Papers by Thomas Schaaf
Effective Convolutional Attention Network for Multi-label Clinical Document Classification (2021.emnlp-main)
Copied to clipboard
| Challenge: | a large number of medical encounters need to be coded everyday due to long document sets and large label set. |
| Approach: | They propose a convolutional attention network for multi-label document classification problem . they use convolution-based encoders and convolution networks to aggregate information across documents . |
| Outcome: | The proposed model outperforms prior best model and multilingual Transformer model on a widely used dataset in the medical domain. |
Annotate the Way You Think: An Incremental Note Generation Framework for the Summarization of Medical Conversations (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing datasets for summarization of medical conversations are limited to conversation-summary pairs . a novel annotation framework is proposed to capture the summarizing process via an annotation task . |
| Approach: | They propose an incremental note generation framework that captures the human summarization process via an annotation task by instructing annotators to first incrementally create a draft note and polish it into a reference note. |
| Outcome: | The proposed framework shows that the human summarization process is much more efficient and accurate than the current method. |
Comparing Two Model Designs for Clinical Note Generation; Is an LLM a Useful Evaluator of Consistency? (2024.findings-naacl)
Copied to clipboard
| Challenge: | a clinical note is a document that documents a doctor's interaction with a patient . authors show that LLMs can be used to measure quality indicators . |
| Approach: | They analyze two different approaches to generate different sections of a SOAP note . they use PEGASUS-X Transformer models to examine note consistency . |
| Outcome: | The proposed approach leads to similar ROUGE values and no difference in Factuality metric . human reviewers perform the same tasks with roughly the same agreement as the LLMs . |
Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations (2021.findings-emnlp)
Copied to clipboard
Longxiang Zhang, Renato Negrinho, Arindam Ghosh, Vasudevan Jagannathan, Hamid Reza Hassanzadeh, Thomas Schaaf, Matthew R. Gormley
| Challenge: | Using pretrained transformer models for automatically summarizing doctor-patient conversations presents challenges . limited training data, domain shift, long and noisy transcripts, and high target summary variability are challenges compared to human annotators. |
| Approach: | They propose a method for fine-tuning pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. |
| Outcome: | The proposed method surpasses the performance of an average human annotator and the quality of previous published work for the task. |
Posterior Calibrated Training on Sentence Classification Tasks (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for posterior calibration have been used to correct poorly calibrated posterior probabilities. |
| Approach: | They propose a posterior calibration procedure that optimizes posterior probability distributions while minimizing calibration errors. |
| Outcome: | The proposed procedure reduces calibration error and improves performance on both objectives. |