Pooling random forest and functional data analysis for biomedical signals supervised classification: Theory and application to electrocardiogram data.

Scientific progress has contributed to creating many devices to gather vast amounts of biomedical data over time. The goal of these devices is generally to monitor people's health conditions, diagnose, and prevent patients' diseases, for example, to discover cardiovascular disorders or predict epileptic seizures. A common way of investigating these data is classification, but these instruments generate signals often characterized by high dimensionality. Learning from these data is definitely a challenging task due to many issues, for example, the trade-off between complexity and accuracy and the course of dimensionality. This study proposes a supervised classification method based on the joint use of functional data analysis, classification trees, and random forest to deal with massive biomedical data recorded over time. For this purpose, this research suggests different original tools to extract features and train functional classifiers, interpret the classification rules, assess leaves' quality and composition, avoid the classical drawbacks due to the COD, and improve the accuracy of the functional classifiers. Focusing on ECG data as a possible example, the final purpose of this study is to offer an original approach to identify and classify patients at risk using different types of biomedical signals. The results confirm that this line of research is exciting; indeed, the interpretative tools show evidence to be very useful for understanding classification rules. Furthermore, the performance of the proposed functional classifier, in terms of accuracy, is excellent because the latter breaks the previous classification record regarding a well-known ECG dataset.

Graphical comparisons of relative disease burden across multiple risk factors.

BACKGROUND: Population attributable fractions (PAF) measure the proportion of disease prevalence that would be avoided in a hypothetical population, similar to the population of interest, but where a particular risk factor is eliminated. They are extensively used in epidemiology to quantify and compare disease burden due to various risk factors, and directly influence public policy regarding possible health interventions. In contrast to individual specific metrics such as relative risks and odds ratios, attributable fractions depend jointly on both risk factor prevalence and relative risk. The relative contributions of these two components is important, and usually needs to be presented in summary tables that are presented together with the attributable fraction calculation. However, representing PAF in an accessible graphical format, that captures both prevalence and relative risk, may assist interpretation. METHODS: Taylor-series approximations to PAF in terms of risk factor prevalence and log-odds ratio are derived that facilitate simultaneous representation of PAF, risk factor prevalence and risk-factor/disease log-odds ratios on a single co-ordinate axis. Methods are developed for binary, multi-category and continuous exposure variables. RESULTS: The methods are demonstrated using INTERSTROKE, a large international case control dataset focused on risk factors for stroke. CONCLUSIONS: The described methods could be used as a complement to tables summarizing prevalence, odds ratios and PAF, and may convey the same information in a more intuitive and visually appealing manner. The suggested nomogram can also be used to visually estimate the effects of health interventions which only partially reduce risk factor prevalence. Finally, in the binary risk factor case, the approximations can also be used to quickly convert logistic regression coefficients for a risk factor into approximate PAFs.

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data.

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

Cardiac resynchronization therapy with multipoint pacing via quadripolar lead versus traditional biventricular pacing: A systematic review and meta-analysis of clinical studies on hemodynamic, clinical, and prognostic parameters.

BACKGROUND: Cardiac resynchronization therapy (CRT) is one of the cornerstones of heart failure (HF) therapy, as it has reduced mortality and morbidity and has shown improvement in functional capacity. Multipoint pacing (MPP) is a way of configuring CRT with the aim to improve the percentage of patients who respond to CRT. OBJECTIVE: To demonstrate the effectiveness of the MPP compared to traditional biventricular pacing (BiV). METHODS: We performed a systematic review and meta-analysis according to PRISMA guidelines of studies in which MPP vs BiV strategy were compared. RESULTS: MPP use is associated with a higher rate of patients experiencing functional improvement (odds ratio: 2.51, 95% confidence interval [CI], 1.56-4.06; P = .0002) and with higher delta LV dP/dtmax (mean difference, 1.82; 95% CI, 0.24-3.39; P = .0240) with respect to BiV. MPP and BiV have no significantly different effect on left ventricular end-systolic volume (LVESV) (mean difference, 0.39; 95% CI, -11.12 to 11.89; P = .9475); moreover, there is no significant difference between the 2 treatments regarding hospitalization for HF (odds ratio, 0.70; 95% CI, 0.32 to 1.54; P = .3816) and all-cause death (odds ratio, 0.81; 95% CI, 0.40 to 1.62; P = .5460). MPP is associated with a significantly lower projected battery longevity (mean difference -8.66 months; 95% CI, -13.67 to -3.66; P = .00007) with respect to BiV. CONCLUSION: MPP significantly improves functional class and acute hemodynamic parameters with respect to BiV. Prognostic indices and LVESV are not significantly influenced by MPP. MPP is associated with a significant reduction in projected battery longevity.

On the Use of Propensity Score Matching in Biomedicine and Pulmonology

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