Automatic processing of spoken dialogue in the home hemodialysis domain.
Spoken medical dialogue is a valuable source of information, and it forms a foundation for diagnosis, prevention and therapeutic management. However, understanding even a perfect transcript of spoken dialogue is challenging for humans because of the lack of structure and the verbosity of dialogues. This work presents a first step towards automatic analysis of spoken medical dialogue. The backbone of our approach is an abstraction of a dialogue into a sequence of semantic categories. This abstraction uncovers structure in informal, verbose conversation between a caregiver and a patient, thereby facilitating automatic processing of dialogue content. Our method induces this structure based on a range of linguistic and contextual features that are integrated in a supervised machine-learning framework. Our model has a classification accuracy of 73%, compared to 33% achieved by a majority baseline (p<0.01). This work demonstrates the feasibility of automatically processing spoken medical dialogue.
Lacson
Ronilda
R
Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.
Barzilay
Regina
R
D000465:Algorithms; D001185:Artificial Intelligence; D006437:Hemodialysis, Home; D006801:Humans; D008499:Medical Records; D009323:Natural Language Processing; D012660:Semantics; D013060:Speech; D009626:Terminology as Topic