Andrea Campagner's Projects
Elementi di Bioinformatica: 2014-15
:triangular_ruler: Jekyll theme for building a personal site, blog, project documentation, or portfolio.
This is the GitHub for the Theory module of the Decision Support Systems course
Code Repository for the paper "Cabitza, F., Campagner, A., Ferrari, D., Di Resta, C., Ceriotti, D., Sabetta, E., Colombini, A., De Vecchi, E., Banfi, G., Locatelli, M. & Carobene, A. (2021). Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests"
Supporting data for publication: Comput Biol Med. 2020 Jun;121:103796. doi: 10.1016/j.compbiomed.2020.103796. Epub 2020 May 16. Assessment and prediction of spine surgery invasiveness with machine learning techniques Andrea Campagner, Pedro Berjano, Claudio Lamartina, Francesco Langella, Giovanni Lombardi, Federico Cabitza PMID: 32568677 DOI: 10.1016/j.compbiomed.2020.103796
Supporting data for the paper "Observing the Kasparov’s law in Human-AI collaboration protocols: the case of medical double reading"
OGSA-PROMs-2023: this is the supporting code for generating and validation the collection of musculoskeletal disorders' public open datasets of patient-reported outcome measures (PROMs)
This a repository collecting all metrics, algorithms and pieces of code related to data and model quality for Machine Learning, developed by me and others at the MUDI lab (https://www.mudilab.net/mudi/) of the DISCo dept. @ University of Milano-Bicocca
Simple implementation of a Stochastic Ray Tracer
A python library for cautious learning inspired by scikit-weak
A library featuring utilities and algorithms for weakly supervised ML
Fast, portable C implementations of Needleman-Wunsch and Smith-Waterman sequence alignment
GitHub for the softpy library: a Python library for soft computing, currently focused on Fuzzy Systems and Evolutionary Computing
Implementation of surprisingly popular algorithm through machine learning ensemble methods