Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Kashyap, Abhinav"'
Autor:
Dwivedi, Vijay Prakash, Schlegel, Viktor, Liu, Andy T., Nguyen, Thanh-Tung, Kashyap, Abhinav Ramesh, Wei, Jeng, Yin, Wei-Hsian, Winkler, Stefan, Tan, Robby T.
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effectively represent structured non-textual data, such as the alphanumeric medical codes used in records li
Externí odkaz:
http://arxiv.org/abs/2410.13351
Autor:
Binici, Kuluhan, Kashyap, Abhinav Ramesh, Schlegel, Viktor, Liu, Andy T., Dwivedi, Vijay Prakash, Nguyen, Thanh-Tung, Gao, Xiaoxue, Chen, Nancy F., Winkler, Stefan
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 like summarization. This issue is particularly pronounced in clinical
Externí odkaz:
http://arxiv.org/abs/2408.14418
Autor:
Subramanian, Anand, Schlegel, Viktor, Kashyap, Abhinav Ramesh, Nguyen, Thanh-Tung, Dwivedi, Vijay Prakash, Winkler, Stefan
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 of understanding of the extent and contributing factors that allow LLM
Externí odkaz:
http://arxiv.org/abs/2406.03699
Autor:
Schlegel, Viktor, Kashyap, Abhinav Ramesh, Nguyen, Thanh-Tung, Yang, Tsung-Han, Dwivedi, Vijay Prakash, Yin, Wei-Hsian, Wei, Jeng, Winkler, Stefan
Computerised clinical coding approaches aim to automate the process of assigning a set of codes to medical records. While there is active research pushing the state of the art on clinical coding for hospitalized patients, the outpatient setting -- wh
Externí odkaz:
http://arxiv.org/abs/2312.13533
Autor:
Schlegel, Viktor, Li, Hao, Wu, Yuping, Subramanian, Anand, Nguyen, Thanh-Tung, Kashyap, Abhinav Ramesh, Beck, Daniel, Zeng, Xiaojun, Batista-Navarro, Riza Theresa, Winkler, Stefan, Nenadic, Goran
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 relies on domain-specific pre-training, to produce a specialised language m
Externí odkaz:
http://arxiv.org/abs/2307.02006
Autor:
Li, Hao, Wu, Yuping, Schlegel, Viktor, Batista-Navarro, Riza, Nguyen, Thanh-Tung, Kashyap, Abhinav Ramesh, Zeng, Xiaojun, Beck, Daniel, Winkler, Stefan, Nenadic, Goran
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. Automatic summarisation of a patient's problems in the form of a problem
Externí odkaz:
http://arxiv.org/abs/2306.02754
There are significant challenges for speaker adaptation in text-to-speech for languages that are not widely spoken or for speakers with accents or dialects that are not well-represented in the training data. To address this issue, we propose the use
Externí odkaz:
http://arxiv.org/abs/2305.18028
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. ICD coding is a challenging multilabel text classification problem d
Externí odkaz:
http://arxiv.org/abs/2306.00005
Autor:
Kashyap, Abhinav Ramesh, Nguyen, Thanh-Tung, Schlegel, Viktor, Winkler, Stefan, Ng, See-Kiong, Poria, Soujanya
Sentence representations are a critical component in NLP applications such as retrieval, question answering, and text classification. They capture the meaning of a sentence, enabling machines to understand and reason over human language. In recent ye
Externí odkaz:
http://arxiv.org/abs/2305.12641
Autor:
Nguyen, Thanh-Tung, Schlegel, Viktor, Kashyap, Abhinav, Winkler, Stefan, Huang, Shao-Syuan, Liu, Jie-Jyun, Lin, Chih-Jen
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. However, there is a lack of widely accepted benchmarks for automated
Externí odkaz:
http://arxiv.org/abs/2304.13998