Data Analytics In Healthcare Research Articles

Big Data Analytics in Healthcare: Exploring the Role of Machine Learning in Predicting Patient Outcomes and Improving Healthcare Delivery

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research

Big Data Analytics in Healthcare: An Analytical Survey

Big Data Analytics in Healthcare: An Analytical Survey

Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit.

The ongoing fast-paced technology trend has brought forth ceaseless transformation. In this regard, cloud computing has long proven to be the paramount deliverer of services such as computing power, software, networking, storage, and databases on a pay-per-use basis. The cloud is a big proponent of the internet of things (IoT), furnishing the computation and storage requisite to address internet-of-things applications. With the proliferating IoT devices triggering a continual data upsurge, the cloud-IoT interaction encounters latency, bandwidth, and connectivity restraints. The inclusion of the decentralized and distributed fog computing layer amidst the cloud and IoT layer extends the cloud's processing, storage, and networking services close to end users. This hierarchical edge-fog-cloud model distributes computation and intelligence, yielding optimal solutions while tackling constraints like massive data volume, latency, delay, and security vulnerability. The healthcare domain, warranting time-critical functionalities, can reap benefits from the cloud-fog-IoT interplay. This research paper propounded a fog-assisted smart healthcare system to diagnose heart or cardiovascular disease. It combined a fuzzy inference system (FIS) with the recurrent neural network model's variant of the gated recurrent unit (GRU) for pre-processing and predictive analytics tasks. The proposed system showcases substantially improved performance results, with classification accuracy at 99.125%. With major processing of healthcare data analytics happening at the fog layer, it is observed that the proposed work reveals optimized results concerning delays in terms of latency, response time, and jitter, compared to the cloud. Deep learning models are adept at handling sophisticated tasks, particularly predictive analytics. Time-critical healthcare applications reap benefits from deep learning's exclusive potential to furnish near-perfect results, coupled with the merits of the decentralized fog model, as revealed by the experimental results.

Australian surgical revision rate for benign prostatic obstruction.

To evaluate the rate of revision surgery following commonly performed procedures for benign prostatic hyperplasia (BPH) is hyperplasia of both glandular and stromal components of prostate especially in periurethral transitional gland, using real-world data from Medicare Australia. Prospection is a Healthcare Data Analytics firm that has negotiated access with the Medicare Benefits Schedule (MBS) to provide longitudinal data on the use of specific procedural item codes. We identified patients over the age of 40 years who had undergone primary transurethral resection of the prostate (TURP), transurethral incision of the prostate (TUIP) or photoselective vaporization of the prostate (PVP) between 2005 and 2010 using MBS item numbers 37203, 37207 and 36854, respectively. Using longitudinal MBS data, primary outcomes included need for revision surgery at 5-years follow-up (2015). The release of these data was approved by Medicare Australia upon application. Data analysis was conducted using chi-squared tests and statistical significance was defined at P < 0.05. The distribution of primary surgical procedures performed between 2005 and 2010 was: TURP 5579 (90%), TUIP 345 (6%) and PVP 258 (4%). TURP was also the most prevalent procedure for treatment of lower urinary tract symptoms in men with BPH requiring revision surgery (75%). At 5-year follow-up the rate of revision surgery for TURP (573/5579), TUIP (47/345) and PVP (30/258) was 10.3%, 13.6% and 11.6%, respectively. The difference was not statistically significant (P= 0.12). There was no significant change (P = 0.59) observed over the years in number of men requiring revision surgery. This study indicates that TURP and PVP have a similar durability after 5 years of follow-up.

Enabling Real-World Data to Accelerate the Development of Innovative Cancer Biomarkers.

The molecular diagnostics industry has historically relied on sanitized clinical trials and commoditized data sources to inform its biomarker discovery and validation process-an under-substantiated approach that was ultra-expensive, resource-consuming and did not reflect how representative a new biomarker would be in broader patient populations. In an effort to gain more accurate insight into the patient experience and bring innovative biomarkers to market more efficiently and accurately, the industry is now expanding into extended real-world data. To access the needed breadth and depth of patient-centric data, diagnostic companies must collaborate with a healthcare data analytics partner that has three key assets: (i) a broad and deep megadata with metadata, (ii) a data-rich provider network, and (iii) an outcomes-improvement engine to support the next generation of molecular diagnostics (Dx) and therapeutics (Rx) development.

Improving the Trustworthiness of Interactive Visualization Tools for Healthcare Data through a Medical Fuzzy Expert System.

