scholarly journals A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1931
Author(s):  
Hassan Harb ◽  
Hussein Mroue ◽  
Ali Mansour ◽  
Abbass Nasser ◽  
Eduardo Motta Cruz
Keyword(s):  
Big Data ◽  
Real Time ◽  
Medical Staff ◽  
Patient Monitoring ◽  
Data Retrieval ◽  
Disease Diagnosis ◽  
Patient Classification ◽  
Healthcare Applications ◽  
Number Of Patients ◽  
Big Data Technologies

Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients’ classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.

Big Data Analytics Framework for Real-Time Genome Analysis: A Comprehensive Approach

2019 ◽  
Vol 16 (8) ◽  
pp. 3419-3427
Author(s):  
Shishir K. Shandilya ◽  
S. Sountharrajan ◽  
Smita Shandilya ◽  
E. Suganya
Keyword(s):  
Big Data ◽  
Real Time ◽  
Genome Analysis ◽  
High Speed ◽  
High Performance ◽  
Big Data Analytics ◽  
Good Precision ◽  
Entire Genome ◽  
Big Data Technologies ◽  
Sequencing Platforms

Big Data Technologies are well-accepted in the recent years in bio-medical and genome informatics. They are capable to process gigantic and heterogeneous genome information with good precision and recall. With the quick advancements in computation and storage technologies, the cost of acquiring and processing the genomic data has decreased significantly. The upcoming sequencing platforms will produce vast amount of data, which will imperatively require high-performance systems for on-demand analysis with time-bound efficiency. Recent bio-informatics tools are capable of utilizing the novel features of Hadoop in a more flexible way. In particular, big data technologies such as MapReduce and Hive are able to provide high-speed computational environment for the analysis of petabyte scale datasets. This has attracted the focus of bio-scientists to use the big data applications to automate the entire genome analysis. The proposed framework is designed over MapReduce and Java on extended Hadoop platform to achieve the parallelism of Big Data Analysis. It will assist the bioinformatics community by providing a comprehensive solution for Descriptive, Comparative, Exploratory, Inferential, Predictive and Causal Analysis on Genome data. The proposed framework is user-friendly, fully-customizable, scalable and fit for comprehensive real-time genome analysis from data acquisition till predictive sequence analysis.

scholarly journals THE IMPACT OF BIG DATA TECHNOLOGIES ON COMPETITIVE ADVANTAGE OF COMPANIES

2017 ◽  
pp. 255 ◽  
Author(s):  
Jelena Lukić
Keyword(s):  
Big Data ◽  
Competitive Advantage ◽  
Real Time ◽  
New Technologies ◽  
Knowledge And Skills ◽  
Big Data Technologies ◽  
The Impact

The emergence of large quantity of data, from various sources, available in real-time, known as Big Data, have stimulated development of new technologies, techniques, tools, knowledge and skills which allows to work with that data. Big Data represent not only the factor from environment that confronts the companies with avalanche of data, but also very imporant resource which provide opportunities for companies to make value on the basis of collected data. Characteristics and possibilities which Big Data technologies offer have positioned them as a valuable factor for gaining and sustaining the competitive advantage in companies. The aim of this paper is to examine how Big Data technologies impact on competitive advantage of the companies that use them.

scholarly journals A Real-time Approach for Dynamic Scheduling Pet Activities in a Hospital

2020 ◽  
Author(s):  
SHIN-YAN CHIOU ◽  
Ya-Xin Dong ◽  
Kun-Ju Lin
Keyword(s):  
Control System ◽  
Real Time ◽  
Waiting Time ◽  
Medical Staff ◽  
Dynamic Scheduling ◽  
Family Members ◽  
Patient Treatment ◽  
Number Of Patients ◽  
And Control ◽  
Automatic Scheduling

Abstract Background: At present, medical personnel use manual scheduling methods to calculate usage times of PET devices, hospital beds, operating rooms, and other treatment spaces. However, as patient treatment and recovery times are unequal and uncertain, arriving times of the scheduled/temporary patients are unexpected, examination items, beds requirements, and drugs waiting times are different, and scan-again requirements are unexpected. Manual scheduling arrangements are easy to make mistakes and put pressure on employees. With changing scheduling arrangements, it is difficult to announce the estimated time to patients and their families in real time. As a result, family members keep asking medical staff about various situations and waiting time. This makes the process unstable and exhausts the medical staff. Although previous researches proposed algorithms for specific inspections to solve the equipment resource allocation problems and calculate outpatient visit time for outpatient clinics, there is no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with Bluetooth Beacon positioning. Results: This system can automatically schedule, estimate, and instantly update the start and end time of various tasks of PET patients during examinations, and automatically allocate beds and real-time announce schedule information, allowing the schedule being automated, instant and almost optimized. Moreover, this system promptly reminds medical staffs in each area. This can greatly reduce the work of medical staff, avoid human error, improve the safety of drugs, and provide medical staff, patients and family members to view the inspection progress and estimated waiting time in real time, reducing the number of patients and family members to query medical staff. We implemented this system in an application for the Android system to prove the feasibility of the system. We also collected time data of 200 actual patients for 2 weeks in the Department of Nuclear Medicine of Chang Gung Memorial Hospital, Linkou, and put these data into the implementation program for simulation and comparison. It was found that the average time difference between manual and automatic scheduling was 7.32 minutes, and it could reduce the average examination time of 82% of patients by 6.14 minutes, proving the system's correctness and efficiency. To our knowledge, this paper is the first to propose a real-time automatic scheduling and control system for PET patients.

