Artificial intelligence supporting cancer patients across Europe-The ASCAPE project.

INTRODUCTION: Breast and prostate cancer survivors can experience impaired quality of life (QoL) in several QoL domains. The current strategy to support cancer survivors with impaired QoL is suboptimal, leading to unmet patient needs. ASCAPE aims to provide personalized- and artificial intelligence (AI)-based predictions for QoL issues in breast- and prostate cancer patients as well as to suggest potential interventions to their physicians to offer a more modern and holistic approach on cancer rehabilitation. METHODS AND ANALYSES: An AI-based platform aiming to predict QoL issues and suggest appropriate interventions to clinicians will be built based on patient data gathered through medical records, questionnaires, apps, and wearables. This platform will be prospectively evaluated through a longitudinal study where breast and prostate cancer survivors from four different study sites across the Europe will be enrolled. The evaluation of the AI-based follow-up strategy through the ASCAPE platform will be based on patients' experience, engagement, and potential improvement in QoL during the study as well as on clinicians' view on how ASCAPE platform impacts their clinical practice and doctor-patient relationship, and their experience in using the platform. ETHICS AND DISSEMINATION: ASCAPE is the first research project that will prospectively investigate an AI-based approach for an individualized follow-up strategy for patients with breast- or prostate cancer focusing on patients' QoL issues. ASCAPE represents a paradigm shift both in terms of a more individualized approach for follow-up based on QoL issues, which is an unmet need for cancer survivors, and in terms of how to use Big Data in cancer care through democratizing the knowledge and the access to AI and Big Data related innovations. TRIAL REGISTRATION: Trial Registration on clinicaltrials.gov: NCT04879563.

Applying Deep Neural Networks over Homomorphic Encrypted Medical Data.

In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regulations on the confidentiality of patient health information have in many cases hindered the adoption of deep learning-based solutions in clinical workflows. To allow for the processing of sensitive health information without disclosing the underlying data, we propose a solution based on fully homomorphic encryption (FHE). The considered encryption scheme, MORE (Matrix Operation for Randomization or Encryption), enables the computations within a neural network model to be directly performed on floating point data with a relatively small computational overhead. We consider the well-known MNIST digit recognition problem to evaluate the feasibility of the proposed method and show that performance does not decrease when deep learning is applied on MORE homomorphic data. To further evaluate the suitability of the method for healthcare applications, we first train a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) hemodynamic model and then provide a solution for classifying encrypted X-ray coronary angiography medical images. The findings highlight the potential of the proposed privacy-preserving deep learning methods to outperform existing approaches by providing, within a reasonable amount of time, results equivalent to those achieved by unencrypted models. Lastly, we discuss the security implications of the encryption scheme and show that while the considered cryptosystem promotes efficiency and utility at a lower security level, it is still applicable in certain practical use cases.

CrowdHEALTH: Big Data Analytics and Holistic Health Records.

The aim of this paper is to present examples of big data techniques that can be applied on Holistic Health Records (HHR) in the context of the CrowdHEALTH project. Real-time big data analytics can be performed on the stored data (i.e. HHRs) enabling correlations and extraction of situational factors between laboratory exams, physical activities, biosignals, medical data patterns, and clinical assessment. Based on the outcomes of different analytics (e.g. risk analysis, pathways mining, forecasting and causal analysis) on the aforementioned HHRs datasets, actionable information can be obtained for the development of efficient health plans and public health policies.

Privacy-Preserving Artificial Intelligence: Application to Precision Medicine.

Motivated by state-of-the-art performances across a wide variety of areas, over the last few years Machine Learning has drawn a significant amount of attention from the healthcare domain. Despite their potential in enabling person-alized medicine applications, the adoption of Deep Learning based solutions in clinical workflows has been hindered in many cases by the strict regulations concerning the privacy of patient health data. We propose a solution that relies on Fully Homomorphic Encryption, particularly on the MORE scheme, as a mechanism for enabling computations on sensitive health data, without revealing the underlying data. The chosen variant of the encryption scheme allows for the computations in the Neural Network model to be directly performed on floating point numbers, while incurring a reasonably small computational overhead. For feasibility evaluation, we demonstrate on the MNIST digit recognition task that Deep Learning can be performed on encrypted data without compromising the accuracy. We then address a more complex task by training a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) model. These results underline the potential of the proposed approach to outperform current solutions by delivering comparable results to the unencrypted Deep Learning based solutions, in a reasonable amount of time. Lastly, the security aspects of the encryption scheme are analyzed, and we show that, even though the chosen encryption scheme favors performance and utility at the cost of weaker security, it can still be used in certain practical applications.