Successful healthcare companies and illness diagnostics require data visualization. Healthcare and medical data analysis are needed to use compound information. Professionals often gather, evaluate, and monitor medical data to gauge risk, performance capability, tiredness, and adaptation to a medical diagnosis. Medical diagnosis data come from EMRs, software systems, hospital administration systems, laboratories, IoT devices, and billing and coding software. Interactive diagnosis data visualization tools enable healthcare professionals to identify trends and interpret data analytics results. Selecting the most trustworthy interactive visualization tool or application is crucial for the reliability of medical diagnosis data. Thus, this study examined the trustworthiness of interactive visualization tools for healthcare data analytics and medical diagnosis. The present study uses a scientific approach for evaluating the trustworthiness of interactive visualization tools for healthcare and medical diagnosis data and provides a novel idea and path for future healthcare experts. Our goal in this research was to make an idealness assessment of the trustworthiness impact of interactive visualization models under fuzzy conditions by using a medical fuzzy expert system based on an analytical network process and technique for ordering preference by similarity to ideal solutions. To eliminate the ambiguities that arose due to the multiple opinions of these experts and to externalize and organize information about the selection context of the interactive visualization models, the study used the proposed hybrid decision model. According to the results achieved through trustworthiness assessments of different visualization tools, BoldBI was found to be the most prioritized and trustworthy visualization tool among other alternatives. The suggested study would aid healthcare and medical professionals in interactive data visualization in identifying, selecting, prioritizing, and evaluating useful and trustworthy visualization-related characteristics, thereby leading to more accurate medical diagnosis profiles.

Big Data Analytics in Healthcare: Opportunities and Challenges

The use of big data analytics in healthcare is growing rapidly as a result of the increasing availability of large, complex, and diverse data sets. Big data analytics can be used to improve patient outcomes, reduce costs, and enhance clinical decision-making. However, there are also significant challenges associated with the use of big data analytics in healthcare. This paper provides an overview of the opportunities and challenges of big data analytics in healthcare, with a focus on the potential benefits of using big data analytics in healthcare, the challenges of implementing big data analytics in healthcare, and the ethical considerations that must be taken into account.

Optimised Cluster-based Approach for Healthcare Data Analytics

Data analytics is an intriguing study due to the fact that an enormous volume of healthcare data is being generated by different smart IOT-based health tracking devices, and the Artificial Intelligent-based applications. Data analytic tools and unsupervised techniques combinedly make it possible to find and comprehend hidden patterns in a dataset that may not be visible through simple data display. Grouping of voluminous data objects into homogenous clusters is a crucial operation in soft computing. Choosing the right clustering technique and the correct number of partitions to divide the healthcare data for effective analysis is complicated and challenging most of the time. This research work examines clustering approaches on the healthcare datasets with the optimum K-clusters, in order to perform the analysis of the data. In this work, the K-means clustering method is examined and the silhouette score is computed to estimate the optimal K-value and the quality of the cluster.

Healthcare analytics in non-profits: Evidence from North America

Background: The implementation of Big Data analytics in healthcare has an incredible chance of improving the quality of care, minimizing waste and error, and also decreasing the cost of care. Purpose: This systematic comment of literature objectives to discover the assortment of Big Data analytics in healthcare, including its applications and challenges in the adoption of its in healthcare. Furthermore, it intends to figure out the strategies to overcome the challenges. Data sources: An organized search of the articles was carried out on five primary scientific databases: ScienceDirect, Taylor, Francis and Emerald. The information articles on Big Data analytics in healthcare published from January 2017 to January 2022 are deemed. Data extraction: two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in the healthcare field; challenges and strategies to overcome the issues in healthcare. Results: A complete of fifty eight articles are selected as per the inclusion needs and examined. The analyses of these posts placed that scientists do not have consensus about the useful definition of Big Data in serious healthcare. Big Data analytics finds the application for healthcare option support, optimization of healthcare operations, and reduction of treatment cost. The chief fight in serious adoption of Big Data analytics is non accessibility of evidence of its in healthcare. Conclusion: This review analysis unveils that there is a paucity of information on evidence of world utilization that is real of Big Data analytics in healthcare. Keywords: Big data, healthcare analytics.

