Learning patient-specific predictive models from clinical data

Learning patient-specific predictive models from clinical data

We introduce an algorithm for learning patient-specific models from clinical data to predict outcomes. Patient-specific models are influenced by the particular history, symptoms, laboratory results, and other features of the patient case at hand, in contrast to the commonly used population-wide models that are constructed to perform well on average on all future cases. The patient-specific algo...

Learning Patient - Specific Models from Clinical Data

Learning Instance-Specific Predictive Models

This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algorithm learns Markov blanket models, carries out Bayesian model averaging over a set of models to predict a target variable of the instance at hand, and employs an instance-specific heuristic to locate a set of suitable m...

Learning Culture-Specific Dialogue Models from Non Culture-Specific Data

We build culture-specific dialogue policies of virtual humans for negotiation and in particular for argumentation and persuasion. In order to do that we use a corpus of non-culture specific dialogues and we build simulated users (SUs), i.e. models that simulate the behavior of real users. Then using these SUs and Reinforcement Learning (RL) we learn negotiation dialogue policies. Furthermore, w...

Towards Machine Learning of Predictive Models from Ecological Data

In a previous paper we described a machine learning approach which was used to automatically generate food-webs from nationalscale agricultural data. The learned food-webs in the previous study consist of hundreds of ground facts representing trophic links between individual species. These species food-webs can be used to explain the structure and dynamics of particular eco-systems, however, th...