Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service.

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short-Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.

Online adaptive assistance control in robot-based neurorehabilitation therapy.

Repetitive and task specific robot-based rehabilitation has been proved to be effective for motor recovery over time. During a therapy, the task should improve subject's impaired movements, but also enhance their efforts for a more effective recovery. This requires an accurate tuning of the task difficulty, which should be tailored directly to the patient. In this work, we propose a system for real-time assistance adaptation based on online performance evaluation for post-stroke subjects. In particular, the aim of the system is to implement the "assist-as-needed" paradigm based on actual patients' motor skills during a therapy session with an active upper-limb robotic exoskeleton. The strength of the work is to propose a real-time algorithm for the assistance tuning based on an "assistance-performance" relationship. Such a relationship is based on experimental measurements, and allows the algorithm to compute a straightforward calculation of the assistance required. Finally, an assessment phase will show how the system provides assistance based on the difficulties experienced from the subjects, also facilitating their adaptation during the task.

Kids (learn how to) save lives in the school with the serious game Relive.

INTRODUCTION: Relive is a serious game focusing on increasing kids and young adults' awareness on CPR. We evaluated the use of Relive on schoolchildren. METHODS: A longitudinal, prospective study was carried out in two high schools in Italy over a 8-month period, divided in three phases: baseline, competition, and retention. Improvement in schoolchildren's CPR awareness, in terms of knowledge (MCQ results) and skills (chest compression (CC) rate and depth), was evaluated. Usability of Relive and differences in CC performance according to sex and BMI class were also evaluated. RESULTS: At baseline, students performed CC with a mean depth of 31mm and a rate of 95 cpm. In the competition phase, students performed CC with a mean depth of 46mm and a rate of 111 cpm. In the retention phase, students performed CC with a mean depth of 47mm and a rate of 131 cpm. Thus, the training session with Relive during the competition phase affected positively both CC depth (p<0.001) and rate (p<0.001). Such an effect persisted up to the retention phase. CC depth was also affected by gender (p<0.01) and BMI class (p<0.01). Indeed, CC depth was significantly greater in male players and in players with higher BMI. Seventy-three percent of students improved their CPR knowledge as represented by an increases in the MCQ score (p<0.001). The participants perceived the Relive to be easy to use with effective feedback. CONCLUSIONS: Relive is an useful tool to spread CPR knowledge and improve CPR skills in schoolchildren.

EasyCluster2: an improved tool for clustering and assembling long transcriptome reads.

BACKGROUND: Expressed sequences (e.g. ESTs) are a strong source of evidence to improve gene structures and predict reliable alternative splicing events. When a genome assembly is available, ESTs are suitable to generate gene-oriented clusters through the well-established EasyCluster software. Nowadays, EST-like sequences can be massively produced using Next Generation Sequencing (NGS) technologies. In order to handle genome-scale transcriptome data, we present here EasyCluster2, a reimplementation of EasyCluster able to speed up the creation of gene-oriented clusters and facilitate downstream analyses as the assembly of full-length transcripts and the detection of splicing isoforms. RESULTS: EasyCluster2 has been developed to facilitate the genome-based clustering of EST-like sequences generated through the NGS 454 technology. Reads mapped onto the reference genome can be uploaded using the standard GFF3 file format. Alignment parsing is initially performed to produce a first collection of pseudo-clusters by grouping reads according to the overlap of their genomic coordinates on the same strand. EasyCluster2 then refines read grouping by including in each cluster only reads sharing at least one splice site and optionally performs a Smith-Waterman alignment in the region surrounding splice sites in order to correct for potential alignment errors. In addition, EasyCluster2 can include unspliced reads, which generally account for >50% of 454 datasets, and collapses overlapping clusters. Finally, EasyCluster2 can assemble full-length transcripts using a Directed-Acyclic-Graph-based strategy, simplifying the identification of alternative splicing isoforms, thanks also to the implementation of the widespread AStalavista methodology. Accuracy and performances have been tested on real as well as simulated datasets. CONCLUSIONS: EasyCluster2 represents a unique tool to cluster and assemble transcriptome reads produced with 454 technology, as well as ESTs and full-length transcripts. The clustering procedure is enhanced with the employment of genome annotations and unspliced reads. Overall, EasyCluster2 is able to perform an effective detection of splicing isoforms, since it can refine exon-exon junctions and explore alternative splicing without known reference transcripts. Results in GFF3 format can be browsed in the UCSC Genome Browser. Therefore, EasyCluster2 is a powerful tool to generate reliable clusters for gene expression studies, facilitating the analysis also to researchers not skilled in bioinformatics.