Diabetes is a life-altering medical condition that affects millions of people and results in many hospitalizations per year. Consequently, predicting the length of stay of inhospital diabetic patients has become increasingly important for staffing and resource planning. Although statistical methods have been used to predict length of stay in hospitalized patients, many powerful machine learning techniques have not yet been explored. In this paper, we compare and discuss the performance of various supervised machine learning algorithms (i.e., multiple linear regression, support vector machines, multi-task learning, and random forests) for predicting long versus short-term length of stay of hospitalized diabetic patients.

Abstract. In this work, we consider a transfer learning approach based on K-means for splice site recognition. We use different representations for the sequences, based on n-gram graphs. In addition, a novel representation based on the secondary structure of the sequences is proposed. We evaluate our approach on genomic sequence data from model organisms of varying evolutionary distance. The first obtained results indicate that the proposed representations are promising for the problem of splice site recognition.

Learning Using Privileged Information (LUPI) is a learning paradigm that aims to improve supervised learning in the presence of additional (privileged) information available during training, but not during the testing phase. For example, the Multi-Ethnic Study of Atherosclerosis (MESA) used in epidemiological studies related to heart disease, contains data from 186 attributes, only eight of which are used in current risk prediction algorithms.