5 citations · 16 across the 10 of their papers we have counts for
11 papers
Clinical Concept Centers in LLMs
Aishik Nagar, Abhishek Vaidyanathan, Arun-Kumar Kaliya-Perumal +2
Large language models are increasingly used in clinical settings. However, research into the reliability and performance of these models has focused almost entirely on the language…
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?
Neeladri Bhuiya, Viktor Schlegel, Stefan Winkler
State-of-the-art Large Language Models (LLMs) are accredited with an increasing number of different capabilities, ranging from reading comprehension, over advanced mathematical and…
Representation Learning of Structured Data for Medical Foundation Models
Vijay Prakash Dwivedi, Viktor Schlegel, Andy T. Liu +6
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effectively represent structured non-t…
Efficient Training of Self-Supervised Speech Foundation Models on a Compute Budget
Andy T. Liu, Yi-Cheng Lin, Haibin Wu +2
Despite their impressive success, training foundation models remains computationally costly. This paper investigates how to efficiently train speech foundation models with self-sup…
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues
Kuluhan Binici, Abhinav Ramesh Kashyap, Viktor Schlegel +6
Automatic Speech Recognition (ASR) systems are pivotal in transcribing speech into text, yet the errors they introduce can significantly degrade the performance of downstream tasks…
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
Aishik Nagar, Viktor Schlegel, Thanh-Tung Nguyen +4
Large Language Models (LLMs) are increasingly adopted for applications in healthcare, reaching the performance of domain experts on tasks such as question answering and document su…
M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering
Anand Subramanian, Viktor Schlegel, Abhinav Ramesh Kashyap +3
There is vivid research on adapting Large Language Models (LLMs) to perform a variety of tasks in high-stakes domains such as healthcare. Despite their popularity, there is a lack…
PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records
Viktor Schlegel, Hao Li, Yuping Wu +8
This paper describes PULSAR, our system submission at the ImageClef 2023 MediQA-Sum task on summarising patient-doctor dialogues into clinical records. The proposed framework relie…
PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models
Hao Li, Yuping Wu, Viktor Schlegel +7
Medical progress notes play a crucial role in documenting a patient's hospital journey, including his or her condition, treatment plan, and any updates for healthcare providers. Au…
A Two-Stage Decoder for Efficient ICD Coding
Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Kashyap +1
Clinical notes in healthcare facilities are tagged with the International Classification of Diseases (ICD) code; a list of classification codes for medical diagnoses and procedures…
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification
Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Kashyap +4
Clinical notes are assigned ICD codes - sets of codes for diagnoses and procedures. In the recent years, predictive machine learning models have been built for automatic ICD coding…