Data Science Tools and Techniques for Healthcare Applications

2021 ◽  
pp. 213-233
Author(s):  
Srinidhi Hiriyannaiah ◽  
Siddesh G. M. ◽  
Divya ◽  
R. Aravind Shreyas ◽  
Dheeraj Bhat ◽  
...  
Keyword(s):  
Data Science ◽  
Healthcare Applications ◽  
Tools And Techniques

scholarly journals Analysing Structured Learning Behaviour in Massive Open Online Courses (MOOCs): An Approach Based on Process Mining and Clustering

2018 ◽  
Vol 19 (5) ◽  
Author(s):  
Antoine Van den Beemt ◽  
Joos Buijs ◽  
Wil Van der Aalst
Keyword(s):  
Data Science ◽  
Online Courses ◽  
Process Mining ◽  
Massive Open Online Courses ◽  
Structured Learning ◽  
Learning Behaviour ◽  
Specific Behaviour ◽  
Learning Progress ◽  
Tools And Techniques ◽  
Successful Students

The increasing use of digital systems to support learning leads to a growth in data regarding both learning processes and related contexts. Learning Analytics offers critical insights from these data, through an innovative combination of tools and techniques. In this paper, we explore students’ activities in a MOOC from the perspective of personal constructivism, which we operationalized as a combination of learning behaviour and learning progress. This study considers students’ data analyzed as per the MOOC Process Mining: Data Science in Action. We explore the relation between learning behaviour and learning progress in MOOCs, with the purpose to gain insight into how passing and failing students distribute their activities differently along the course weeks, rather than predict students' grades from their activities. Commonly-studied aggregated counts of activities, specific course item counts, and order of activities were examined with cluster analyses, means analyses, and process mining techniques. We found four meaningful clusters of students, each representing specific behaviour ranging from only starting to fully completing the course. Process mining techniques show that successful students exhibit a more steady learning behaviour. However, this behaviour is much more related to actually watching videos than to the timing of activities. The results offer guidance for teachers.

scholarly journals Mathematical Issues in Data Science and Applications for Health care

2019 ◽  
Author(s):  
Sitti Zuhaerah Thalhah ◽  
Mohammad Tohir ◽  
Phong Thanh Nguyen ◽  
K. Shankar ◽  
Robbi Rahim
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Source Data ◽  
Military Applications

For development in military applications, industrial and government the predictive analytics and decision models have long been cornerstones. In modern healthcare system technologies and big data analytics and modeling of multi-source data system play an increasingly important role. Into mathematical models in these domains various problems arising that can be formulated, by using computational techniques, sophisticated optimization and decision analysis it can be analyzed. This paper studies the use of data science in healthcare applications and the mathematical issues in data science.

A Semantic Web Pragmatic Approach to Develop Clinical Ontologies, and thus Semantic Interoperability, based in HL7 v2.xml Messaging

2013 ◽  
pp. 205-214 ◽  
Author(s):  
David Mendes ◽  
Irene Pimenta Rodrigues
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Knowledge Representation ◽  
Ontology Mapping ◽  
Knowledge Representation And Reasoning ◽  
Healthcare Applications ◽  
Current Trends ◽  
Web Tools ◽  
Ontology Population ◽  
Tools And Techniques

The ISO/HL7 27931:2009 standard intends to establish a global interoperability framework for healthcare applications. However, being a messaging related protocol, it lacks a semantic foundation for interoperability at a machine treatable level intended through the Semantic Web. There is no alignment between the HL7 V2.xml message payloads and a meaning service like a suitable ontology. Careful application of Semantic Web tools and concepts can ease the path to the fundamental concept of Shared Semantics. In this chapter, the Semantic Web and Artificial Intelligence tools and techniques that allow aligned ontology population are presented and their applicability discussed. The authors present the coverage of HL7 RIM inadequacy for ontology mapping and how to circumvent it, NLP techniques for semi-automated ontology population, and the current trends about knowledge representation and reasoning that concur to the proposed achievement.

