Analysis Of Healthcare Data Research Articles

Abstract TP94: Stroke Severity Scales Do Not Predict Adverse Hospital Outcome Variables In Young Stroke Patients: Results From A Regional Stroke Center Of Excellence

Introduction: Acute ischemic stroke (AIS) among young patients is increasing in the US. This study sought to describe the risk factors, clinical characteristics, and hospital outcomes of young AIS patients who were treated in intensive care units (ICU). Methods: The current study was retrospective analysis of electronic healthcare data from a Regional Comprehensive Stroke Center of Excellence. The data was collected from admitted patients between Oct 2016 and Feb 2021 and included AIS patients between 18 and 45 years of age. Correlation and regression analysis were performed in order to understand the relation between risk predictors such as Acute Physiology and Chronic Health Evaluation (APACHE) IV score, Acute Physiology Score (APS), National Institute of Health Stroke Score (NIHSS), and Intensive Care after Thrombolytic (ICAT) and hospital outcomes. Results: There were 217 young AIS patients and 50.2% were males. More than half of the patients were white Hispanic (54%), followed by African American (24%), and whites (13%). Nearly 8% patients received intravenous thrombolysis, 2% endovascular reperfusion therapy, and 6% both treatments. Nearly 16% were smokers, 46% were hypertensive, 31% were dyslipidemic, 18% had previous stoke, 49% were obese or overweight, 18% were diabetic, 2% had carotid stenosis, 6% had CAD, and 1% had dysrhythmia. The mean (standard deviation) of APACHE, APS, NIHSS and ICAT scores were 5.0 (3.8), 33.8 (17.5), 5.5 (8.5), and 1.5 (1.5) respectively. Severity adjusted mean (SD) length of stay in hospital and ICU were 1.2 (0.9) and 1.2 (1.2) days, respectively. Severity adjusted ventilator days were 1.7 (2.1) days. Mean severity (SD) adjusted ICU and in-hospital mortality rates were 0.05 (0.54) and 0.60 (2.8) respectively. Analysis showed no association between risk predictors and adverse hospital outcome variables. Conclusion: Risk predictor scores did not predict adverse hospital outcomes in our AIS patients. Significant number of young AIS patients had chronic conditions which could have increased their stroke risk. Considering that majority of stroke risk factors are modifiable, strategies to decrease these risk factors to lower stroke rates should be prioritized among all these patients, independent of their risk scores.

Review of Innovative Immersive Technologies for Healthcare Applications

ABSTRACTImmersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications' usability, innovation, and future work in the healthcare domain, and digital health data visual analytics.

Multi-core parallel algorithms for hiding high-utility sequential patterns

High-utility sequential pattern mining (HUSPM) can be applied in many applications such as retail, market basket analysis, click-stream analysis, healthcare data analysis, and bioinformatics. HUSPM algorithms discover useful information from data. However, looking at the dark side, the sensitive patterns can also be disclosed by the competitors, who use a HUSPM algorithm on the leaked data. Therefore, high-utility sequential pattern hiding (HUSPH) is used to protect the privacy information from HUSPM algorithms. This paper proposes three algorithms named High Utility Sequential Pattern Hiding Using Pure Array Structure (USHPA), High Utility Sequential Pattern Hiding Using Parallel Strategy (USHP), and High Utility Sequential Pattern Hiding Using Random Distribution Strategy (USHR) for hiding high-utility sequential patterns on quantitative sequence datasets. These algorithms use a proposed data structure named Pattern Utility Set for Hiding (PUSH) to speed up the hiding process. We also introduce a metric called Privacy Factor to evaluate the quality of hiding results. The comparative experiments were conducted on real datasets to evaluate the performance of the proposed algorithms in terms of runtime, memory consumption, scalability, missing cost, and privacy factor. Results show that the proposed algorithms can efficiently sanitize the input datasets, and they outperform the compared algorithms for all metrics.

The Mental Health and Grade Point Average among College Students from Lower Socioeconomic Status Based on Healthcare Data Analysis.

