scholarly journals DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

Computation ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 25 ◽  
Author(s):  
Abhaya Kumar Sahoo ◽  
Chittaranjan Pradhan ◽  
Rabindra Kumar Barik ◽  
Harishchandra Dubey
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Deep Learning ◽  
Recommender System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Condition ◽  
Healthcare Sector ◽  
Learning Method ◽  
Decision Making Processes

In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches.

Survey of Deep-learning Techniques in Big-Data analytics

2021 ◽  
Author(s):  
Shubhashish Goswami ◽  
Abhimanyu Kumar
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Learning Process ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Operational Parameters ◽  
Learning Techniques ◽  
The Impact ◽  
Business Sectors

Abstract The present elaboration of Big-data research studies relying upon Deep-learning methods had revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms' operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. The present enhancements in the Deep-learning approaches in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition to having more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper elaborates the above statements stating the impact of the Deep-learning process utilizing the Big-data to operate in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing on having the impacts of operational parameters, concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors, the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

scholarly journals Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance

2021 ◽  
Author(s):  
Philipp Korherr ◽  
Dominik Kanbach
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Scientific Basis ◽  
Critical Factors ◽  
Organizational Capability ◽  
First Order ◽  
Management Capability ◽  
Decision Making Processes

AbstractThis study intends to provide scholars and practitioners with an understanding of human resource challenges in the context of Big Data Analytics (BDA). This paper provides a holistic framework of human-related capabilities that organizations must consider when implementing BDA to facilitate decision-making. For this purpose, the authors conducted a systematic literature review adapted from Tranfield et al. (BJM 14:207–222, 2003) to identify relevant studies. The 75 publications reviewed provided the sample for an inductive, and systematic data evaluation following the well-known and accepted approach introduced by Gioia et al. (ORM 16:15–31, 2012). The comprehensive review uncovered 33 first-order concepts linked to human-related capabilities, which were distilled into 15 s-order themes and then merged into five aggregated dimensions: Personnel Capability, Management Capability, Organizational Capability, Culture and Governance Capability, and Strategy and Planning Capability. The study is, to the best of the authors’ knowledge, the first to categorize all relevant human-related capabilities for successful BDA application. As such, it not only provides the scientific basis for further research, but also serves as a useful overview of the critical factors for BDA use in decision-making processes.

scholarly journals Deep Learning Techniques to Address Issues in Data Quality and Data Variety

2019 ◽  
Vol 8 (4) ◽  
pp. 5950-5956
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Data Quality ◽  
Data Analytics ◽  
Medical Information ◽  
Big Data Analytics ◽  
Training Data ◽  
Huge Amount ◽  
Learning Techniques

Deep Learning and Big Data Analytics are key focus in current rapidly growing environment. The use of large data has become crucial to different organizations as they collecting huge amount of domain-specific data, which contains critical information about cyber security, theft detection, national resources, business economics, marketing, and medical information. The assessment of this huge amount of data needs advanced and improved analytical techniques for surveying and guessing future courses of action by making advanced decision-making strategies. Deep learning algorithms utilize the collected training data, to create a representation model. This model uses the computer for predictions or decision making about new data without needing to train the machine explicitly to perform user task. These techniques and algorithms infer greater level complicated abstractions as data are represented through tree like structure. A major use of Deep Learning is processing, learning and training from the huge amounts of unsupervised data, analyze patterns from the data and can be used for large Datasets in which the raw data is largely unlabeled and not classified. In this paper, Deep Learning techniques for addressing Data of different variety/formats is analyzed, enabling fast and full processing and integration of large amounts of different variety of information i.e. Data transformation is also addressed. It also addresses the quality of data as the performances of a machine improve depending on the data quality. Further exploration on the deep learning techniques to assist Big Data by focusing on two key topics: (1) is it possible for Deep Learning to assist some of the specific problems like Data Variety and Data Quality in Big Data Analytics, and (2) Whether these techniques can aid in processing the Big Data

scholarly journals Understanding abortion seeking care outside of formal health care settings in Cape Town, South Africa: a qualitative study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Jane Harries ◽  
Kristen Daskilewicz ◽  
Tshegofatso Bessenaar ◽  
Caitlin Gerdts
Keyword(s):  
South Africa ◽  
Health Care ◽  
Decision Making ◽  
Unintended Pregnancy ◽  
Informal Sector ◽  
Cape Town ◽  
Healthcare Sector ◽  
Safe Abortion ◽  
Decision Making Processes ◽  
Formal Health Care

Abstract Background Although abortion was legalized in South Africa in 1996, barriers to safe, legal abortion services remain, and women continue to seek abortions outside of the formal healthcare sector. This study explored the decision-making processes that women undertake when faced with an unintended pregnancy, the sources of information used to make their decisions and the factors that contribute to their seeking of informal sector abortion in Cape Town, South Africa. Methods We conducted 15 semi-structured in-depth interviews in English with women who had accessed an abortion outside of the formal health care sector. Women were recruited with the assistance of a community-based key informant. Data was analyzed using a thematic analysis approach. Results Participants were aware that abortions were legal and accessible in public clinics, however they were concerned that others would find out about their unintended pregnancy and abortion if they went to legal providers. Women were also concerned about judgment and mistreatment from providers during their care. Rather than seek care in the formal sector, women looked past concerns around the safety and effectiveness of informal sector abortions and often relied on their social networks for referrals to informal providers. Conclusions The findings highlight the decision-making processes employed by women when seeking abortion services in a setting where abortion is legal and demonstrate the role of institutional and societal barriers to safe abortion access. Abortion service delivery models should adapt to women’s needs to enhance the preferences and priorities of those seeking abortion care-including those who prefer facility-based care as well as those who might prefer self-managed medical abortions.

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