Health Analytics Research Articles

Advanced intelligent health advice with informative summaries to facilitate treatment decision-making

PurposeWhether automatically generated summaries of health social media can assist users in appropriately managing their diseases and ensuring better communication with health professionals becomes an important issue. This paper aims to develop a novel deep learning-based summarization approach for obtaining the most informative summaries from online patient reviews accurately and effectively.Design/methodology/approachThis paper proposes a framework to generate summaries that integrates a domain-specific pre-trained embedding model and a deep neural extractive summary approach by considering content features, text sentiment, review influence and readability features. Representative health-related summaries were identified, and user judgements were analysed.FindingsExperimental results on the three real-world health forum data sets indicate that awarding sentences without incorporating all the adopted features leads to declining summarization performance. The proposed summarizer significantly outperformed the comparison baseline. User judgement through the questionnaire provides realistic and concrete evidence of crucial features that remarkably influence patient forum review summaries.Originality/valueThis study contributes to health analytics and management literature by exploring users’ expressions and opinions through the health deep learning summarization model. The research also developed an innovative mindset to design summarization weighting methods from user-created content on health topics.

COVID-19 Contact Tracing Experience at a Tertiary Healthcare Center in Saudi Arabia.

Introduction As of May 2023, the end of the pandemic, the cumulative number of COVID-19 cases reached more than 841,000 cases. Healthcare workers (HCWs) especially have been at the frontline during this pandemic and are at a higher chance of contracting COVID-19. Approximately half of all high-risk exposures were to healthcare personnel with COVID-19. While several tools for contact tracing were developed for the general public, contact tracing for infectious diseases in the healthcare setting is limited, and global testing of HCWs, or in-hospital digital tracing, is not performed in most facilities. The King Faisal Specialist Hospital and Research Center (KFSH&RC) collaborated with the Infection Control and Health Information Technology Affairs (HITA)to create an automated COVID-19 contact tracing tool specifically for HCWs who worked at the institute. This study aims to describe the contact tracing experience at KFSH&RC. Methods A retrospective study was conducted to describe the use of an automated tool that was developed to assist in the contact tracing process and that was to be used by KFSH&RC employees who had been in contact with a COVID-19-positiveindividual. This tool is utilized for the early identification of possible COVID-19 cases and risk stratification of the exposed individuals. The tool can be accessed through the KFSH&RC website; it also collects information about the COVID-19 exposure rate among the different departments such as administration, capital projects/facilities, and healthcare at a tertiary care hospital in Riyadh, Saudi Arabia Results The tool has been utilized 7,353 times by contact cases. Approximately 7% of those tested later developed a COVID-19 infection. When assessing the positivity rates per department, The Environmental Services Department had the highest positivity rate of 28.21%, followed by Health Information Technology and Analytics (HITA), and then the Central Transportation Department. Conclusion This study acts as the first of its kind to describe the successful use of the healthcare contact tracing system in one of Saudi Arabia's largest hospitals (KFSH&RC) and describe the infection trends in different departments of the hospital. Through the tracing system, the departments with the highest COVID-19 infection occurrences at the hospital were identified in a timely manner, and safety protocols were implemented.

Financial burden of men with localized prostate cancer: a process paper.

