Objective Computing patients’ similarity is of great interest in precision oncology since it supports clustering and subgroup identification, eventually leading to tailored therapies. The availability of large amounts of biomedical data, characterized by large feature sets and sparse content, motivates the development of new methods to compute patient similarities able to fuse heterogeneous data sources with the available knowledge. Materials and Methods In this work, we developed a data integration approach based on matrix trifactorization to compute patient similarities by integrating several sources of data and knowledge. We assess the accuracy of the proposed method: (1) on several synthetic data sets which similarity structures are affected by increasing levels of noise and data sparsity, and (2) on a real data set coming from an acute myeloid leukemia (AML) study. The results obtained are finally compared with the ones of traditional similarity calculation methods. Results In the analysis of the synthetic data set, where the ground truth is known, we measured the capability of reconstructing the correct clusters, while in the AML study we evaluated the Kaplan-Meier curves obtained with the different clusters and measured their statistical difference by means of the log-rank test. In presence of noise and sparse data, our data integration method outperform other techniques, both in the synthetic and in the AML data. Discussion In case of multiple heterogeneous data sources, a matrix trifactorization technique can successfully fuse all the information in a joint model. We demonstrated how this approach can be efficiently applied to discover meaningful patient similarities and therefore may be considered a reliable data driven strategy for the definition of new research hypothesis for precision oncology. Conclusion The better performance of the proposed approach presents an advantage over previous methods to provide accurate patient similarities supporting precision medicine.

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Lost circulation is a challenging aspect during drilling operations as uncontrolled flow of wellbore fluids into formation can expose rig personnel and environment to risks. Further, the time required to regain the circulation of drilling fluid typically results in unplanned Non-Productive Time (NPT) causing undesired amplified drilling cost. Thus, it is of primary importance to support drilling supervisors with accurate and effective detection tools for safe and economic drilling operations. In this framework, a novel lost circulation intelligent detection system is proposed which relies on the simultaneous identification of decreasing trends in the paddle mud flow-out and standpipe pressure signals, at constant mud flow-in rate. First, mud flow-out and standpipe pressure signals underlie cubic-spline-based smoothing step to remove background noise caused by the measurement instrument and the intrinsic variability of the drilling environment. To identify structural changes in the considered signals, a nonparametric kernel-based change point detection algorithm is employed. Finally, an alarm is raised if flow-out and standpipe pressure decreasing trends have been detected and their negative variations are below prefixed threshold values. The proposed intelligent lost circulation detection system has been verified with respect to historical field data recorded from several Eni wells located in different countries. Results show that the proposed system satisfactorily and reliably detects both partial and total lost circulation events. Further, its integration with already existing Eni NPT prediction models has led to a significant improvement in terms of events correctly triggered.