# Weakly Supervised Medication Regimen Extraction from Medical Conversations

arXiv: https://arxiv.org/abs/2010.05317 · HTML: https://mlsanity.com/paper/2010.05317/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Making Document-Level Information Extraction Right for the Right Reasons](https://mlsanity.com/paper/2110.07686/) (2021)
- [ASR Error Detection via Audio-Transcript entailment](https://mlsanity.com/paper/2207.10849/) (2022)
- [Anterior's Approach to Fairness Evaluation of Automated Prior Authorization System](https://mlsanity.com/paper/2603.14631/) (2026)

## Abstract (arXiv)

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.