Many individuals undergoing cancer treatment experience substantial financial hardship, often referred to as financial toxicity (FT). Those undergoing prostate cancer treatment may experience FT and its impact can exacerbate disparate health outcomes. Localized prostate cancer treatment options include: radiation, surgery, and/or active surveillance. Quality of life tradeoffs and costs differ between treatment options. In this project, our aim was to quantify direct healthcare costs to support patients and clinicians as they discuss prostate cancer treatment options. We provide the transparent steps to estimate healthcare costs associated with treatment for localized prostate cancer among the privately insured population using a large claims dataset. To quantify the costs associated with their prostate cancer treatment, we used data from the Truven Health Analytics MarketScan Commercial Claims and Encounters, including MarketScan Medicaid, and peer reviewed literature. Strategies to estimate costs included: (1) identifying the problem, (2) engaging a multidisciplinary team, (3) reviewing the literature and identifying the database, (4) identifying outcomes, (5) defining the cohort, and (6) designing the analytic plan. The costs consist of patient, clinician, and system/facility costs, at 1-year, 3-years, and 5-years following diagnosis. We outline our specific strategies to estimate costs, including: defining complex research questions, defining the study population, defining initial prostate cancer treatment, linking facility and provider level related costs, and developing a shared understanding of definitions on our research team. Analyses are underway. We plan to include these costs in a prostate cancer patient decision aid alongside other clinical tradeoffs.

Pac-12 Health Analytics Program: An Innovative Approach to Health Care Operations, Data Analytics, and Clinical Research in Intercollegiate Athletics.

The objective of this study was to describe the purpose, methods, and effects of the Pac-12 Health Analytics Program (HAP) approach on sports medicine informatics, research, analytics, and health care operations. Sports injury-surveillance initiatives have been supporting the clinical research community in sports medicine for nearly 4 decades. Whereas the initial systems tracked only a few sports, current surveillance programs have expanded to include entire professional and elite athlete organizations, providing important statistics on sports injury risk management. The HAP is a conference-wide data-sharing and-analytics program. It collects authorized, deidentified clinical data, encompassing multiple domains of sports medicine injury management, including sports injuries and illnesses, concussions, risk exposure, and COVID-19 testing elements. The HAP provides clinicians with access to curated data to inform evidence-based practice and support local health care operations with respect to emerging sports injury trends. The HAP supplies approved research groups with access to a data repository that describes a homogeneous, elite intercollegiate athlete sample, thereby supporting nonresearch clinical initiatives as well as contributions to peer-reviewed research that can improve the health and well-being of Pac-12 student-athletes. The HAP is a novel approach to sports injury epidemiology and surveillance that has allowed the Pac-12 Conference to meet larger objectives regarding improving the student-athlete experience and clinical research among its member schools. Data quality control has improved the accuracy of the data and value to clinical athletic trainers within the conference. Curated dashboards displaying aggregated project data offer clinicians data-driven decision-making tools that help inform sports injury risk management. As of 2021, the HAP had supported more than 3 dozen data requests. These investigations have resulted in numerous peer-reviewed research contributions to the sports medicine community with findings that have great potential to improve the health and well-being of Pac-12 student-athletes.

Preventing Heart Attack and Stroke Events Through Surveillance (PHASES): Evaluation of a Health Analytics System for Primary Care

Preventing Heart Attack and Stroke Events Through Surveillance (PHASES): Evaluation of a Health Analytics System for Primary Care

Computer vision and IoT research landscape for health and safety management on construction sites

AimsPerform a systematic review of current literature to evaluate and summarise the health and safety hazards on construction sites. MethodsScience Direct, SCOPUS and web of science databases were searched for research articles published from 2013 to 2021. From an initial search of 350 research articles, we removed the duplicate articles and carried out an analysis of the abstract and full text that focused on health, safety, hazards, behaviour, on-site health and safety and the digital technologies leaving a total of 66 studies included. ResultsComputer vision and Internet of Things (IoT) are the dominant technologies for health and safety management. A comparison of the two technologies reveals that computer vision is dominant because of its non-intrusive approach to data collection; thus, supporting the scalability of computer vision approach at the expense of cost and development time. It will help to prevent on-site health and safety hazards and injuries on construction site. ConclusionComputer vision offers non-intrusive benefits over Internet of Things (IoT); being able to detect the health and safety hazards. Computer vision has proved to be beneficial for better accuracy prediction, real time data monitoring, and model development for onsite health and safety analytics on the construction site.