This study evaluated the mediating role of social support in the relationships between mental health and academic achievement and used a sample of 640 college students from lower socioeconomic status (LSES) compared to 501 from higher socioeconomic status (HSES) in China. Self-report measures of depression, anxiety, Internet addiction, self-esteem, perceived social support, and grade point average (GPA) were measured online. Group differences were examined with Chi-square analyses. Results. (1) There were significant differences in mental health, academic achievement, and social support between LSES and HSES. (2) Anxiety, depression, and Internet addiction were significantly negatively correlated with academic achievement; self-esteem and social support were significantly positively correlated with academic achievement. (3) Social support has a mediating role between mental health and academic achievement. These results proved that it is necessary to pay more attention to their mental health and develop social support to improve their academic achievement for LSES students. Previous studies have paid little attention to the LSES students, but these students are more prone to psychological problems. Therefore, this study focuses on the LSES students.

MiCADO-Edge: Towards an Application-level Orchestrator for the Cloud-to-Edge Computing Continuum

Automated deployment and run-time management of microservices-based applications in cloud computing environments is relatively well studied with several mature solutions. However, managing such applications and tasks in the cloud-to-edge continuum is far from trivial, with no robust, production-level solutions currently available. This paper presents our first attempt to extend an application-level cloud orchestration framework called MiCADO to utilise edge and fog worker nodes. The paper illustrates how MiCADO-Edge can automatically deploy complex sets of interconnected microservices in such multi-layered cloud-to-edge environments. Additionally, it shows how monitoring information can be collected from such services and how complex, user- defined run-time management policies can be enforced on application components running at any layer of the architecture. The implemented solution is demonstrated and evaluated using two realistic case studies from the areas of video processing and secure healthcare data analysis.

Integration of Machine Learning and Blockchain Technology in the Healthcare Field: A Literature Review and Implications for Cancer Care

This brief report aimed to describe a narrative review about the application of machine learning (ML) methods and Blockchain technology (BCT) in the healthcare field, and to illustrate the integration of these two technologies in cancer survivorship care. A total of six eligible papers were included in the narrative review. ML and BCT are two data-driven technologies, and there is rapidly growing interest in integrating them for clinical data management and analysis in healthcare. The findings of this report indicate that both technologies can integrate feasibly and effectively. In conclusion, this brief report provided the state-of-art evidence about the integration of the most promising technologies of ML and BCT in health field, and gave an example of how to apply these two most disruptive technologies in cancer survivorship care.

Is urban green space associated with lower mental healthcare expenditure?

IntroductionWhile the evidence of mental health benefits from investing in green space accumulates, claims of reduced healthcare expenditure are rarely supported by evidence from analyses of actual healthcare data. Additionally, the question of ‘who pays?’ has been ignored. We addressed these gaps using person-level data in three Australian cities. Methods55,339 participants with a mean follow-up time of 4.97 years in the Sax Institute's 45 and Up Study (wave 2, collected 2012–2015) were linked to fee-for-service records of antidepressant prescriptions and talking therapy subsidised by the Australian Government (including data on per unit fee, state subsidy, and individual co-payment). Total green space, tree canopy and open grass within 1.6 km road network distances were linked to each participant. Multilevel logistic, negative binomial, and generalised linear models with gamma distribution adjusted for demographic and socioeconomic confounders were used to assess association between each green space variable and prescribing/referral and costs of antidepressants and talking therapy. ResultsPrescription of at least one course of antidepressants occurred for 20.01% (n = 11,071). Referral for at least one session of talking therapy occurred in 8.95% (n = 4954). 13,482 participants (24.4%) had either a prescription or a referral. A 10% increase in green space was associated with higher levels of antidepressant prescribing (e.g. incident rate ratio (IRR) = 1.06, 95%CI = 1.04–1.08). Tree canopy was not associated with antidepressant prescribing or referrals for talking therapy. Open grass was associated with higher odds (OR = 1.17, 95%CI = 1.13–1.20) and counts (IRR = 1.05, 95%CI = 1.02–1.08) of antidepressant prescriptions. Open grass was also associated with lower odds (OR = 0.87, 95%CI = 0.82–0.92) and counts (IRR = 0.93, 95%CI = 0.90–0.96) of talking therapy referrals. Open grass was associated with higher total and mean per-person levels of expenditure on antidepressant prescriptions. ConclusionAlthough green space supports mental health, these unexpected results provide pause for reflection on whether greening strategies will always result in purported reductions in mental healthcare expenditure.

