Bayesian Machine Learning Techniques for revealing complex interactions among genetic and clinical factors in association with extra-intestinal Manifestations in IBD patients

The objective of the study is to assess the predictive performance of three different techniques as classifiers for extra-intestinal manifestations in 152 patients with Crohn's disease. Na\uefve Bayes, Bayesian Additive Regression Trees and Bayesian Networks implemented using a Greedy Thick Thinning algorithm for learning dependencies among variables and EM algorithm for learning conditional probabilities associated to each variable are taken into account. Three sets of variables were considered: (i) disease characteristics: presentation, behavior and location (ii) risk factors: age, gender, smoke and familiarity and (iii) genetic polymorphisms of the NOD2, CD14, TNFA, IL12B, and IL1RN genes, whose involvement in Crohn's disease is known or suspected. Extra-intestinal manifestations occurred in 75 patients. Bayesian Networks achieved accuracy of 82% when considering only clinical factors and 89% when considering also genetic information, outperforming the other techniques. CD14 has a small predicting capability. Adding TNFA, IL12B to the 3020insC NOD2 variant improved the accuracy

Quantitative spatial upscaling of categorical information: The multi‐dimensional grid‐point scaling algorithm

Categorical raster datasets often require upscaling to a lower spatial resolution to make them compatible with the scale of ecological analysis. When aggregating categorical data, two critical issues arise: (a) ignoring compositional information present in the high‐resolution grid cells leads to high and uncontrolled loss of information in the scaled dataset; and (b) restricting classes to those present in the high‐resolution dataset assumes validity of the classification scheme at the lower, aggregated resolution. I introduce a new scaling algorithm that aggregates categorical data while simultaneously controlling for information loss by generating a non‐hierarchical, representative, classification system for the aggregated scale. The Multi‐Dimensional Grid‐Point (MDGP) scaling algorithm acknowledges the statistical constraints of compositional count data. In a neutral‐landscape simulation study implementing a full‐factorial design for landscape characteristics, scale factors and algorithm parameters, I evaluated consistency and sensitivity of the scaling algorithm. Consistency and sensitivity were assessed for compositional information retention (IRcmp) and class‐label fidelity (CLF, the probability of recurring scaled class labels) for neutral random landscapes with the same properties. The MDGP‐scaling algorithm consistently preserved information at a significantly higher rate than other commonly used algorithms. Consistency of the algorithm was high for IRcmp and CLF, but coefficients of variation of both metrics across landscapes varied most with class‐abundance distribution. A diminishing return for IRcmp was observed with increasing class‐label precision. Mean class‐label recurrence probability was consistently above 75% for all simulated landscape types, scale factors and class‐label precisions. The MDGP‐scaling algorithm is the first algorithm that generates data‐driven, scale‐specific classification schemes while conducting spatial data aggregation. Consistent gain in IRcmp and the associated reproducibility of classification systems strongly suggest that the increased precision of scaled maps will improve ecological models that rely on upscaling of high‐resolution categorical raster data

Adherence to antiretroviral therapy in patients enrolled in a comprehensive care program in Cambodia: a 24-month follow-up assessment

BACKGROUND: The long-term maintenance of antiretroviral therapy (ART) remains an important issue, especially in limited-resource settings where additional barriers exist. A cross-sectional study was performed 24 months after ART initiation for patients treated in Cambodia in order to estimate the prevalence and identify determinants of non-adherence. METHODS: Adults receiving ART for 24 +/- 2 months were considered eligible for the study. Self-reported non-adherence was defined according to an algorithm based on six items. The questionnaire also assessed ART-related side effects and HIV disclosure. HIV-1 RNA plasma viral load was measured using real-time PCR. Multivariate rare events logistic regression analysis was used to identify independent factors associated with non-adherence. RESULTS: A total of 346 patients participated in the study. At 24 months, 95% of patients were adherent, 80% had HIV RNA <40 copies/ml and 75% had CD4+ T-cell counts >200 cells/mm3. Virological success was significantly higher in adherent patients than in non-adherent patients (81% versus 56%, P=0.021). Living in a rural area, limited HIV disclosure and perceived lipodystrophy were independently associated with non-adherence. CONCLUSIONS: At 24 months, adherence to ART was high and explained positive virological outcomes. In order to maintain adherence and long-term virological benefits, special attention should be given to patients living in rural areas, those with lipodystrophy-related symptoms and others who express difficulties disclosing their condition to close family members

Epidemiological Algorithm and Early Molecular Testing to Prevent COVID-19 Outbreaks in a Mexican Oncologic Center