Scaling innovative solutions during times of transformation

Atrium Health is a nationally recognised leader in shaping health outcomes through innovative research, education and compassionate patient care. As the health system experiences rapid growth with a larger geographical footprint owing to multiple strategic combinations, it has identified and designed an organisational structure that strategically drives systemic change and creates buy-in for integrated, large-scale value-based initiatives. The population health division effectively applies population health data and analytics to formulate a strategy to address health-care disparities, identify community needs and provide access to high-quality care.

Real-world treatment patterns of non-small cell lung cancer in an Indian hospital.

e18604 Background: The circumstances regarding lung cancer in India are similar to many countries around the world – it is one of the most commonly diagnosed cancers with many treatment options available. India, however, relies heavily on an out-of-pocket healthcare payment model in which patients are often directly responsible for the majority of these treatment costs. This paradigm makes many of the newer generation of medications financially inaccessible for most Indians. In this research, we aimed to understand the current treatment patterns of non-small cell lung cancer (NSCLC) from an analysis of Indian hospital data, as a way to contextualize the real-world treatment situation. Methods: Electronic Medical Record (EMR) data was sourced from a major cancer hospital located in India’s national capital region. Using the Querent health analytics platform, EMR data across 5 years (2017-2022) were analyzed. Demographics, disease characteristics, and treatment patterns were identified through a combination of structured and unstructured data including data from registration, laboratory, radiology, diagnosis, procedure, pharmacy, and clinical notes. Results: 1,227 unique patients with NSCLC were observed in the hospital over five years, with an average (SD) duration of observation time of 12.1 (15.4) months. The patients were, on average (SD), 59.0 (11.2) years old and were more likely to be male (69.4%). Most patients were in later cancer stages, with a distribution of I (3%), II (2%), III (13%), and IV (82%). Among those with metastatic NSCLC, 773 (76%) received cancer treatment at the hospital, with 52% receiving surgery, 5% radiotherapy, and 88% anti-cancer medication. The most commonly administered medications were chemotherapies (carboplatin (52%) and pemetrexed (38%)). Targeted therapies were administered in 55% of patients. Conclusions: Only about half of all metastatic NSCLC patients received targeted cancer therapy. This proportion pales in comparison to targeted therapy utilization rates observed in the US and Europe, indicating the need to address the affordability issues that hamper patient access to these medications. Increased adoption of advanced therapeutics could lead to improved outcomes for many patients in India.

Participation in arts and culture among individuals with serious mental illnesses and its relationship to quality of life and recovery.

Engagement in arts and cultural activities is valued and yields positive outcomes but may be understudied in populations with serious mental illness (SMI). Our aims are to evaluate the extent to which individuals with SMI deem participation areas related to arts and culture (e.g., going to a theater) to be important, and if important, how often they are being done and if it is enough in comparison to a nonSMI sample. We conducted analyses using a sample of 1,120 individuals with SMI from nine research studies between 2008 and 2016 and a sample of 300 individuals without SMI that were part of the Truven Health Analytics PULSE survey. All participants completed a survey containing questions related community participation. Analyses were conducted using independent samples t tests, followed by analysis of variances, and chi-square tests. The results indicate that adults with SMI are as, or more interested in arts and cultural activities as adults in the general population, but do not participate in those areas as much as they would like in comparison. We also found that, as with the general population, participation in these areas is positively associated with quality of life and to a lesser degree, recovery. Engagement in arts and culture activities may be an overlooked compared to other areas of participation, such as employment. Psychiatric rehabilitation practitioners may need to pay greater attention to areas related to art appreciation, rather than just art production. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Safeguarding Connected Health: Leveraging Trustworthy AI Techniques to Harden Intrusion Detection Systems Against Data Poisoning Threats in IoMT Environments

