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Mitigating risk-of-readmission of Congestive Heart Failure (CHF) patients within 30 days of discharge is important because such readmissions are not only expensive but also critical indicator of provider care and quality of treatment. Accurately predicting the risk-of-readmission may allow hospitals to identify high-risk patients and eventually improve quality of care by identifying factors that contribute to such readmissions in many scenarios. In this paper, we investigate the problem of predicting risk-of-readmission as a supervised learning problem, using a multi-layer classification approach. Earlier contributions inadequately attempted to assess a risk value for 30 day readmission by building a direct predictive model as opposed to our approach. We first split the problem into various stages, (a) at risk in general (b) risk within 60 days (c) risk within 30 days, and then build suitable classifiers for each stage, thereby increasing the ability to accurately predict the risk using multiple layers of decision. The advantage of our approach is that we can use different classification models for the subtasks that are more suited for the respective problems. Moreover, each of the subtasks can be solved using different features and training data leading to a highly confident diagnosis or risk compared to a one-shot single layer approach. An experimental evaluation on actual hospital patient record data from Multicare Health Systems shows that our model is significantly better at predicting risk-of-readmission of CHF patients within 30 days after discharge compared to prior attempts.

by the immense growth of social applications in web environment, the role of trust in connecting people is getting more important than ever. Although many researchers have already conducted comprehensive studies on the trust related applications, the understanding of distrust relations is still unclear to the researchers. In this paper, we have investigated some of mechanisms that determine the signs of links in trust networks which consist of both trust and distrust relationships. Achieving this, we develop a framework of trust sign prediction, taking a machine-learning approach. We report experiments conducted on Epinions which is a well-known and very large collection of data dealing with trust computation. Empirical results show that the sign of relations in the trust networks can be effectively predicted using pre-trained classifiers.

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