Digital continuous healthcare and disruptive medical technologies: m-Health and telemedicine skills training for data-driven healthcare

2018 ◽  
Vol 25 (10) ◽  
pp. 623-635 ◽  
Author(s):  
A Hasan Sapci ◽  
H Aylin Sapci
Keyword(s):  
Health Informatics ◽  
Data Science ◽  
Medical Information ◽  
Training Session ◽  
Skills Training ◽  
Application Development ◽  
Healthcare Applications ◽  
Wireless Monitoring ◽  
Medical Technologies ◽  
Monitoring Devices

Introduction Disruptive medical technologies, wearable devices and new diagnostic solutions have been shaping the future of healthcare, and the health informatics skills gap has become a major problem for technology-centric healthcare applications. This study evaluated the relationships between a specific practical skills training method and students' confidence in using wireless monitoring devices along with the attitude towards technology adoption. Methods Six practical exercises were developed to provide health informatics technical skills to transfer medical information and display multi-channel biological signals. Two hundred and six undergraduate nursing students received a telemedicine and homecare training course. Their familiarity with various data formats and likelihood to recommend telemedicine and remote monitoring applications were measured. Results The skills training session changed students' attitudes towards remote patient monitoring, and the majority of students provided positive feedback about their confidence in using wireless monitoring devices after the training session. Students stated their plans to use the technology when they start practising and to educate their patients to promote the use of telemedicine. Conclusion We propose a skills training framework that covers (a) telemedicine, (b) m-Health and connected health, (c) health informatics application development, (d) health informatics device innovation, and (e) data science.

Amelioration of Big Data Analytics by Employing Big Data Tools and Techniques

2018 ◽  
pp. 212-232
Author(s):  
Stephen Dass ◽  
Prabhu J.
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Volume ◽  
Data Science ◽  
Big Data Analytics ◽  
Data Capture ◽  
Digital Data ◽  
Step Process ◽  
Science Domain ◽  
Tools And Techniques

This chapter describes how in the digital data era, a large volume of data became accessible to data science engineers. With the reckless growth in networking, communication, storage, and data collection capability, the Big Data science is quickly growing in each engineering and science domain. This paper aims to study many numbers of the various analytics ways and tools which might be practiced to Big Data. The important deportment in this paper is step by step process to handle the large volume and variety of data expeditiously. The rapidly evolving big data tools and Platforms have given rise to numerous technologies to influence completely different Big Data portfolio.In this paper, we debate in an elaborate manner about analyzing tools, processing tools and querying tools for Big datahese tools used for data analysis Big Data tools utilize numerous tasks, like Data capture, storage, classification, sharing, analysis, transfer, search, image, and deciding which might also apply to Big data.

scholarly journals A New Distributed Type-2 Fuzzy Logic Method for Efficient Data Science Models of Medical Informatics

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Fatéma Zahra Benchara ◽  
Mohamed Youssfi
Keyword(s):  
Fuzzy Logic ◽  
Data Analysis ◽  
High Performance ◽  
Data Science ◽  
Prediction Models ◽  
Medical Decision ◽  
Healthcare Applications ◽  
Fuzzy Logic Method ◽  
Logic Method

The paper aims to propose a distributed method for machine learning models and its application for medical data analysis. The great challenge in the medicine field is to provide a scalable image processing model, which integrates the computing processing requirements and computing-aided medical decision making. The proposed Fuzzy logic method is based on a distributed approach of type-2 Fuzzy logic algorithm and merges the HPC (High Performance Computing) and cognitive aspect on one model. Accordingly, the method is assigned to be implemented on big data analysis and data science prediction models for healthcare applications. The paper focuses on the proposed distributed Type-2 Fuzzy Logic (DT2FL) method and its application for MRI data analysis under a massively parallel and distributed virtual mobile agent architecture. Indeed, the paper presents some experimental results which highlight the accuracy and efficiency of the proposed method.

scholarly journals The role of data science in healthcare advancements: applications, benefits, and future prospects

2021 ◽  
Author(s):  
Sri Venkat Gunturi Subrahmanya ◽  
Dasharathraj K. Shetty ◽  
Vathsala Patil ◽  
B. M. Zeeshan Hameed ◽  
Rahul Paul ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Unstructured Data ◽  
Healthcare Applications ◽  
Interdisciplinary Field

