A recommendation method based on link prediction in drug-disease bipartite network

Gündoğan, Esra; Kaya, Buket

doi:10.1109/aiact.2017.8020081

Cited by 8 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to diagnostic suggestions, treatment plans for frequent diseases are relatively fixed, which means treatment recommendation is more knowledge dependent. Therefore, the study of treatment recommendation in cognitive computing-based CDSS puts more focus on constructing the medical knowledge Health Data Science graph, e.g., the disease-drug bipartite graph [99], where treatment plans are induced once the diagnosis is determined.…”

Section: Treatment Recommendationmentioning

confidence: 99%

Cognitive Computing-Based CDSS in Medical Practice

Chen

Huang

et al. 2021

Health Data Sci

View full text Add to dashboard Cite

Importance. The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies. From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making. This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade. Highlights. (1) A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system. (2) Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework. (3) The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction. Conclusion. Different from medical content providers, cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data. The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories. Given the current status of primary health care like high diagnostic error rate and shortage of medical resources, it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.

show abstract

Section: Treatment Recommendationmentioning

confidence: 99%

Cognitive Computing-Based CDSS in Medical Practice

Chen

Huang

et al. 2021

Health Data Sci

View full text Add to dashboard Cite

show abstract

“…Graphs can be designed as directed, undirected, weighted, unweighted or bipartite according to study. Bipartite graphs are an important type of social networks and are often encountered in real world [18–21]. A bipartite graph is defined as Definition 1.…”

Section: Link Prediction In Bipartite Networkmentioning

confidence: 99%

Hotel recommendation system by bipartite networks and link prediction

Kaya

2019

Journal of Information Science

Self Cite

View full text Add to dashboard Cite

With the rapid growth of the Internet in recent years, online social media has become very important for people. People often use social media tools to communicate and share their ideas or experiences regardless of time and place. One of the areas where the use of these tools is widespread is tourism. It is one of the hardest tasks to find a suitable hotel for travellers. There are many websites where accommodation services of the tourism enterprises are evaluated. People share their experiences about the hotels they stayed through these websites. Positive or negative comments are an effective factor in hotel selection. The purpose of this study is to construct a hotel recommendation system based on user’s location using online hotel reviews. The reviews crawled from TripAdvisor.com were filtered according to their total scores, and a dataset was obtained consisting of users and reviews of their liked hotels. For the recommendation task, first, three different bipartite networks consisting of users and hotels were modelled, which were global, country and city based. Then, in these networks, it was recommended hotels to users for their next choice with link prediction methods, using the common hotels where the users prefer to stay in the past. The most successful results were obtained in the city-based network. With this study, it was tried to reduce the time spent reading the online reviews and finding the suitable hotel. To the best of our knowledge, this is the first study on recommending a hotel using online reviews by bipartite networks.

show abstract

“…Nowadays, the Internet is highly needed in people's lives, as people are dependent for performing routine activities such as making necessary purchases, conducting financial transactions, and entertainments, among others. Businesses like to use this opportunity to provide services and products to customers [1][2][3], as companies such as Amazon, Yahoo, Netflix, Facebook, and IMDB benefit from this technology [4,5]. Providing online shopping is not enough anymore to survive in the competitive market for the e-commerce world, as customers may easily visit different stores and vendors.…”

Section: Introductionmentioning

confidence: 99%