4 citations · 8 across the 5 of their papers we have counts for
5 papers
Translation of Black-Box Clinical Prediction Models into Standalone Transparent Nomograms: Temporal External Validation in Heart Transplantation
Henry Pigot, Paulo J. G. Lisboa, Sandra Ortega-Martorell +3
We convert black-box clinical prediction models for tabular data into standalone nomograms that can be audited term by term. PRiSM (Partial Responses in Structured Models) takes th…
Optimized HDBSCAN clustering for reconstructing the merger history of the Milky Way: applications and limitations
Andrea Sante, Andreea S. Font, Dharmesh Mistry +2
Clustering algorithms can help reconstruct the assembly history of the Milky Way by identifying groups of stars sharing similar properties in a kinematical or chemical abundance sp…
Applying machine learning to Galactic Archaeology: how well can we recover the origin of stars in Milky Way-like galaxies?
Andrea Sante, Andreea S. Font, Sandra Ortega-Martorell +2
We present several machine learning (ML) models developed to efficiently separate stars formed in-situ in Milky Way-type galaxies from those that were formed externally and later a…
The Partial Response Network: a neural network nomogram
Paulo J. G. Lisboa, Sandra Ortega-Martorell, Sadie Cashman +1
Among interpretable machine learning methods, the class of Generalised Additive Neural Networks (GANNs) is referred to as Self-Explaining Neural Networks (SENN) because of the line…
A Probabilistic framework for Quantum Clustering
Raúl V. Casaña-Eslava, Paulo J. G. Lisboa, Sandra Ortega-Martorell +2
Quantum Clustering is a powerful method to detect clusters in data with mixed density. However, it is very sensitive to a length parameter that is inherent to the Schrödinger equat…