Artificial intelligence in action: addressing the COVID-19 pandemic with natural language processing
The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on
society, both because of the serious health effects of COVID-19 and because of public …
society, both because of the serious health effects of COVID-19 and because of public …
Artificial intelligence in critical illness and its impact on patient care: a comprehensive review
M Saqib, M Iftikhar, F Neha, F Karishma… - Frontiers in …, 2023 - frontiersin.org
Artificial intelligence (AI) has great potential to improve the field of critical care and enhance
patient outcomes. This paper provides an overview of current and future applications of AI in …
patient outcomes. This paper provides an overview of current and future applications of AI in …
WRENCH: A comprehensive benchmark for weak supervision
Recent Weak Supervision (WS) approaches have had widespread success in easing the
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …
Bigbio: A framework for data-centric biomedical natural language processing
Training and evaluating language models increasingly requires the construction of meta-
datasets--diverse collections of curated data with clear provenance. Natural language …
datasets--diverse collections of curated data with clear provenance. Natural language …
Towards a universal privacy model for electronic health record systems: an ontology and machine learning approach
This paper proposed a novel privacy model for Electronic Health Records (EHR) systems
utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It …
utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It …
Language models in the loop: Incorporating prompting into weak supervision
We propose a new strategy for applying large pre-trained language models to novel tasks
when labeled training data is limited. Rather than apply the model in a typical zero-shot or …
when labeled training data is limited. Rather than apply the model in a typical zero-shot or …
Cohort design and natural language processing to reduce bias in electronic health records research
Electronic health record (EHR) datasets are statistically powerful but are subject to
ascertainment bias and missingness. Using the Mass General Brigham multi-institutional …
ascertainment bias and missingness. Using the Mass General Brigham multi-institutional …
The Stanford Medicine data science ecosystem for clinical and translational research
A Callahan, E Ashley, S Datta, P Desai, TA Ferris… - JAMIA …, 2023 - academic.oup.com
Objective To describe the infrastructure, tools, and services developed at Stanford Medicine
to maintain its data science ecosystem and research patient data repository for clinical and …
to maintain its data science ecosystem and research patient data repository for clinical and …
Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data
Objectives As the real-world electronic health record (EHR) data continue to grow
exponentially, novel methodologies involving artificial intelligence (AI) are becoming …
exponentially, novel methodologies involving artificial intelligence (AI) are becoming …
SUSIE: Pharmaceutical CMC ontology-based information extraction for drug development using machine learning
V Mann, S Viswanath, S Vaidyaraman… - Computers & Chemical …, 2023 - Elsevier
Automatically extracting information from unstructured text in pharmaceutical documents is
important for drug discovery and development. This information can be integrated with …
important for drug discovery and development. This information can be integrated with …