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pro vyhledávání: '"Lalor, John"'
Previous studies reveal that Electronic Health Records (EHR), which have been widely adopted in the U.S. to allow patients to access their personal medical information, do not have high readability to patients due to the prevalence of medical jargon.
Externí odkaz:
http://arxiv.org/abs/2408.05555
Directly learning from examples of random difficulty levels is often challenging for both humans and machine learning models. A more effective strategy involves exposing learners to examples in a progressive order, from easy to difficult. Curriculum
Externí odkaz:
http://arxiv.org/abs/2408.05326
Publikováno v:
Journal of Medical Internet Research, Vol 23, Iss 5, p e26354 (2021)
BackgroundInterventions to define medical jargon have been shown to improve electronic health record (EHR) note comprehension among crowdsourced participants on Amazon Mechanical Turk (AMT). However, AMT participants may not be representative of the
Externí odkaz:
https://doaj.org/article/d24a41850e2541f985125c2f576f2224
Autor:
Safadi, Hani hanisaf@uga.edu, Lalor, John P. jlalor1@nd.edu, Berente, Nicholas nberente@nd.edu
Publikováno v:
MIS Quarterly. Sep2024, Vol. 48 Issue 3, p1279-1295. 17p. 3 Diagrams, 6 Charts, 2 Graphs.
Autor:
Duan, Xiaojing, Lalor, John P.
With the rapid advancement of machine learning models for NLP tasks, collecting high-fidelity labels from AI models is a realistic possibility. Firms now make AI available to customers via predictions as a service (PaaS). This includes PaaS products
Externí odkaz:
http://arxiv.org/abs/2311.11981
Transformer-based pretrained large language models (PLM) such as BERT and GPT have achieved remarkable success in NLP tasks. However, PLMs are prone to encoding stereotypical biases. Although a burgeoning literature has emerged on stereotypical bias
Externí odkaz:
http://arxiv.org/abs/2311.10395
Sentiment analysis is integral to understanding the voice of the customer and informing businesses' strategic decisions. Conventional sentiment analysis involves three separate tasks: aspect-category detection (ACD), aspect-category sentiment analysi
Externí odkaz:
http://arxiv.org/abs/2305.01710
Autor:
Lalor, John P., Guo, Hong
Algorithmic interpretability is necessary to build trust, ensure fairness, and track accountability. However, there is no existing formal measurement method for algorithmic interpretability. In this work, we build upon programming language theory and
Externí odkaz:
http://arxiv.org/abs/2205.10207
Autor:
Lalor, John P., Rodriguez, Pedro
py-irt is a Python library for fitting Bayesian Item Response Theory (IRT) models. py-irt estimates latent traits of subjects and items, making it appropriate for use in IRT tasks as well as ideal-point models. py-irt is built on top of the Pyro and
Externí odkaz:
http://arxiv.org/abs/2203.01282