Weakly Supervised Medication Regimen Extraction from Medical Conversations

Dhruvesh Patel, Sandeep Konam, Sai Prabhakar


Abstract
Automated Medication Regimen (MR) extraction from medical conversations can not only improve recall and help patients follow through with their care plan, but also reduce the documentation burden for doctors. In this paper, we focus on extracting spans for frequency, route and change, corresponding to medications discussed in the conversation. We first describe a unique dataset of annotated doctor-patient conversations and then present a weakly supervised model architecture that can perform span extraction using noisy classification data. The model utilizes an attention bottleneck inside a classification model to perform the extraction. We experiment with several variants of attention scoring and projection functions and propose a novel transformer-based attention scoring function (TAScore). The proposed combination of TAScore and Fusedmax projection achieves a 10 point increase in Longest Common Substring F1 compared to the baseline of additive scoring plus softmax projection.
Anthology ID:
2020.clinicalnlp-1.20
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
178–193
Language:
URL:
https://aclanthology.org/2020.clinicalnlp-1.20
DOI:
10.18653/v1/2020.clinicalnlp-1.20
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.clinicalnlp-1.20.pdf
Video:
 https://slideslive.com/38939826