Internet of Medical Things (IoMT) environments introduce vast security exposures including vulnerabilities to data poisoning threats that undermine integrity of automated patient health analytics like diagnosis models. This research explores applying trustworthy artificial intelligence (AI) methodologies including explainability, bias mitigation, and adversarial sample detection to substantially enhance resilience of medical intrusion detection systems. We architect an integrated anomaly detector featuring purpose-built modules for model interpretability, bias quantification, and advanced malicious input recognition alongside conventional classifier pipelines. Additional infrastructure provides full-lifecycle accountability via independent auditing. Our experimental intrusion detection system design embodying multiple trustworthy AI principles is rigorously evaluated against staged electronic record poisoning attacks emulating realistic threats to healthcare IoMT ecosystems spanning wearables, edge devices, and hospital information systems. Results demonstrate significantly strengthened threat response capabilities versus baseline detectors lacking safeguards. Explainability mechanisms build justified trust in model behaviors by surfacing rationale for each prediction to human operators. Continuous bias tracking enables preemptively identifying and mitigating unfair performance gaps before they widen into operational exposures over time. SafeML classifiers reliably detect even camouflaged data manipulation attempts with 97% accuracy. Together the integrated modules restore classification performance to baseline levels even when overwhelmed with 30% contaminated data across all samples. Findings strongly motivate prioritizing adoption of ethical ML practices to fulfill duty of care around patient safety and data integrity as algorithmic capabilities advance.

0925 Population Health and Data Analytics as an Opportunity for Improving Care Quality in Sleep Medicine

Abstract Introduction The volume and diversity of sleep-related and quality of life complaints in sleep medicine has increased through improved availability of data from wearables and public education on sleep health. Future shifts away from quantity and towards quality-based compensation by insurance payors will require practices to understand and actively monitor population health and outcomes data and implement data-driven practices to improve diagnostic efficiency. Currently, prevalence and population health data benchmarks are incomplete or absent in many aspects of sleep disorders treatment. The integrated care model of sleep medicine, with board-certified primary sleep physicians, an in-house Positive Airway Pressure (PAP) Therapy clinic and certified sleep medicine Dentist, is uniquely positioned to improve care by characterizing the impact of clinical decision-making on care cost and quality. Methods Population health data was gathered in the course of patient care in the integrated care model. Baseline/Diagnostic Home Sleep Apnea Test (HSAT) results from 2018-2021 were included. Diagnostic severity was categorized based on AASM treatment guidelines. Matched therapy visits were included from PAP Therapy Initiation or Compliance visit and treatment outcomes, as well as non-PAP treatments for sleep-disordered breathing, Oral Appliance (OA) and Inspire Therapy. Results 20% of 8531 Diagnostic HSAT results were non-diagnostic for sleep apnea, 35% were Mild, 21% Moderate and 24% Severe. Of non-diagnostic tests, 81% had no sleep apnea treatment associated. Of Mild diagnostic tests, 49% had associated PAP initiation or compliance, 9% had associated OA, and 42% had no treatment associated. The relationship between diagnostic severity and associated treatment was significant (Χ2 < 0.0001). Further, Moderate and Severe resulting diagnostic tests had no associated treatment in 24% and 28% of cases, respectively. Conclusion Sleep medicine patients are commonly symptomatic and often have major quality of life complaints. Yet as these data demonstrate, there is a substantial proportion of patients who do not have an associated treatment after a positive diagnostic home sleep apnea test. Future studies should include additional demographic and follow up data to better understand care pathways both for patients who receive a positive diagnostic test but pursue alternative treatment methods, as well as more complicated non-diagnostic sleep disorders patients. Support (if any)

Impact of the COVID-19 Pandemic Hiatus From Sports Activities on Injuries Observed Among Division I NCAA Athletes.