Increasing rate of diagnosed childhood mental illness in the United States: Incidence, prevalence and costs

ObjectiveThis study examined the rate and economic burden of pediatric mental illness from 2012 to 2018. Study designObservational, retrospective analysis of administrative healthcare data. MethodsThis retrospective study of the MarketScan Commercial Research Database included calendar year-based samples (2012-2018) of children aged 4-17 with continuous medical, pharmacy, and mental health/substance abuse coverage for the year of interest and prior year. Incidence and prevalence rates of overall and specific mental illness diagnoses were calculated based on the appearance of diagnosis codes on claims: alcohol/substance abuse, depression, anxiety, eating disorders, bipolar, schizophrenia, developmental disorders, attention deficit/hyperactivity, and conduct disorders. Annual direct medical costs were compared between children with any mental illness and a matched non-mental illness control population. ResultsBetween 2.4 and 4.1 million children qualified for each calendar year sample. From 2012 to 2018, there was a 34.6% increase in the prevalence of mental illness. Attention deficit/hyperactivity, conduct disorders, anxiety, and depression were the most common conditions, while eating disorders, anxiety, and depression presented the greatest increases at 96%, 95%, and 73% respectively. Children with a mental illness incurred significantly greater medical costs compared to matched controls in all years assessed (2018 comparison: $6,055±$27,198 vs. $1,629±$7,274; p < 0.001). ConclusionsChildhood mental illness diagnoses have increased substantially in the United States from 2012 to 2018. In addition to patient impacts, mental health diagnoses also place a notable burden on the healthcare system via increased medical costs. As mental illness is known to be underdiagnosed, the true rate of mental illnesses among children is likely even greater.

Assessment of RHIS Quality Assurance Practices in Tarkwa Submunicipal Health Directorate, Ghana

Background. Routine health information system (RHIS) quality assurance has become an important issue, not only because of its significance in promoting high standard of patient care, but also because of its impact on government budgets for the maintenance of health services. Routine health information system comprises healthcare data collection, compilation, storage, analysis, report generation, and dissemination on routine basis at the various healthcare settings. The data from RHIS give a representation of health status, health services, and health resources. The sources of RHIS data are normally individual health records, records of services delivered, and records of health resources. Using reliable information from routine health information systems is fundamental in the healthcare delivery system. Quality assurance practices are measures that are put in places to ensure the health data that are collected meet required quality standards. Routine health information system quality assurance practices ensure that data that are generated from the system are fit for use. This study considered quality assurance practices in the RHIS processes. Methods. A cross-sectional study was conducted in eight health facilities in Tarkwa Submunicipal health service in the western region of Ghana. The study involved routine quality assurance practices among the 90-health staff and management selected from facilities in Tarkwa Submunicipal who collect or use data routinely from 24th December, 2019, to 20th January, 2020. Results. Generally, Tarkwa Submunicipal health service appears to practice quality assurance during data collection, compilation, storage, analysis, and dissemination. The results show some achievement in quality control performance in report dissemination (77.6%), data analysis (68.0%), data compilation (67.4%), report compilation (66.3%), data storage (66.3%), and collection (61.1%). Conclusions. Even though Tarkwa Submunicipal health directorate engages some control measures to ensure data quality, there is the need to strengthen the process to achieve the targeted percentage of performance (90.0%). There was significant shortfall in quality assurance practices performance especially during data collection, with respect to the expected performance.

