Multi-class boosting for the analysis of multiple incomplete views on microbiome data.

BACKGROUND: Microbiome dysbiosis has recently been associated with different diseases and disorders. In this context, machine learning (ML) approaches can be useful either to identify new patterns or learn predictive models. However, data to be fed to ML methods can be subject to different sampling, sequencing and preprocessing techniques. Each different choice in the pipeline can lead to a different view (i.e., feature set) of the same individuals, that classical (single-view) ML approaches may fail to simultaneously consider. Moreover, some views may be incomplete, i.e., some individuals may be missing in some views, possibly due to the absence of some measurements or to the fact that some features are not available/applicable for all the individuals. Multi-view learning methods can represent a possible solution to consider multiple feature sets for the same individuals, but most existing multi-view learning methods are limited to binary classification tasks or cannot work with incomplete views. RESULTS: We propose irBoost.SH, an extension of the multi-view boosting algorithm rBoost.SH, based on multi-armed bandits. irBoost.SH solves multi-class classification tasks and can analyze incomplete views. At each iteration, it identifies one winning view using adversarial multi-armed bandits and uses its predictions to update a shared instance weight distribution in a learning process based on boosting. In our experiments, performed on 5 multi-view microbiome datasets, the model learned by irBoost.SH always outperforms the best model learned from a single view, its closest competitor rBoost.SH, and the model learned by a multi-view approach based on feature concatenation, reaching an improvement of 11.8% of the F1-score in the prediction of the Autism Spectrum disorder and of 114% in the prediction of the Colorectal Cancer disease. CONCLUSIONS: The proposed method irBoost.SH exhibited outstanding performances in our experiments, also compared to competitor approaches. The obtained results confirm that irBoost.SH can fruitfully be adopted for the analysis of microbiome data, due to its capability to simultaneously exploit multiple feature sets obtained through different sequencing and preprocessing pipelines.

Late presentation of traumatic tricuspid valve chordal rupture and pericardial rupture with cardiac herniation: a case report.

BACKGROUND: Although chest trauma happens very often, accompanying tricuspid valve injuries occur rarely and may be manifested by scarce symptoms and signs. Pericardial rupture with cardiac herniation is even a bigger rarity. Transthoracic echocardiography plays a key role in the diagnosis of valve injuries but is of limited value in cardiac herniation. CASE PRESENTATION: We present the case of 58-year-old man who experienced severe chest trauma in a car accident. Symptoms of right heart failure occurred 10 years after the injury, due to the loss of tricuspid leaflet support caused by the rupture of tendinous chords with significant tricuspid regurgitation. Intraoperatively, old posttraumatic pericardial rupture into left pleura was also found, with partial cardiac herniation and pressure of the edge of pericardium on all left-sided coronary arteries simultaneously. The patient was successfully operated and is free of symptoms 4 years later. CONCLUSIONS: This case emphasizes the importance of timely diagnosis and underlines a mechanism that leads to delayed rupture of the tricuspid valve apparatus. Repeated echocardiography in all patients who experienced chest trauma could be of great importance. Also, given the limited value of echocardiography in posttraumatic pericardial rupture and cardiac herniation, cardiac computed tomography should be performed.

Time-series analysis in the medical domain: a study of Tacrolimus administration and influence on kidney graft function.

There exists a major concern regarding toxic effects of immunosuppressive medication on the kidney graft during post-transplant care, with observed variation in individual susceptibility to adverse drug effects amongst patients. To date, there has been no possibility to identify susceptible patients prospectively. This study analyzes medical data which includes time series of measures of renal function and trough levels of immunosuppressive drug Tacrolimus, with the main aim of identifying patients susceptible to drug toxicity. We evaluate a plethora of time-series distance measures, determining their appropriateness to the domain based on two criteria: (1) preserving the expected correlations between distances, and (2) ability to detect the expected patterns of interaction between immunosuppressive drug levels and renal function. Besides identifying the most suitable time-series distance measures, we observed that the majority of patients do not exhibit an association between impaired graft function and higher Tacrolimus dosing. On the other hand, the minority of patients determined most sensitive to varying Tacrolimus levels showed a strong tendency to prefer low Tacrolimus dosing.