The coronavirus disease 2019 (COVID-19) pandemic significantly disrupted athletic activities, including those in the Pacific 12 (Pac-12) Conference of the National Collegiate Athletic Association. It is currently unknown how the disruption in training and competition impacted athletes' risk of injury upon resumption of activities. To describe and compare the rate, timing, mechanism, and severity of injuries among collegiate athletes across multiple sports in the Pac-12 Conference before and after the COVID-19 pandemic-associated hiatus of intercollegiate athletic activities. Descriptive epidemiology study. Descriptive and injury data among intercollegiate athletes from both the season before the hiatus and the season after the hiatus were acquired from the Pac-12 Health Analytics Program database. Injury elements (timing of injury onset, injury severity, mechanism, recurrence, outcome, need for procedural intervention, and event segment during which the injury took place) were compared by time using the chi-square test and a multivariate logistic regression model. Subgroup analyses were performed on knee and shoulder injuries among athletes participating in sports with traditionally high rates of knee and shoulder injuries. A total of 12,319 sports-related injuries across 23 sports were identified, with 7869 pre-hiatus injuries and 4450 post-hiatus injuries. There was no difference in the overall incidence of injury between the pre-hiatus and post-hiatus seasons. However, the proportion of noncontact injuries was higher in the post-hiatus season for football, baseball, and softball players, and the proportion of nonacute injuries in the post-hiatus season was higher among football, basketball, and rowing athletes. Finally, the overall proportion of injuries sustained by football players in the final 25% of competition or practice was higher in the post-hiatus season. Athletes competing in the post-hiatus season were observed to have higher rates of noncontact injuries and injuries sustained in the final 25% of competition. This study demonstrates that the COVID-19 pandemic has had varied effects on athletes from different sports, suggesting that many factors must be considered when designing return-to-sports programs for athletes after an extended absence from organized training.

Elevated Healthcare Resources Utilization and Costs among Individuals Diagnosed with Rett syndrome (P13-9.004)

<h3>Objective:</h3> Compare the HCRU and medical costs of children with and without Rett syndrome. <h3>Background:</h3> No studies have examined the healthcare resource utilization (HCRU) and costs of individuals diagnosed with Rett syndrome using a commercially available dataset. <h3>Design/Methods:</h3> A retrospective cohort study examined individuals diagnosed with Rett compared to a control sample on HCRU and costs during the 12-months following index. Study data (2017–2022) were integrated medical claims from Clarivate. Index was defined as the initial Rett Dx, or medical claim on record during the case-finding period among individuals in the Rett and control cohorts, respectively. Outcomes were HCRU and cost (per patient per year, PPPY). <h3>Results:</h3> Eligibility criteria identified 1,046 individuals diagnosed with Rett and 2,157 in the control cohort. All-cause healthcare visits totaled 24.7 vs. 8.3 PPPY (p&lt;0.05) among the Rett and control cohorts, respectively. The Rett cohort averaged 3-fold more PCP visits (15.0 vs. 4.9, p&lt;0.05). Similar differences were observed for non-PCP outpatient visits (Rett: 6.3 vs. control: 2.2, p&lt;0.05). Inpatient admissions were 7.3-fold higher among the Rett compared with the control cohort (2.2 vs. 0.3, p&lt;0.05). ED visits were also higher (1.2 vs. 0.9, p&lt;0.05). Other medical services used, which included medical equipment, labs etc., were 20-fold higher among the Rett cohort (42.1 vs. 2.1, p&lt;0.05). Healthcare utilization patterns translated into higher medical costs for the Rett cohort vs. controls. Total medical costs were 20-fold higher among the Rett cohort PPPY ($25,926.30 vs. $1,611.00, p&lt;0.05). Cost differences between cohorts were driven by other medical services used ($19,384.20 vs $437.80, p&lt;0.05) although all cost categories were higher and statistically different for the Rett and control cohort. <h3>Conclusions:</h3> HCRU and costs among individuals diagnosed with Rett are substantially higher relative to other children in the US and may result in considerable financial burden for parents and caregivers. <b>Disclosure:</b> Dr. Davis has received personal compensation for serving as an employee of Health Analytics,LLC. The institution of Dr. Davis has received research support from Acadia Pharmaceuticals . Dr. Parab has received personal compensation for serving as an employee of Health Analytics, LLC.. The institution of Dr. Parab has received research support from Acadia. Dr. May has received personal compensation for serving as an employee of Acadia Pharmaceuticals. Dr. May has stock in ACADIA Pharmaceuticals. Dr. May has stock in Medtronic. Dr. May has stock in Johnson &amp; Johnson. Dr. May has stock in Pfizer Pharmaceuticals. The institution of Dr. Ruetsch has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Otsuka Amrerica. The institution of Dr. Ruetsch has received research support from Otsuka America. The institution of Dr. Ruetsch has received research support from Acadia. The institution of Dr. Ruetsch has received research support from Atentiv. The institution of Dr. Ruetsch has received research support from GSK. The institution of Dr. Ruetsch has received research support from Medtronic. The institution of Dr. Ruetsch has received research support from Novartis. The institution of Dr. Ruetsch has received research support from Orexo.

