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pro vyhledávání: '"Joyce Dan"'
Mental health in children and adolescents has been steadily deteriorating over the past few years. The recent advent of Large Language Models (LLMs) offers much hope for cost and time efficient scaling of monitoring and intervention, yet despite spec
Externí odkaz:
http://arxiv.org/abs/2404.16461
Pre-trained Large Language Models (LLMs) often struggle on out-of-domain datasets like healthcare focused text. We explore specialized pre-training to adapt smaller LLMs to different healthcare datasets. Three methods are assessed: traditional masked
Externí odkaz:
http://arxiv.org/abs/2403.19802
Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) -- a particularly important use-case for mental health where a majority of routine
Externí odkaz:
http://arxiv.org/abs/2403.19790
Adapting language models (LMs) to novel domains is often achieved through fine-tuning a pre-trained LM (PLM) on domain-specific data. Fine-tuning introduces new knowledge into an LM, enabling it to comprehend and efficiently perform a target domain t
Externí odkaz:
http://arxiv.org/abs/2403.18025
Autor:
Lorge, Isabelle, Joyce, Dan W., Taylor, Niall, Nevado-Holgado, Alejo, Cipriani, Andrea, Kormilitzin, Andrey
Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where despite treatment, they continue to experience significant burden. We sought to develop a Large L
Externí odkaz:
http://arxiv.org/abs/2402.07645
Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown impressive performance on a number of natural language tasks with common benchmarking text datasets in full, few-shot, and zero-shot train-evaluation set
Externí odkaz:
http://arxiv.org/abs/2205.05535
Autor:
Taylor, Niall, Kormilitzin, Andrey, Lorge, Isabelle, Nevado-Holgado, Alejo, Cipriani, Andrea, Joyce, Dan W.
Publikováno v:
In Artificial Intelligence In Medicine November 2024 157
Autor:
Taylor, Niall, Sha, Lei, Joyce, Dan W, Lukasiewicz, Thomas, Nevado-Holgado, Alejo, Kormilitzin, Andrey
The development of neural networks for clinical artificial intelligence (AI) is reliant on interpretability, transparency, and performance. The need to delve into the black-box neural network and derive interpretable explanations of model output is p
Externí odkaz:
http://arxiv.org/abs/2111.07611
Publikováno v:
Annals of General Psychiatry, Vol 5, Iss Suppl 1, p S87 (2006)
Externí odkaz:
https://doaj.org/article/cb7c017311684efa92097b397aea7d55
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