Enhanced survival prediction using explainable artificial intelligence in heart transplantation.

The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance.

Access control and privilege management in electronic health record: a systematic literature review.

This study presents a systematic literature review of access control for electronic health record systems to protect patient's privacy. Articles from 2006 to 2016 were extracted from the ACM Digital Library, IEEE Xplore Digital Library, Science Direct, MEDLINE, and MetaPress using broad eligibility criteria, and chosen for inclusion based on analysis of ISO22600. Cryptographic standards and methods were left outside the scope of this review. Three broad classes of models are being actively investigated and developed: access control for electronic health records, access control for interoperability, and access control for risk analysis. Traditional role-based access control models are extended with spatial, temporal, probabilistic, dynamic, and semantic aspects to capture contextual information and provide granular access control. Maintenance of audit trails and facilities for overriding normal roles to allow full access in emergency cases are common features. Access privilege frameworks utilizing ontology-based knowledge representation for defining the rules have attracted considerable interest, due to the higher level of abstraction that makes it possible to model domain knowledge and validate access requests efficiently.