Physician Approaches to the Initial Management of an Intraluminal Thrombus in Recently Symptomatic Carotid Artery Stenosis: Results from the Hot Carotid Study (S20.005)

Physician Approaches to the Initial Management of an Intraluminal Thrombus in Recently Symptomatic Carotid Artery Stenosis: Results from the Hot Carotid Study (S20.005)

Breaking the 80:20 rule in health research using large administrative data sets.

Objective: To explore the application of online analytic processing (OLAP) to improve the efficiency of analytics using large administrative health data sets. Methods: 18 years of administrative health data (1994/95 to 2012/13) were obtained from the Alberta Ministry of Health in Canada. The data sets included hospitalization, ambulatory care and practitioner claims data. Reference files were obtained that provided information including patient demographics, resident postal code, facility, and provider details. Population counts and projections for each year, sex, age were included for rate calculations. These sources were used to develop a data cube using OLAP tools. Results: Time required for analyses was reduced to 5% of that required when comparing run-time for simple queries that did not require linkage of data sets. The data cube negated the need for many intermediary steps for data extraction and analyses for research activities. Conventional methods required over 250GB of server space for multiple analytic subsets, compared to only 10.3GB for the data cube. Conclusions: Cross-training in information technology and health analytics is recommended to provide capacity to better leverage OLAP tools which are available with many common applications.

Artificial Intelligence in Crop Management: Predictive Analytics for Soil Health and Weather Patterns

Artificial Intelligence in Crop Management: Predictive Analytics for Soil Health and Weather Patterns

A Platform for Data-Centric, Continuous Epidemiological Analyses (EpiGraphHub): Descriptive Analysis.

Guaranteeing durability, provenance, accessibility, and trust in open data sets can be challenging for researchers and organizations that rely on public repositories of data critical for epidemiology and other health analytics. The required data repositories are often difficult to locate and may require conversion to a standard data format. Data-hosting websites may also change or become unavailable without warning. A single change to the rules in one repository can hinder updating a public dashboard reliant on data pulled from external sources. These concerns are particularly challenging at the international level, because policies on systems aimed at harmonizing health and related data are typically dictated by national governments to serve their individual needs. In this paper, we introduce a comprehensive public health data platform, EpiGraphHub, that aims to provide a single interoperable repository for open health and related data. The platform, curated by the international research community, allows secure local integration of sensitive data while facilitating the development of data-driven applications and reports for decision-makers. Its main components include centrally managed databases with fine-grained access control to data, fully automated and documented data collection and transformation, and a powerful web-based data exploration and visualization tool. EpiGraphHub is already being used for hosting a growing collection of open data sets and for automating epidemiological analyses based on them. The project has also released an open-source software library with the analytical methods used in the platform. The platform is fully open source and open to external users. It is in active development with the goal of maximizing its value for large-scale public health studies.