PHAS: An End-to-End, Open-Source, and Portable Healthcare Analytics Stack

In today's binary world, digital health is of paramount importance, primarily due to the prevalence of IoT devices, upsurging health costs, growing elderly population, and shortage of clinical providers, to name a few. In light of these, providing efficient and smarter full-stack healthcare data analytics to manage and process healthcare data is a crucial topic from both academic and professional perspectives. The key goals of this full-stack healthcare data analytics are to detect health issues and promote human-being health proactively. The existing healthcare data analytics stacks are generally classified into commercial or open-source solutions. In designing a healthcare data stack, it is critical to offer the researchers a collaborative, modular, easy-to-use, cost/time-effective, reproducible, uniform, and shared- knowledge framework. Such healthcare stacks need to pave the way for researchers to focus on developing data analytics algorithms, and the underlying infrastructure should comply with longitudinal characteristics of healthcare data. Nonetheless, the existing healthcare data analytics stacks need holistically incorporate all the parameters mentioned. In this paper, we propose a novel healthcare data analytics stack called Open-Source Portable Healthcare Analytics Stack (PHAS) to address this issue. PHAS considers the mentioned features by fusing the merits of open-source and commercial solutions at the right place in its architecture. PHAS proposes a new shared-knowledge and time-series-aware framework, which enables researchers to perform health data collection, integration, storage, visualization, and analysis. Moreover, we demonstrate the capabilities of the PHAS framework by implementing an anomaly detection algorithm for heart rate and blood pressure anomaly detection using the Medical Information Mart for Intensive Care III (MIMIC III) dataset. We provide open-source PHAS for the community to integrate into their solutions.

Big Data Analytics in Healthcare: An Overview

Big Data Analytics in Healthcare: An Overview

A Semantic Approach for Extracting the Medical Association Rules

Healthcare data analytics has indispensable responsibility in advancing remedial decision-making and healthcare practices. In this paper, a novel semantic approach is introduced for extracting medical association rules, designed at discovering expressive interactions between medical things. Our methodology incorporates medical ontologies, and improved semantic measures to boost the accuracy and interpretability of the obtained rules. Our method delivers clinically pertinent information into patient perspectives on mental health, the accessibility of mental health resources, and the influence of physical health on mental well-being by mapping medical notions to ontological items and examining semantic relationships. Experimental validation on a case study of mental health dataset proves the dominance of our method over traditional association rule mining methods. The proposed semantic method presents valuable support to healthcare data analysis and decision-making processes.

HOW HEALTHCARE INDUSTRY CAN LEVERAGE BIG DATA ANALYTICS TECHNOLOGY AND TOOLS FOR EFFICIENT MANAGEMENT

Recently, huge quantities of organised, unstructured, and semi structured data are being produced by different systems and machines around the world. Such a huge volume of heterogenous data set that typically cannot be managed by traditional system is described as big data. The healthcare sector continues to be confronted by the importance of managing the fundamental information created by numerous sources, known for creating high volumes of heterogeneous information. Different big data analytics programs, as well as strategies, have already been created for managing these substantial quantities of information in the healthcare market. In this particular paper, we go over the effect of big data on healthcare, as well as different methods offered in the Hadoop ecosystem for managing it. We also examine the conceptual structure of big data analytics for healthcare, which involves the information collecting historical past of various limbs, the genome database, electronic health records, text/imagery, and then the medical conclusions support program.

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Hydrocephalus classification in brain computed tomography medical images using deep learning

Recent technological advancements, like big data analytics, is driving the growing adoption of cyber-physical systems and digital twins in the area of healthcare. Congenital hydrocephalus is one important example of recent healthcare data analytics. Congenital hydrocephalus is a buildup of excess cerebrospinal fluid (CSF) in the brain at birth. Congenital hydrocephalus can be lethal without treatment and represents an urgent issue in present-day clinical practice. Congenital hydrocephalus has a significant effect on a human entire life since it causes damage to the brain. It is important to accurately diagnose hydrocephalus early, which will help in the early treatment of the infant by a surgical procedure called ventriculoperitoneal (VP) shunt which will reduce the damage caused by hydrocephalus on the brain. Deep Learning is an evolving technology that is currently actively researched in the field of radiology. Compared to the traditional hydrocephalus diagnosing techniques, automatic diagnosing algorithms in deep learning can save diagnosis time, improve diagnosing accuracy, reduce cost, and reduce the radiologist's workload. In this paper, we have used a novel dataset collected from king Hussein medical center hospital in Jordan that consists of CT scans for hydrocephalus and non-hydrocephalus infants, the dataset has gone through multiple stages in preprocessing which are; cropping and filtering, normalization, segmentation (three segmentation techniques have been applied), and augmentation. These data have been used to build deep learning and machine learning models that will help physicians in the early and accurate diagnosing of congenital hydrocephalus which will lead to a decrease in the death rate and brain damage. The results of our models were impressive with a 98.5% accuracy for congenital hydrocephalus classification in infants' brain CT images.

