Term-based personalization for feature selection in clinical handover form auto-filling

Feature learning and selection have been widely applied in many research areas because of their good performance and lower complexity. Traditional methods usually treat all terms with same feature sets, such that performance can be damaged when noisy information is brought via wrong features for a given term. In this paper, we propose a term-based personalization approach to finding the best features for each term. First, features are given as the input so that we focus on selection strategies. Second, the importance of each feature subset to a given term is evaluated by the term-feature probabilistic relevance model. We present a feature searching method to generate feature candidate subsets for each term, since evaluating all the possible feature subsets is computationally intensive. Finally, we obtain the personalized feature set for each term as a subset of all features. Experiments have been conducted on the NICTA Synthetic Nursing Handover dataset and the results show that our approach is promising and effective.