scholarly journals The First Step to Initiate Pediatric Palliative Care: Identify Patient Needs and Cooperation of Medical Staff

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 127
Author(s):  
Su Hyun Bae ◽  
Yeo Hyang Kim
Keyword(s):  
Palliative Care ◽  
Medical Staff ◽  
Care Service ◽  
Care Center ◽  
Palliative Care Service ◽  
Disease Diagnosis ◽  
Pediatric Palliative Care ◽  
Number Of Patients ◽  
Palliative Care Services ◽  
Limiting Condition

Few Korean hospitals had experience in pediatric palliative care. Since the beginning of the national palliative care project, interest in pediatric palliative care has gradually increased, but the establishment of professional palliative care is still inadequate due to a lack of indicators. This study aimed to find considerations in the process of initiating palliative care services. The general and clinical characteristics of 181 patients aged less than 24 years who were registered at the pediatric palliative care center from January 2019 to August 2021 were evaluated. Life-limiting condition group 1 had the largest number of patients. The primary need for palliative care was psychological and emotional support, followed by information sharing and help in communication with the medical staff in decision-making processes. Seventy-two patients were technologically dependent, with one to four technical supports for each patient. The registration of patients with cancer increased with time, and the time from disease diagnosis to consultation for pediatric palliative care service was significantly reduced. In conclusion, before starting pediatric palliative care, it is necessary to understand the needs of patients and their families and to cooperate with medical staff.

Big Data Applications and Architectures for Emerging Countries

2021 ◽  
pp. 120-142
Author(s):  
Anil K. Maheshwari
Keyword(s):  
Social Media ◽  
Big Data ◽  
Special Needs ◽  
Emerging Countries ◽  
Healthcare Sector ◽  
Healthcare Applications ◽  
Big Data Applications ◽  
Big Data Technologies

Exponential increases in generation of data, especially through social media, has found an increased influence in society over the last decade. This chapter provides an overview of big data technologies and architectures and how this data could be applied to meet the special needs of the emerging societies. Healthcare applications are most important, especially for the rural and the marginal sections of society. This chapter lays out architecture designs of 10 big data applications with half of them relating to the healthcare sector. These designs can be seeds for the implementation of other imaginative beneficial big data applications.

scholarly journals Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform (Preprint)

2018 ◽  
Author(s):  
Jacob McPadden ◽  
Thomas JS Durant ◽  
Dustin R Bunch ◽  
Andreas Coppi ◽  
Nathaniel Price ◽  
...  
Keyword(s):  
Health Care ◽  
Big Data ◽  
Precision Medicine ◽  
Open Source ◽  
Real Time ◽  
Data Science ◽  
Patient Monitoring ◽  
Medicine Research ◽  
Health Care Data ◽  
Precision Medicine Research

BACKGROUND Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth. OBJECTIVE The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform. METHODS We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics. RESULTS Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases. CONCLUSIONS The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research.

scholarly journals The Metamorphosis (of RAM3S)

2021 ◽  
Vol 11 (24) ◽  
pp. 11584
Author(s):  
Ilaria Bartolini ◽  
Marco Patella
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Streams ◽  
Multimedia Data ◽  
Data Streaming ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Big Data Technologies ◽  
Big Data Streams ◽  
The One

The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effectively exploited by analysts. RAM3S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. RAM3S has been proven helpful in simplifying the deployment of non-parallel techniques to streaming platforms, such as Apache Storm or Apache Flink. In this paper, we show how RAM3S has been updated to incorporate novel stream processing platforms, such as Apache Samza, and to be able to communicate with different message brokers, such as Apache Kafka. Abstracting from the message broker also provides us with the ability to pipeline several RAM3S instances that can, therefore, perform different processing tasks. This represents a richer model for stream analysis with respect to the one already available in the original RAM3S version. The generality of this new RAM3S version is demonstrated through experiments conducted on three different multimedia applications, proving that RAM3S is a formidable asset for enabling efficient and effective Data Mining and Machine Learning on multimedia data streams.

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