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Found 4 papers, 1 papers with code
Genetic Programming for Explainable Manifold Learning
Our experimental analysis demonstrates that GP-EMaL is able to match the performance of the existing approach in most cases, while using simpler, smaller, and more interpretable tree structures.
Explaining Genetic Programming Trees using Large Language Models
In this research, we investigate the potential of leveraging eXplainable AI (XAI) and large language models (LLMs) like ChatGPT to improve the interpretability of GP-based non-linear dimensionality reduction.
Forecasting Patient Flows with Pandemic Induced Concept Drift using Explainable Machine Learning
This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level indicators can both generally improve the forecasting models of patient flows and effectively adapt the models to the unfolding disruptions of pandemic conditions.
Forecasting Patient Demand at Urgent Care Clinics using Machine Learning
This study explores the ability of machine learning methods to generate accurate patient presentations at two large urgent care clinics located in Auckland, New Zealand.