Introduction: Prevention strategies and detection of latent COVID-19 infections in oncology staff and oncologic patients are essential to prevent outbreaks in a cancer center. In this study, we used two statistical predictive models in oncology staff and patients from the radiotherapy area to prevent outbreaks and detect COVID-19 cases. Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data was collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). According to the algorithm\u27s models, cut-off values were established. SARS-CoV-2 qRT-PCR tests confirmed the algorithm\u27s positive individuals. Results: Oncology staff members (n=142) were tested, and 14% (n=20) were positives for the R-Track algorithm; 75% (n=15) were qRT-PCR positive. The S-Facts algorithm identified 7.75% (n=11) positive oncology staff members, and 81.82% (n=9) were qRT-PCR positive. Oncology patients (n=369) were evaluated, and 1.36% (n=5) were positive for the algorithms. The 5 patients (100%) were confirmed by qRT-PCR at a very early stage. Conclusions: The proposed algorithms could prove to become an essential prevention tool in countries where qRT-PCR tests and vaccines are insufficient for the population

Epidemiological Algorithm for Early Detection of COVID-19 Cases in a Mexican Oncologic Center

An early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the R-Track algorithm; 75% (n = 15) were qRT-PCR positive. The S-Facts Algorithm identified 7.75% (n = 11) positive oncology staff members, and 81.82% (n = 9) were qRT-PCR positive. Oncology patients (n = 369) were evaluated, and 1.36% (n = 5) were positive for the Algorithm used. The five patients (100%) were confirmed by qRT-PCR. (4) Conclusions: The proposed early detection tools have proved to be a low-cost and efficient tool in a country where qRT-PCR tests and vaccines are insufficient for the population. View Full-Tex

Saliency-driven system models for cell analysis with deep learning.

Background and objectivesSaliency refers to the visual perception quality that makes objects in a scene to stand out from others and attract attention. While computational saliency models can simulate the expert's visual attention, there is little evidence about how these models perform when used to predict the cytopathologist's eye fixations. Saliency models may be the key to instrumenting fast object detection on large Pap smear slides under real noisy conditions, artifacts, and cell occlusions. This paper describes how our computational schemes retrieve regions of interest (ROI) of clinical relevance using visual attention models. We also compare the performance of different computed saliency models as part of cell screening tasks, aiming to design a computer-aided diagnosis systems that supports cytopathologists.MethodWe record eye fixation maps from cytopathologists at work, and compare with 13 different saliency prediction algorithms, including deep learning. We develop cell-specific convolutional neural networks (CNN) to investigate the impact of bottom-up and top-down factors on saliency prediction from real routine exams. By combining the eye tracking data from pathologists with computed saliency models, we assess algorithms reliability in identifying clinically relevant cells.ResultsThe proposed cell-specific CNN model outperforms all other saliency prediction methods, particularly regarding the number of false positives. Our algorithm also detects the most clinically relevant cells, which are among the three top salient regions, with accuracy above 98% for all diseases, except carcinoma (87%). Bottom-up methods performed satisfactorily, with saliency maps that enabled ROI detection above 75% for carcinoma and 86% for other pathologies.ConclusionsROIs extraction using our saliency prediction methods enabled ranking the most relevant clinical areas within the image, a viable data reduction strategy to guide automatic analyses of Pap smear slides. Top-down factors for saliency prediction on cell images increases the accuracy of the estimated maps while bottom-up algorithms proved to be useful for predicting the cytopathologist's eye fixations depending on parameters, such as the number of false positive and negative. Our contributions are: comparison among 13 state-of-the-art saliency models to cytopathologists' visual attention and deliver a method that the associate the most conspicuous regions to clinically relevant cells

Feature Relevance in Ward’s Hierarchical Clustering Using the Lp Norm

In this paper we introduce a new hierarchical clustering algorithm called Ward p . Unlike the original Ward, Ward p generates feature weights, which can be seen as feature rescaling factors thanks to the use of the L p norm. The feature weights are cluster dependent, allowing a feature to have different degrees of relevance at different clusters. We validate our method by performing experiments on a total of 75 real-world and synthetic datasets, with and without added features made of uniformly random noise. Our experiments show that: (i) the use of our feature weighting method produces results that are superior to those produced by the original Ward method on datasets containing noise features; (ii) it is indeed possible to estimate a good exponent p under a totally unsupervised framework. The clusterings produced by Ward p are dependent on p. This makes the estimation of a good value for this exponent a requirement for this algorithm, and indeed for any other also based on the Lp norm.Peer reviewedFinal Accepted Versio

On Relaxed Averaged Alternating Reflections (RAAR) Algorithm for Phase Retrieval from Structured Illuminations

In this paper, as opposed to the random phase masks, the structured illuminations with a pixel-dependent deterministic phase shift are considered to derandomize the model setup. The RAAR algorithm is modified to adapt to two or more diffraction patterns, and the modified RAAR algorithm operates in Fourier domain rather than space domain. The local convergence of the RAAR algorithm is proved by some eigenvalue analysis. Numerical simulations is presented to demonstrate the effectiveness and stability of the algorithm compared to the HIO (Hybrid Input-Output) method. The numerical performances show the global convergence of the RAAR in our tests.Comment: 17 pages, 26 figures, submitting to Inverse Problem