Value and Applicability of Large Administrative Healthcare Databases in Prosthetics and Orthotics Outcomes Research.

The goal of health economics and outcomes research is to improve healthcare decision making. In the absence of high-value clinical data, the availability and quality of administrative healthcare data could be vital in the generation of evidence for orthotics and prosthetics services. The purpose of this article is to provide a stronger understanding of administrative healthcare data analysis, an area that has been scarcely examined within prosthetics and orthotics despite the wealth of information available within such data. Examples of common datasets in this arena currently available are provided, as well as an overview of the limitations and advantages of studies utilizing such datasets.

Regional differences in the incidence of asthma exacerbations in Japan: A heat map analysis of healthcare insurance claims data

BackgroundIn Japan, regional differences in asthma mortality have been reported; however, regional differences in asthma exacerbations have not been studied extensively. Therefore, using a health insurance claims database, we investigated the regional differences in the incidence of asthma exacerbations in Japan. MethodsThis study used data from Medi-Scope (Japan Medical Information Research Institute Inc., Japan)—a nationwide health insurance claims database. Patients with asthma at the index date (the latest date of an asthma-related prescription with an asthma diagnosis before October 1, 2018) were included in the analysis. The pre-index period was defined as 1 year before the index date, and the follow-up period as 1 year after the index date. The incidence of asthma exacerbation events was analyzed for each region. ResultsThe primary analysis population comprised 24,883 patients who were continuously prescribed ICS or ICS/LABA at least four times during the pre-index period. The incidence rate of asthma exacerbations with hospitalization was the highest in Chugoku (2.95/100 person-years [95% CI, 1.97–4.43]) and the lowest in Kanto (1.52/100 person-years [95% CI, 1.26–1.83]). The incidence rate of asthma exacerbations for the composite outcome of hospitalization, injectable corticosteroid prescription, and oral corticosteroid burst was the highest in Fukui (105.00/100 person-years [95% CI, 64.53–170.85]) and the lowest in Nagasaki (15.69/100 person-years [95% CI, 10.84–22.72]). ConclusionsRegional differences in the incidence of asthma exacerbations as well as their treatments were observed in Japan.

Alternative Data in Investment Management: Usage, Challenges, and Valuation

<i>Alternative data</i> in finance is an umbrella term for diverse nontraditional datasets used by quantitative and fundamental institutional investors to enhance portfolio returns. Although the use of alternative data is a recent phenomenon, it was not until the last five years that it gained widespread acceptance and the sector started to evolve into a complex ecosystem of data originators, intermediaries, and investors. The alternative data industry faces several obstacles, including difficulty estimating a dataset’s value to investors and technical challenges in leveraging these datasets efficiently at a large scale. In this article, the authors provide an up-to-date description of the alternative data space as it relates to the institutional investment industry. The authors elaborate on what alternative data are and how they are used in investment management. The authors identify and discuss some of the key challenges that arise when working with alternative data. In particular, they address issues such as entity mapping, ticker-tagging, panel stabilization, and debiasing with modern statistical and machine learning approaches. The authors advance several methodologies for the valuation of alternative datasets, including an event study methodology they refer to as the <i>golden triangle</i>, the application of report cards, and the relationship between a dataset’s structure of information content and its potential to enhance investment returns. To illustrate the effectiveness of these methods, they apply them to a case study analysis of real-world healthcare data, delivering an improvement in revenue prediction accuracy from an 88% mean absolute error to a 2.6% mean absolute error. <b>Key Findings</b> ▪ The authors describe the alternative data industry in an investment context. They discuss the commercial incentives for different categories of organizations within the industry and methodologies to valuate alternative datasets. ▪ They identify key technical challenges in working with alternative data, including entity mapping, ticker-tagging, panel stabilization, and debiasing. ▪ Through a case study, they demonstrate how to use alternative data combined with imputation and optimization techniques to predict revenues of publicly traded companies within the healthcare sector.

