Comparison of in vitro cell survival predictions using Monte Carlo methods for proton irradiation.

Purpose: It is possible to combine theoretical models with Monte Carlo simulations to investigate the relationship between radiation-induced initial DNA damage and cell survival. Several combinations of models have been proposed in recent years, sparking interest in comparing their predictions in view of future clinical applications.

Methods: Two in silico methods for calculating cell survival fractions were optimized for proton irradiation of the Chinese hamster V79 cell line, for LET values ranging from 3.

Interpretable multimodal classification for age-related macular degeneration diagnosis.

Explainable Artificial Intelligence (XAI) is an emerging machine learning field that has been successful in medical image analysis. Interpretable approaches are able to "unbox" the black-box decisions made by AI systems, aiding medical doctors to justify their diagnostics better. In this paper, we analyze the performance of three different XAI strategies for medical image analysis in ophthalmology.

Combination of unsupervised discretization methods for credit risk.

Creating robust and explainable statistical learning models is essential in credit risk management. For this purpose, equally spaced or frequent discretization is the de facto choice when building predictive models. The methods above have limitations, given that when the discretization procedure is constrained, the underlying patterns are lost.

High tibial derotation osteotomy for distal extensor mechanism alignment in patients with squinting patella due to increased external tibial torsion.

Background: The patellofemoral joint is often affected by torsionaldisorders of the lower limb, causing pain, instability and knee degeneration. The aims of this study were to determine functional outcomes of patients who underwent a high tibial derotation osteotomy (HTDO) for symptomatic squinting patella due to increased external tibial torsion. Moreover, factors associated with inferior clinical outcomes were investigated.

Risk Factors for Community-Acquired Acute Kidney Injury in Medical Patients: A Nested Case-Control Study.

Introduction: Research about the risk factors associated with community-acquired acute kidney injury (CA-AKI) in acute medical diseases is scarce. Data extrapolation from surgical to medical illnesses is questionable.

Objectives: To evaluate potential risk factors and hospital outcomes associated with a CA-AKI in medical illnesses.

Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies.

We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolution of math problems. A binary classification problem was constructed using correlations and phase synchronization between different electroencephalographic channels as characteristics and, as labels or classes, the math performances of individuals participating in specially designed experiments.

[Prevalence of atrial fibrillation and predictors of its occurrence in patients with dual-chamber pacemakers].

Objectives: To assess the prevalence of atrial fibrillation (AF) in patients with dual-chamber pacemakers (DP), determine the variables associated with development of AF and evaluate the changes in AF's management by physicians.

Methods: Five hundred patients with DP were prospectively included and interrogated. When AF was detected physicians in charge of the patient were warned.