AbstractData science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. The healthcare industry generates large datasets of useful information on patient demography, treatment plans, results of medical examinations, insurance, etc. The data collected from the Internet of Things (IoT) devices attract the attention of data scientists. Data science provides aid to process, manage, analyze, and assimilate the large quantities of fragmented, structured, and unstructured data created by healthcare systems. This data requires effective management and analysis to acquire factual results. The process of data cleansing, data mining, data preparation, and data analysis used in healthcare applications is reviewed and discussed in the article. The article provides an insight into the status and prospects of big data analytics in healthcare, highlights the advantages, describes the frameworks and techniques used, briefs about the challenges faced currently, and discusses viable solutions. Data science and big data analytics can provide practical insights and aid in the decision-making of strategic decisions concerning the health system. It helps build a comprehensive view of patients, consumers, and clinicians. Data-driven decision-making opens up new possibilities to boost healthcare quality.

scholarly journals Interfaces to Scripting Languages in Visual Analytics Applications

2020 ◽  
Author(s):  
Johanna Schmidt
Keyword(s):  
Visual Analytics ◽  
Data Science ◽  
Visual Analysis ◽  
Future Research ◽  
Research Directions ◽  
Scripting Languages ◽  
Daily Work ◽  
Future Research Directions ◽  
Tools And Techniques ◽  
Levels Of Integration

The need to use data visualization and visual analysis in various fields has led to the development of feature-rich standalone applications such as Tableau and MS Power BI. These applications provide ready-to-use functionality for loading, analyzing and visualizing data, even for users who are not familiar with programming and scripting. Meanwhile, data scientists have to combine many different tools and techniques in their daily work, since no standalone application can yet cover the entire workflow. As a result, a rich landscape of open source libraries is available today, covering various tasks from data analysis to modeling and visualization. To combine the best of two worlds, interfaces for scripting languages have been integrated into standalone applications in recent years. We analyzed which interfaces to six common scripting languages are offered. The interfaces offer different levels of integration and therefore support different steps of the data science workflow. In this paper we investigated the integration levels of script languages in standalone applications and divided them into four groups. We used this classification to evaluate 13 standalone visual analysis applications currently available on the market. We then analyzed which groups of applications best support which steps in the data science workflow. We found that a tight integration of scripting languages can especially support the explorative analysis and modeling phase of the data science workflow. We also discuss our results in the light of visual analysis research and give suggestions for future research directions.

scholarly journals Mathematical Issues in Data Science and Applications for Health care

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4153-4156
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Source Data ◽  
Military Applications

For development in military applications, industrial and government the predictive analytics and decision models have long been cornerstones. In modern healthcare system technologies and big data analytics and modeling of multi-source data system play an increasingly important role. Into mathematical models in these domains various problems arising that can be formulated, by using computational techniques, sophisticated optimization and decision analysis it can be analyzed. This paper studies the use of data science in healthcare applications and the mathematical issues in data science

The Contribution of Data Science Applied to Customer Relationship Management

2021 ◽  
pp. 522-543
Author(s):  
Dora Maria Simões
Keyword(s):  
Digital Media ◽  
Customer Relationship Management ◽  
Data Science ◽  
Relationship Management ◽  
Customer Relationship ◽  
Academic Researcher ◽  
Contemporary World ◽  
The Face ◽  
Relationship Processes ◽  
Tools And Techniques

In the face the contemporary world lives, and the consequent data produced at an unprecedented speed through digital media platforms, the data are nowadays called the new global currency. It raises numerous opportunities to improve outcomes in businesses, namely at the level of customer relationship management (CRM) strategies and their systems. Nevertheless, how analytics can be applied and support the customer relationship processes seems unclear for academics and industries. To better connect customer relationship processes needs and what data science analytics can offer, this chapter presents a systematic literature review around the concepts, tools, and techniques behind this field, looking particularly on customer acquisition and customer retention in businesses. The outcomes highlight that academic researcher works in this field are very scare and recent. Searching the Scopus and Web of Science databases resulted in only 12 documents from 2013 to 2020, eight of them published in the last two years.

Sign in / Sign up

Export Citation Format

Share Document