Data Mining in Healthcare and Biomedicine: A Survey of the Literature

By Illhoi Yoo, Patricia E. Alafaireet, Miroslav Marinov, Keila Pena-Hernandez, Rajitha Gopidi, Jia-Fu Chang, Lei Hua. Journal of medical systems, 2012

585 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management, Information Systems

View Paper: https://www.semanticscholar.org/paper/3682e46e898bb1cfcd135c79c3dcba48019ecf14

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Health Information Management

In Information Systems

Citations in the corpus

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013, methodology): “The authors in [18,20] found the data mining algorithms mainly in two categories (1) descriptive or unsupervised learning (i.”
Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review (2016, background): “For further information on how can these algorithms be applied in this application and for a comparison between them, we encourage the reader to consult some interesting reviews on these topics (see [171–173] for feature selection, [174] for classification and [175,176] for a wider overview of Data ”
Big Data Meet Green Challenges: Big Data Toward Green Applications (2016, background): “[94] discussed how data mining algorithms, such as those relevant to classification, clustering, and association, had been applied to the research in the biomedical and healthcare fields.”
A Neural Network Based Model for Predicting Psychological Conditions (2015, background): “Another recent study used support vector machines and random forest classifiers to try to predict cancer diagnosis using different types of patient data [13], and based on recent review papers, cancer and heart disease are two of the more commonly researched diseases with respect to predictive model”
The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges (2018, background): “large observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner—helps researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets [321].”