Using Similarity Metrics on Real World Data and Patient Treatment Pathways to Recommend the Next Treatment.

Non-small-cell lung cancer (NSCLC) is one of the most prevalent types of lung cancer and continues to have an ominous five year survival rate. Considerable work has been accomplished in analyzing the viability of the treatments offered to NSCLC patients; however, while many of these treatments have performed better over populations of diagnosed NSCLC patients, a specific treatment may not be the most effective therapy for a given patient. Coupling both patient similarity metrics using the Gower similarity metric and prior treatment knowledge, we were able to demonstrate how patient analytics can complement clinical efforts in recommending the next best treatment.

Medication Adherence Prediction Through Online Social Forums: A Case Study of Fibromyalgia.

Background: Medication nonadherence can compound into severe medical problems for patients. Identifying patients who are likely to become nonadherent may help reduce these problems. Data-driven machine learning models can predict medication adherence by using selected indicators from patients' past health records.

Leveraging user search behavior to design personalized browsing interfaces for healthcare Web sites.

Understanding and leveraging user search behavior is increasingly becoming a key component towards improving web sites functionality for the health care consumer and provider. In this study we propose to leverage user search behavior to design user-tailored browsing interfaces to better support locating information in healthcare websites at the point-of-need.

Visualization of usability and functionality of a professional website through web-mining.

Functional interface design requires understanding of the information system structure and the user. Web logs record user interactions with the interface, and thus provide some insight into user search behavior and efficiency of the search process. The present study uses a data-mining approach with techniques such as association rules, clustering and classification, to visualize the usability and functionality of a digital library through in depth analyses of web logs.