J Biomed Inform - Toward automated consumer question answering: automatically separating consumer questions from professional questions in the healthcare domain.


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JECTIVE: Both healthcare professionals and healthcare consumers have information needs that can be met through the use of computers, specifically via medical question answering systems. However, the information needs of both groups are different in terms of literacy levels and technical expertise, and an effective question answering system must be able to account for these differences if it is to formulate the most relevant responses for users from each group. In this paper, we propose that a first step toward answering the queries of different users is automatically classifying questions according to whether they were asked by healthcare professionals or consumers.DESIGN: We obtained two sets of consumer questions (~10,000 questions in total) from Yahoo answers. The professional questions consist of two question collections: 4654 point-of-care questions (denoted as PointCare) obtained from interviews of a group of family doctors following patient visits and 5378 questions from physician practices through professional online services (denoted as OnlinePractice). With more than 20,000 questions combined, we developed supervised machine-learning models for automatic classification between consumer questions and professional questions. To evaluate the robustness of our models, we tested the model that was trained on the Consumer-PointCare dataset on the Consumer-OnlinePractice dataset. We evaluated both linguistic features and statistical features and examined how the characteristics in two different types of professional questions (PointCare vs. OnlinePractice) may affect the classification performance. We explored information gain for feature reduction and the back-off linguistic category features.RESULTS: The 10-fold cross-validation results showed the best F1-measure of 0.936 and 0.946 on Consumer-PointCare and Consumer-OnlinePractice respectively, and the best F1-measure of 0.891 when testing the Consumer-PointCare model on the Consumer-OnlinePractice dataset.CONCLUSION: Healthcare consumer questions posted at Yahoo online communities can be reliably classified from professional questions posted by point-of-care clinicians and online physicians. The supervised machine-learning models are robust for this task. Our study will significantly benefit further development in automated consumer question answering.

Resumo Limpo

jectiv healthcar profession healthcar consum inform need can met use comput specif via medic question answer system howev inform need group differ term literaci level technic expertis effect question answer system must abl account differ formul relev respons user group paper propos first step toward answer queri differ user automat classifi question accord whether ask healthcar profession consumersdesign obtain two set consum question question total yahoo answer profession question consist two question collect pointofcar question denot pointcar obtain interview group famili doctor follow patient visit question physician practic profession onlin servic denot onlinepractic question combin develop supervis machinelearn model automat classif consum question profession question evalu robust model test model train consumerpointcar dataset consumeronlinepractic dataset evalu linguist featur statist featur examin characterist two differ type profession question pointcar vs onlinepractic may affect classif perform explor inform gain featur reduct backoff linguist categori featuresresult fold crossvalid result show best fmeasur consumerpointcar consumeronlinepractic respect best fmeasur test consumerpointcar model consumeronlinepractic datasetconclus healthcar consum question post yahoo onlin communiti can reliabl classifi profession question post pointofcar clinician onlin physician supervis machinelearn model robust task studi will signific benefit develop autom consum question answer

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