Retracted: Spread of Online Public Opinion of Animal Epidemic Emergency: A Case Study of the H7N9 Incident Based on Healthcare Data Analytics.

[This retracts the article DOI: 10.1155/2021/1512742.].

Machine learning ensemble approach for healthcare data analytics

In healthcare machine learning is used mainly for disease diagnosis or acute condition detection based on patient data analysis. In the proposed work diabetic patient dataset analysis is done for hypoglycemia detection which means the lowering of blood glucose level. Often in healthcare it is observed that the dataset is imbalanced. Therefore an Ensemble Approach using imbalanced dataset techniques Synthetic Minority Over-sampling Technique and Adaptive Synthetic oversampling methods with different evaluation methods like train-test, k-fold, Stratified K-Fold and repeat train-test were used. This ensemble approach was implemented on diabetic dataset using K-Nearest Neighbor, Support Vector Machine, Random Forest, Naïve Bayes and Logistic Regression classifiers with average Stacking-C method thereafter to conclude. Comparative analysis was done using three different considerations. The results showed that KNN and Random forest gives more stable metric values both on balanced and imbalanced dataset. The confusion matrix consideration concluded that KNN and Random Forest were found to be better with least false negative and maximum true positive count. But if average train and test time is taken into consideration then Naïve Bayes and Random forest had least average train-test time. Thus the three different considerations concluded that the proposed ensemble approach gives better clarity for different classifier implementation using machine learning.

Understanding the User-Generated Geographic Information by Utilizing Big Data Analytics for Health Care.

There are two main ways to achieve an active lifestyle, the first is to make an effort to exercise and second is to have the activity as part of your daily routine. The study's major purpose is to examine the influence of various kinds of physical engagements on density dispersion of participants in Shanghai, China, and even prototype check-in data from a Location-Based Social Network (LBSN) utilizing a mix of spatial, temporal, and visualization methodologies. This paper evaluates Weibo used for big data evaluation and its dependability in some types rather than physically collected proofs by investigating the relationship between time, class, place, frequency, and place of check-in built on geographic features and related consequences. Kernel density estimation has been used for geographical assessment. Physical activities and frequency allocation are formed as a result of hour-to-day consumption habits. Our observations are based on customer check-in activities in physical venues such as gyms, parks, and playing fields, the prevalence of check-ins, peak times for visiting fun parks, and gender disparities, and we applied relative difference formulation to reveal the gender difference in a much better way. The purpose of this research is to investigate the influence of physical activity and health-related standard of living on well-being in a selection of Shanghai inhabitants.

Challenges of Big Data and Cyber Security in Health Center

Today, big data analytics are used in many fields. This section introduces the characteristics of big data, which is a visualization tool for big data, main prospects of big data use in healthcare, Benefits of Big Data Analytics in Healthcare and Progress. This document describes big data technologies in healthcare. blockchain and Data protection is becoming more important in the medical field Privacy issues and blockchain-based cyber security are also affected scrutinized closely. Healthcare is changing rapidly, and so is technology. It should be modified to suit the needs expressed by the provider. When Big data is necessary to promote personalized medicine Good luck in the field of healthcare, but there are many challenges Big data in healthcare and cyber security discussed..

Deriving Value from Big Data Analytics in Healthcare: A Value-focused Thinking Approach

With the potential to generate more insights from data than ever before, big data analytics has become highly valuable to many industries, especially healthcare. Big data analytics can make important contributions to many areas, such as enhancements in the quality of patient care and improvements in operational efficiencies. Big data analytics provides opportunities to address concerns such as disease diagnoses and prevention. However, it has posed challenges such as data security and privacy issues. Also, healthcare institutions have concerns about deriving the greatest benefit from their big data analytics endeavors. Therefore, identifying actionable objectives that can help healthcare organizations derive the maximum value from big data analytics is needed. Using the value-focused thinking (VFT) approach, we interviewed individuals associated with data analytics in healthcare to identify actionable objectives that one needs to consider to derive value from big data analytics, which practitioners can use for their own endeavors and provide opportunities for future research.