745P Ovarian Cancer Retrospective European (O’CaRE) observational study to assess burden of disease and time to next treatment in real-world clinical practice: Results from the United Kingdom (UK)

The retrospective O’CaRE study assessed real-world burden of disease, treatment patterns, and outcomes in patients (pts) with ovarian cancer (OC) through analysis of healthcare data across 5 European countries. Interim results are reported herein for the UK cohort.

Impact of agitation in long-term care residents with dementia in the United States.

ObjectivesTo describe characteristics and compare clinical outcomes including falls, fractures, infections, and neuropsychiatric symptoms (NPS) among long‐term care residents with dementia with and without agitation.MethodsA cross‐sectional secondary analysis of administrative healthcare data was conducted whereby residents with dementia residing in a long‐term care facility for ≥12 months were identified from the AnalytiCare LLC database (10/2010–06/2014) and were classified into mutually exclusive cohorts (Agitation Cohort or No‐Agitation Cohort) based on available agitation‐related symptoms. Entropy balancing was used to balance demographic and clinical characteristics between the two cohorts. The impact of agitation on clinical outcomes was compared between balanced cohorts using weighted logistic regression models.ResultsThe study included 6,265 long‐term care residents with dementia among whom, 3,313 were included in the Agitation Cohort and 2,952 in the No‐Agitation Cohort. Prior to balancing, residents in the Agitation Cohort had greater dementia‐related cognitive impairment and clinical manifestations compared to the No‐Agitation Cohort. After balancing, residents with and without agitation, respectively, received a median of five and four distinct types of medications (including antipsychotics). Further, compared to residents without agitation, those with agitation were significantly more likely to have a recorded fall (OR = 1.58), fracture (OR = 1.29), infection (OR = 1.18), and other NPS (OR = 2.11).ConclusionsAgitation in long‐term care residents with dementia was associated with numerically higher medication use and an increased likelihood of experiencing falls, fractures, infections, and additional NPS compared to residents without agitation, highlighting the unmet need for effective management of agitation symptoms in this population.

Evaluating the relationship between clinical and demographic characteristics of insulin-using people with diabetes and their health outcomes: a cluster analysis application

BackgroundThe aim of this study was to determine how clusters or subgroups of insulin-treated people with diabetes, based upon healthcare resource utilization, select social demographic and clinical characteristics, and diabetes management parameters, are related to health outcomes including acute care visits and hospital admissions.MethodsThis was a non-experimental, retrospective cluster analysis. We utilized Aetna administrative claims data to identify insulin-using people with diabetes with service dates from 01 January 2015 to 30 June 2018. The study included adults over the age of 18 years who had a diagnosis of type 1 (T1DM) or type 2 diabetes mellitus (T2DM) on insulin therapy and had Aetna medical and pharmacy coverage for at least 18 months (6 months prior and 12 months after their index date, defined as either their first insulin prescription fill date or their earliest date allowing for 6 months’ prior coverage). We used K-means clustering methods to identify relevant subgroups of people with diabetes based on 13 primary outcome variables.ResultsA total of 100,650 insulin-using people with diabetes were identified in the Aetna administrative claims database and met study criteria, including 11,826 (11.7%) with T1DM and 88,824 (88.3%) with T2DM. Of these 79,053 (78.5%) people were existing insulin users. Seven distinct clusters were identified with different characteristics and potential risks of diabetes complications. Overall, clusters were significantly associated with differences in healthcare utilization (emergency room visits, inpatient admissions, and total inpatient days) after multivariable adjustment.ConclusionsThis analysis of healthcare claims data using clustering methodologies identified meaningful subgroups of patients with diabetes using insulin. The subgroups differed in comorbidity burden, healthcare utilization, and demographic factors which could be used to identify higher risk patients and/or guide the management and treatment of diabetes.

