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SIBILA: High-performance computing and interpretable machine learning join efforts toward personalised medicine in a novel decision-making tool

2022-05-12 17:23:24
Antonio Jesús Banegas-Luna, Horacio Pérez-Sánchez


Background and Objectives: Personalised medicine remains a major challenge for scientists. The rapid growth of Machine learning and Deep learning has made it a feasible alternative for predicting the most appropriate therapy for individual patients. However, the lack of interpretation of their results and high computational requirements make many reluctant to use these methods. Methods: Several Machine learning and Deep learning models have been implemented into a single software tool, SIBILA. Once the models are trained, SIBILA applies a range of interpretability methods to identify the input features that each model considered the most important to predict. In addition, all the features obtained are put in common to estimate the global attribution of each variable to the predictions. To facilitate its use by non-experts, SIBILA is also available to all users free of charge as a web server at this https URL. Results: SIBILA has been applied to three case studies to show its accuracy and efficiency in classification and regression problems. The first two cases proved that SIBILA can make accurate predictions even on uncleaned datasets. The last case demonstrates that SIBILA can be applied to medical contexts with real data. Conclusion: With the aim of becoming a powerful decision-making tool for clinicians, SIBILA has been developed. SIBILA is a novel software tool that leverages interpretable machine learning to make accurate predictions and explain how models made those decisions. SIBILA can be run on high-performance computing platforms, drastically reducing computing times.

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3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Drone Dynamic_Memory_Network Edge_Detection Embedding Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot