[PDF][PDF] Better metrics for evaluating explainable artificial intelligence
A Rosenfeld - Proceedings of the 20th international conference …, 2021 - researchgate.net
This paper presents objective metrics for how explainable artificial intelligence (XAI) can be
quantified. Through an overview of current trends, we show that many explanations are …
quantified. Through an overview of current trends, we show that many explanations are …
Machine learning in patient flow: a review
This work is a review of the ways in which machine learning has been used in order to plan,
improve or aid the problem of moving patients through healthcare services. We decompose …
improve or aid the problem of moving patients through healthcare services. We decompose …
AI for explaining decisions in multi-agent environments
Explanation is necessary for humans to understand and accept decisions made by an AI
system when the system's goal is known. It is even more important when the AI system …
system when the system's goal is known. It is even more important when the AI system …
Detecting human bias in emergency triage using LLMs: Literature review, preliminary study, and experimental plan
M Avalos, D Cohen, D Russon, M Davids… - The International …, 2024 - journals.flvc.org
Detecting Human Bias in Emergency Triage Using LLMs: Literature Review, Preliminary Study,
and Experimental Plan Page 1 Detecting Human Bias in Emergency Triage Using LLMs …
and Experimental Plan Page 1 Detecting Human Bias in Emergency Triage Using LLMs …
Variation of in-hospital trauma team staffing: new resuscitation, new team
OEC van Maarseveen, RLN Huijsmans… - BMC Emergency …, 2022 - Springer
Background Non-technical errors, such as insufficient communication or leadership, are a
major cause of medical failures during trauma resuscitation. Research on staffing variation …
major cause of medical failures during trauma resuscitation. Research on staffing variation …
[PDF][PDF] Explicit Gradient Learning for Black-Box Optimization.
Abstract Black-Box Optimization (BBO) methods can find optimal policies for systems that
interact with complex environments with no analytical representation. As such, they are of …
interact with complex environments with no analytical representation. As such, they are of …
Fast and Interpretable Mixed-Integer Linear Program Solving by Learning Model Reduction
By exploiting the correlation between the structure and the solution of Mixed-Integer Linear
Programming (MILP), Machine Learning (ML) has become a promising method for solving …
Programming (MILP), Machine Learning (ML) has become a promising method for solving …
A unified framework for differentiated services in intelligent healthcare systems
The Coronavirus disease 2019 (COVID-19) outbreak continues to significantly expose the
vulnerabilities of healthcare systems around the world. These unprecedented circumstances …
vulnerabilities of healthcare systems around the world. These unprecedented circumstances …
Uncovering Judgment Biases in Emergency Triage: A Public Health Approach Based on Large Language Models
A Guerra-Adames, M Avalos, O Dorémus… - … for Machine Learning …, 2024 - hal.science
Judgment biases in emergency triage can adversely affect patient outcomes. This study
examines sex/gender biases using four advanced language models fine-tuned on real …
examines sex/gender biases using four advanced language models fine-tuned on real …
LBA: Online Learning-Based Assignment of Patients to Medical Professionals
Central to any medical domain is the challenging patient to medical professional assignment
task, aimed at getting the right patient to the right medical professional at the right time. This …
task, aimed at getting the right patient to the right medical professional at the right time. This …