Big Data Analysis and Management in Healthcare

Basically, Big Data means large volumes of data that can be used to solve problems. It has piqued people’s attention over the past two decades because of the enormous potential it holds. Big data is generated, stored, and analyzed by a variety of public and private sector industries in order to enhance the services they provide. Hospital reports, patient medical records, medical test outcomes, and internet of things applications are all examples of big data outlets in the healthcare industry. Biomedical research often produces a large amount of big data that is pertinent to public health. To extract useful information from this data, it must be properly managed and analyzed. Otherwise, finding solutions by analyzing big data quickly becomes impossible. The ability to identify trends and transform large amounts of data into actionable information for precision , medicine and decision makers is at the heart of Big Data’s potential in healthcare. In a variety of areas, the use of Big Data in healthcare is now offering solutions for optimizing patient care and creating value in healthcare organizations. In this paper, some big data solutions are provided for healthcare. Big Data Analytics strategies to mitigate covid-19 health disparities are provided. Finally we analyse some of the challenges with big data in healthcare.

A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study

Background: According to the renowned and Oscar award-winning American actor and film director Marlon Brando, “privacy is not something that I am merely entitled to, it is an absolute prerequisite.” Privacy threats and data breaches occur daily, and countries are mitigating the consequences caused by privacy and data breaches. The Indian healthcare industry is one of the largest and rapidly developing industry. Overall, healthcare management is changing from disease-centric into patient-centric systems. Healthcare data analysis also plays a crucial role in healthcare management, and the privacy of patient records must receive equal attention. Purpose: This paper mainly presents the utility and privacy factors of the Indian healthcare data and discusses the utility aspect and privacy problems concerning Indian healthcare systems. It defines policies that reform Indian healthcare systems. The case study of the NITI Aayog report is presented to explain how reformation occurs in Indian healthcare systems. Findings: It is found that there have been numerous research studies conducted on Indian healthcare data across all dimensions; however, privacy problems in healthcare, specifically in India, are caused by prevalent complacency, culture, politics, budget limitations, large population, and existing infrastructures. This paper reviews the Indian healthcare system and the applications that drive it. Additionally, the paper also maps that how privacy issues are happening in every healthcare sector in India. Originality/Value: To understand these factors and gain insights, understanding Indian healthcare systems first is crucial. To the best of our knowledge, we found no recent papers that thoroughly reviewed the Indian healthcare system and its privacy issues. The paper is original in terms of its overview of the healthcare system and privacy issues. Social Implications: Privacy has been the most ignored part of the Indian healthcare system. With India being a country with a population of 130 billion, much healthcare data are generated every day. The chances of data breaches and other privacy violations on such sensitive data cannot be avoided as they cause severe concerns for individuals. This paper segregates the healthcare system’s advances and lists the privacy that needs to be addressed first.

Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson's Disease.

Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease-modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson’s disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N = 88,867) and IBM MarketScan Research Databases (N = 106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, beneficial against common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

Handling missing and outliers values by enhanced algorithms for an accurate diabetic classification system

Recently, healthcare data analysis has become an attractive research topic. Data gathering is the first step in data analysis and processing. During the collection of the data, some errors may occur due to human mistakes, devices’ errors, or the transmission process noise. The correct treatment of the missed data and outliers conserve the data size and improve the model’s performance. This paper provides two enhanced algorithms to handle missing values and outliers in big datasets. The main idea is dividing the dataset into its different classes, or clustering it by using k-means++, then calculate the average value of each part, finally replace the missed data and outliers with its corresponding part mean value. The projected imputation and outliers’ data handling algorithms are tested on a dataset called Pima Indian diabetic, which contains 2768 patients dividing into 952 diabetic and 1816 controls. Four classifiers (Random Forest, Decision Tree, Support Vector Machine, and Naive Bayes) are used to evaluate the effect of the proposed algorithms. The results show that the proposed algorithms improve classification accuracy by 8% and decrease the RMSE by 17% over Deep Learning (DL). DL is the most powerful algorithms used in repairing the missed data.

Thrombocytopenia in chronic liver disease: the analysis of Italian administrative healthcare data by Fondazione RES

Thrombocytopenia in chronic liver disease: the analysis of Italian administrative healthcare data by Fondazione RES