Predictive Analytics with Sequence-based Clustering and Markov Chain

This research proposes a predictive modeling framework for Web user behavior with Web usage mining (WUM). The proposed predictive model utilizes sequence-based clustering, in order to group Web users into clusters with similar Web browsing behavior and Markov chains, in order to model Web users’ Web navigation behavior. This research will also provide a performance evaluation framework and suggest WUM systems that can improve advertisement placement and target marketing in a Web site

A Revision of Preventive Web-based Psychotherapies in Subjects at Risk of Mental Disorders

For the last years, the impulse of new technologies has overcome the traditional pathways of face-to-face clinical intervention and web-based psychological methodologies for intervention have started to gain success. This study aims to review the state-of-art about the effectiveness studies on preventive web- based interventions accomplished in samples of subjects at high risk for depressive, anxiety, eating behavior, problematic substance use symptoms and promotion of psychological well-being. Results showed that web-based psychological interventions for the prevention of mental disorders seemed to be effective for at risk individuals. Online health promotion in the general population was also effective to avoid the onset of clinical psychological circumstances. Future research should focus on personalized online intervention and on the evaluation of web-based engagement

On the application of data-driven population segmentation to design patient-centred integrated care

Rationale: Retailers use segmentation methods to identify groups of distinct and homogeneous customers, tailoring their products and services to these groups. In healthcare, patient-centred integrated care also aims to design care models around the patient, but the use of data to support this is limited. Data-driven segmentation could be used to identify patients with similar care needs, who might benefit from integrated care initiatives. Aim: To define the potential role of data-driven population segmentation in designing patient-centred integrated care. Methods: Existing applications of segmentation in healthcare were explored through literature, case study and systematic reviews. Segmentation analyses were performed on a 300,000-patient database, containing primary and secondary care data. Methods included k-means cluster analysis, regression analysis, artificial neural networks and decision trees, in addition to descriptive and statistical analyses. Results: Several integrated care programmes apply segmentation, but their use of data-driven methods is limited. Nevertheless, there exist many healthcare studies that used cluster analysis to segment patient populations. Segmenting a whole population resulted in eight distinct care user segments, providing an evidence base for population health. Segmenting the subpopulation of patients with ACSC hospitalisations identified four different care utilisation patterns, each requiring different preventive interventions. Risk stratification is a segmentation method in itself, but descriptive segmentation can help to identify different groups within the high-risk population. Where no patient-level data is available, an a priori rule can be used to identify high-needs patients. Conclusion: Data-driven segmentation can play an important role in designing patient-centred integrated care. It can be used to describe different patient groups within a population, a subpopulation, or a high-risk population, and design integrated care interventions around the needs of each segment. It can also be used to predict which patients are in the target group for integrated care initiatives.Open Acces