Artificial Intelligence is providing astonishing results, with medicine being one of its fa-vourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new require-ments. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifi-cally, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely consid-ered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future.

Banegas-Luna, A.j., Pena-Garcia, J., Iftene, A., Guadagni, F., Ferroni, P., Scarpato, N., et al. (2021). Towards the interpretability of machine learning predictions for medical applications targeting personalised therapies: A cancer case survey. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 22(9) [10.3390/ijms22094394].

Towards the interpretability of machine learning predictions for medical applications targeting personalised therapies: A cancer case survey

Zanzotto F. M.;
2021-01-01

Abstract

Artificial Intelligence is providing astonishing results, with medicine being one of its fa-vourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new require-ments. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifi-cally, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely consid-ered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future.
2021
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore INF/01 - INFORMATICA
Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
English
Cancer treatment
Deep learning
Drug repurposing
High performance computing
Machine learning
Personalised therapy
Antineoplastic Agents
Humans
Neoplasm Proteins
Neoplasms
Neural Networks, Computer
Machine Learning
Molecular Targeted Therapy
Precision Medicine
Banegas-Luna, A.j., Pena-Garcia, J., Iftene, A., Guadagni, F., Ferroni, P., Scarpato, N., et al. (2021). Towards the interpretability of machine learning predictions for medical applications targeting personalised therapies: A cancer case survey. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 22(9) [10.3390/ijms22094394].
Banegas-Luna, Aj; Pena-Garcia, J; Iftene, A; Guadagni, F; Ferroni, P; Scarpato, N; Zanzotto, Fm; Bueno-Crespo, A; Perez-Sanchez, H
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/298911
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