A secure and privacy-preserving word vector training scheme based on functional encryption with inner-product predicates

With the extensive applications of machine learning, it has been witnessed that machine learning has been applied in various fields such as e-commerce, mobile data processing, health analytics and behavioral analytics etc. Word vector training is usually deployed in machine learning to provide a model architecture and optimization, for example, to learn word embeddings from a large amount of datasets. Training word vector in machine learning needs a lot of datasets to train and then outputs a model, however, some of which might contain private and sensitive information, and the training phase will lead to the exposure of the trained model and user datasets. In order to offer utilizable, plausible, and personalized alternatives to users, this process usually also entails a breach of their privacy. For instance, the user data might contain of face,irirs and personal identities etc. This will release serious problem in the machine learning. In this article, we investigate the problem of training high-quality word vectors on encrypted datasets by using privacy-preserving learning algorithms. Firstly, we use a pseudo-random function to generate a statistical token for each word to help build the vocabulary of the word vector. Then we employ functional inner-product encryption to calculate the activation function to obtain the inner product, securely. Finally, we use BGN cryptosystem to encrypt and hide the sensitive datasets, and complete the homomorphic operation over the ciphertexts to perform the training procedure. In order to implement the privacy preservation of word vector training, we propose four privacy-preserving machine learning schemes to provide the privacy protection in our scheme. We analyze the security and efficiency of our protocols and give the numerical experiments. Compared with the existing solutions, it indicates that our scheme can provide a higher efficiency and less communication overhead.

A Virtual Reality Exergame: Clinician-Guided Breathing and Relaxation for Children with Muscular Dystrophy.

This study introduces a VR-based breathing and relaxation exergame tailored for individuals with Duchenne muscular dystrophy (DMD). DMD is a rare neuromuscular disease that leads to respiratory muscle dysfunction with anxiety being a common comorbidity. Clinical management requires frequent visits to rare disease specialists to manage symptom progression. Limited availability and/or proximity of rare disease experts present challenges to care and can lead to missed care opportunities and reduced quality of life. We propose a breathing and relaxation exergame with remote telehealth applicability that incorporates shared patient-clinician VR interaction, and physiological sensors that provide both real-time feedback to the patient and health analytics for the clinician. The game focuses on two key aspects of DMD clinical care that can be mediated through control of breathing: relaxation/mindfulness training and respiratory muscle exercise. The system was evaluated among 13 individuals, including 4 participants with DMD. Feedback surveys, interviews, and focus group discussions with participants, accompanying family members, and clinicians demonstrated the feasibility of this VR tool for telehealth or as part of a home exercise program.

Evaluation of IoT-Enabled hybrid model for genome sequence analysis of patients in healthcare 4.0

Genome sequence matching is vital for health analytics and treatment in healthcare 4.0. It focuses on finding whether a given sequence resembles other sequences that can help identify disease outbreaks faster. The healthcare 4.0 primarily require more efficient systems that can easily connect and interact with big data. The Internet of things (IoT) can potentially realize smart devices that can perform this role in the health sector. This research presents an IoT-enabled hybrid model for Genome sequence analysis of patients in healthcare 4.0. The proposed model utilizes an improved pattern-matching technique that uses Hadoop's ideas, OpenCL, and APAR (API). The primary local alignment search tool (BLAST) is the core algorithm extensively used to compare and find preliminary biological sequence information and calculate the matches' statistical importance. BLAST enables many strings to be distributed in the form of blocks; after that, an instance of the Mapper is mapped to process it after the output of all the mappers combined through the reducer. This mapper and reducer process has inter-node parallelism to speed up the process and efficiently utilize the shared resources. In this work, fine-grained parallelism has been introduced in Hadoop-based BLAST. The proposed work also provided inter-node and intra-node parallelism. The Mapper and reducer accelerated the conventional BLAST algorithm. The proposed model achieved better results than other traditional algorithms for different datasets in all cases.