Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients. Arsela Prelaj , Edoardo Gregorio Galli , Vanja Miskovic , Mattia Pesenti , Giuseppe Viscardi , Benedetta Pedica , Laura Mazzeo , Achille Bottiglieri , Leonardo Provenzano , Andrea Spagnoletti , Roberto Marinacci , Alessandro De Toma , Claudia Proto , Roberto Ferrara , Marta Brambilla , Mario Occhipinti , Sara Manglaviti , Giulia Galli , Diego Signorelli , Claudia Giani , Teresa Beninato , Chiara Carlotta Pircher , Alessandro Rametta , Sokol Kosta , Michele Zanitti , Maria Rosa Di Mauro , Arturo Rinaldi , Settimio Di Gregorio , Martinetti Antonia , Marina Chiara Garassino , Filippo G. M. de Braud , Marcello Restelli , Giuseppe Lo Russo , Monica Ganzinelli , Francesco Trovò , Alessandra Laura Giulia Pedrocchi Frontiers in oncology（2022）
摘要
In this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.
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关键词
explainable artificial intelligence, immunotherapy, machine learning, non-small cell lung cancer, treatment
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