Kerstin Denecke


2024

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How Do Conversational Agents in Healthcare Impact on Patient Agency?
Kerstin Denecke
Proceedings of the 1st Worskhop on Towards Ethical and Inclusive Conversational AI: Language Attitudes, Linguistic Diversity, and Language Rights (TEICAI 2024)

In healthcare, agency refers to the ability of patients to actively participate in and control their health through collaborating with providers, informed decision-making and understanding health information. Conversational agents (CAs) are increasingly used for realizing digital health interventions, but it is still unclear how they are enhancing patient agency. This paper explores which technological components are required to enable CAs impacting on patient agency, and identifies metrics for measuring and evaluating this impact. We do this by drawing on existing work related to developing and evaluating healthcare CAs and through analysis of a concrete example of a CA. As a result, we identify five main areas where CAs enhance patient agency, namely by: improved access to health information, personalized advice, increased engagement, emotional support and reduced barriers to care. For each of these areas, specific technological functions have to be integrated into CAs such as sentiment and emotion analysis methods that allow a CA to support emotionally.

2010

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Cross-Corpus Textual Entailment for Sublanguage Analysis in Epidemic Intelligence
Avaré Stewart | Kerstin Denecke | Wolfgang Nejdl
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Textual entailment has been recognized as a generic task that captures major semantic inference needs across many natural language processing applications. However, to date, textual entailment has not been considered in a cross-corpus setting, nor for user generated content. Given the emergence of Medicine 2.0, medical blogs are becoming an increasingly accepted source of information. However, given the characteristics of blogs( which tend to be noisy and informal; or contain a interspersing of subjective and factual sentences) a potentially large amount of irrelevant information may be present. Given the potential noise, the overarching problem with respect to information extraction from social media is achieving the correct level of sentence filtering - as opposed to document or blog post level. Specifically for the task of medical intelligence gathering. In this paper, we propose an approach to textual entailment with uses the text from one source of user generated content (T text) for sentence-level filtering within a new and less amenable one (H text), when the underlying domain, tasks or semantic information is the same, or overlaps.