Verbal Autopsy: First Steps Towards Questionnaire Reduction

Verbal Autopsy (VA) is the instrument used to collect Causes of Death (CoD) in places in which the access to health services is out of reach. It consists of a questionnaire addressed to the caregiver of the deceased and involves closed questions (CQ) about signs and symptoms prior to the decease. There is a global effort to reduce the number of questions in the questionnaire to the minimum essential information to ascertain a CoD. To this end we took two courses of action. On the one hand, the relation of the responses with respect to the CoD was considered by means of the entropy in a supervised feature subset selection (FSS) approach. On the other hand, we inspected the questions themselves by means of semantic similarity leading to an unsupervised approach based on semantic similarity (SFSS). In an attempt to assess, quantitatively, the impact of reducing the questionnaire, we assessed the use of these FSS approaches on the CoD predictive capability of a classifier. Experimental results showed that unsupervised semantic similarity feature subset selection (SFSS) approach was competitive to identify similar questions. Nevertheless, naturally, supervised FSS based on the entropy of the responses performed better for CoD prediction. To sum up, the necessity of reviewing the VA questionnaire was accompanied with quantitative evidence.