed, Dimensions, and Scopus for research papers dated between January 2016 and September 2021. The search keywords are 'data mining', 'machine learning' in combination with 'suicidal behaviour', 'suicide', 'suicide attempt', 'suicidal ideation', 'suicide plan' and 'self-harm'. The studies that used machine learning techniques were synthesized according to the countries of the articles, sample description, sample size, classification tasks, number of features used to develop the models, types of machine learning techniques, and evaluation of performance metrics.Results: Thirty-five empirical articles met the criteria to be included in the current review. We provide a general overview of machine learning techniques, examine the feature categories, describe methodological challenges, and suggest areas for improvement and research directions. Ensemble prediction models have been shown to be more accurate and useful than single prediction models.Conclusions: Machine learning has great potential for improving estimates of future suicidal behaviour and monitoring changes in risk over time. Further research can address important challenges and potential opportunities that may contribute to significant advances in suicide prediction. [ABSTRACT FROM AUTHOR]

Background: The recent, rapid development of digital technologies offers new possibilities for more efficient implementation of electronic health record (EHR) and personal health record (PHR) systems. A growing volume of healthcare data has been the hallmark of this digital transformation. The large healthcare datasets' complexity and their dynamic nature pose various challenges related to processing, analysis, storage, security, privacy, data exchange, and usability.Materials and Methods: We performed a systematic review of systematic reviews to assess technological progress in EHR and PHR systems. We searched MEDLINE, Cochrane, Web of Science, and Scopus for systematic literature reviews on technological advancements that support EHR and PHR systems published between January 1, 2010, and October 06, 2020.Results: The searches resulted in a total of 2,448 hits. Of these, we finally selected 23 systematic reviews. Most of the included papers dealt with information extraction tools and natural language processing technology (n = 10), followed by studies that assessed the use of blockchain technology in healthcare (n = 8). Other areas of digital technology research included EHR and PHR systems in austere settings (n = 1), de-identification methods (n = 1), visualization techniques (n = 1), communication tools within EHR and PHR systems (n = 1), and methodologies for defining Clinical Information Models that promoted EHRs and PHRs interoperability (n = 1).Conclusions: Technological advancements can improve the efficiency in the implementation of EHR and PHR systems in numerous ways. Natural language processing techniques, either rule-based, machine-learning, or deep learning-based, can extract information from clinical narratives and other unstructured data locked in EHRs and PHRs, allowing secondary research (i.e., phenotyping). Moreover, EHRs and PHRs are expected to be the primary beneficiaries of the blockchain technology implementation on Health Information Systems. Governance regulations, lack of trust, poor scalability, security, privacy, low performance, and high cost remain the most critical challenges for implementing these technologies. [ABSTRACT FROM AUTHOR]