Healthcare data are a valuable source of healthcare intelligence. Sharing of healthcare data is one essential step to make healthcare system smarter and improve the quality of healthcare service. Healthcare data, one personal asset of patient, should be owned and controlled by patient, instead of being scattered in different healthcare systems, which prevents data sharing and puts patient privacy at risks. Blockchain is demonstrated in the financial field that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we proposed an App (called Healthcare Data Gateway (HGD)) architecture based on blockchain to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private. Our proposed purpose-centric access model ensures patient own and control their healthcare data; simple unified Indicator-Centric Schema (ICS) makes it possible to organize all kinds of personal healthcare data practically and easily. We also point out that MPC (Secure Multi-Party Computing) is one promising solution to enable untrusted third-party to conduct computation over patient data without violating privacy.

Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control

Proceedings of world academy of science, engineering and technology, vol 8

Understanding social commerce

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Predicting Academic Performance of Students from VLE Big Data using Deep Learning Models

https://cyberleninka.org/article/n/566686.pdf

A Review on Predicting Student's Performance Using Data Mining Techniques

https://cyberleninka.org/article/n/320403.pdf

Data Mining in Healthcare – A Review

In this era of evolving technology, there are various channels and platforms through which travelers can find tour information and share their tour experience. These include tourism websites, social network sites, blogs, forums, and various search engines such as Google, Yahoo, etc. However, information found in this way is not filtered based on travelers’ preferences. Hence, travelers face an information overflow problem.. There is also increasing demand for more information on local area attractions, such as local food, shopping spots, places of interest and so on during the tour. The goal of this research is to propose a suitable recommendation method for use in a Personalized Location-based Traveler Recommender System (PLTRS) to provide personalized tourism information to its users. A comparative study of available recommender systems and location-based services (LBS) is conducted to explore the different approaches to recommender systems and LBS technology. The effectiveness of the system based on the proposed framework is tested using various scenarios which might be faced by users.

A Framework of a Personalized Location-based Traveler Recommendation System in Mobile Application

Procurement is one of the major aspects for an enterprise to improve its supply chain management and hence enterprise performance. The advent of Internet has introduced e-commerce and drastically changing the way procurement is done to improve operation efficiency and reduce transaction cost significantly. Thereby, this lead to the term "e-procurement" or "e-purchasing". However, many Small and Medium Enterprises (SMEs) still perceive e-procurement as infeasible although e-procurement is introduced in the early 2000. Hence, this paper attempts to provide a comprehensive survey on existing literature with regards to the benefits and barriers of e-procurement in the perspective of Malaysian SMEs. Highlighting the benefits and identifying the major barriers of SMEs e-procurement implementation enables formation of effective solution to encourage more adoption of eprocurement system in Malaysian SMEs.

/pdf/survey-on-benefits-and-barriers-of-e-procurement-malaysian-59jq2k8x2j.pdf

Survey on Benefits and Barriers of E-Procurement: Malaysian SMEs Perspective

rising costs and risks in health care have shifted the preference of individuals from health treatment to disease prevention. This prevention treatment is known as wellness. In recent years, the Internet has become a popular place for wellness-conscious users to search for wellness-related information and solutions. As the user community becomes more wellness conscious, service improvement is needed to help users find relevant personalised wellness solutions. Due to rapid development in the wellness market, users value convenient access to wellness services. Most wellness websites reflect common health informatics approaches; these amount to more than 70,000 sites worldwide. Thus, the wellness industry should improve its Internet services in order to provide better and more convenient customer service. This paper discusses the development of a wellness recommender system that would help users find and adapt suitable personalised wellness therapy treatments based on their individual needs. This paper introduces new approaches that enhance the convenience and quality of wellness information delivery on the Internet. The wellness recommendation task is performed using an Artificial Intelligence technique of hybrid case-based reasoning (HCBR). HCBR solves users’ current wellness problems by applying solutions from similar cases in the past. From the evaluation results for our prototype wellness recommendation system, we conclude that wellness consultants are using consistent wellness knowledge to recommend solutions for sample wellness cases generated through an online consultation form. Thus, the proposed model can be integrated into wellness websites to enable users to search for suitable personalized wellness therapy treatment based on their health condition.

/pdf/recommender-system-for-personalised-wellness-therapy-3km1y05dee.pdf

Wahidah Husain

Papers

A Review on Predicting Student's Performance Using Data Mining Techniques

Data Mining in Healthcare – A Review

A Framework of a Personalized Location-based Traveler Recommendation System in Mobile Application

Survey on Benefits and Barriers of E-Procurement: Malaysian SMEs Perspective

Recommender System for Personalised Wellness Therapy