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  • × author_ss:"Goharian, N."
  • × author_ss:"Yates, A."
  1. Soldaini, L.; Yates, A.; Goharian, N.: Learning to reformulate long queries for clinical decision support (2017) 0.00 
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    Abstract
    The large volume of biomedical literature poses a serious problem for medical professionals, who are often struggling to keep current with it. At the same time, many health providers consider knowledge of the latest literature in their field a key component for successful clinical practice. In this work, we introduce two systems designed to help retrieving medical literature. Both receive a long, discursive clinical note as input query, and return highly relevant literature that could be used in support of clinical practice. The first system is an improved version of a method previously proposed by the authors; it combines pseudo relevance feedback and a domain-specific term filter to reformulate the query. The second is an approach that uses a deep neural network to reformulate a clinical note. Both approaches were evaluated on the 2014 and 2015 TREC CDS datasets; in our tests, they outperform the previously proposed method by up to 28% in inferred NDCG; furthermore, they are competitive with the state of the art, achieving up to 8% improvement in inferred NDCG.
    Type
    a
  2. Cohan, A.; Young, S.; Yates, A.; Goharian, N.: Triaging content severity in online mental health forums (2017) 0.00 
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    Abstract
    In recent years, social media has become a significant resource for improving healthcare and mental health. Mental health forums are online communities where people express their issues, and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential self-harm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We propose an approach for triaging user content into four severity categories that are defined based on an indication of self-harm ideation. Our models are based on a feature-rich classification framework, which includes lexical, psycholinguistic, contextual, and topic modeling features. Our approaches improve over the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Furthermore, using our proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.
    Type
    a

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