Please note: This PhD seminar will be given online.
Georgios Michalopoulos, PhD candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Helen Chen and Ian McKillop
In this study, we investigated the key characteristics exhibited by questions in medical narrative data (e.g., review logs) in order to uncover the most impactful variables on data quality issues during the data entry and review process of a real-word clinical system.
More specifically, we evaluated the performance of traditional rule-based and learning-based methods for detecting question sentences. Next, we proposed a novel multi-channel deep convolutional neural network architecture designed for the purpose of separating real questions that expect an answer (information or help) about an issue from sentences that are not questions, as well as from questions referring to an issue mentioned in a nearby sentence (e.g., can you clarify this?), which we will refer as “c-questions”. Finally, we performed a comprehensive performance comparison analysis of the proposed multi-channel deep convolutional neural network against other deep neural networks. We illustrated the efficacy of the proposed network which achieved the best F1 score on a benchmark dataset of medical logs about renal patients and on a general domain dataset.
To join this PhD seminar on Zoom, please go to https://us02web.zoom.us/j/87677364079?pwd=eGdXWXRKbHRlbXI1ODN3QzRxbXVDUT09.
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