Disease pathogenesis, a type of domain knowledge about biological mechanisms leading to diseases, has not been adequately encoded in machine-learning-based medical diagnostic models because of the inter-patient variabilities and complex dependencies of the underlying pathogenetic mechanisms. We propose 1) a novel pathogenesis probabilistic graphical model (PPGM) to quantify the dynamics underpinning patient-specific data and pathogenetic domain knowledge, 2) a Bayesian-based inference paradigm to answer the medical queries and forecast acute onsets. The PPGM model consists of two components a Bayesian network of patient attributes and a temporal model of pathogenetic mechanisms. The model structure was reconstructed from expert knowledge elicitation, and its parameters were estimated using Variational Expectation-Maximization algorithms. We benchmarked our model with two well-established hidden Markov models (HMMs) - Input-output HMM (IO-HMM) and Switching Auto-Regressive HMM (SAR-HMM) - to evaluate the computational costs, forecasting performance, and execution time. Two case studies on Obstructive Sleep Apnea (OSA) and Paroxysmal Atrial Fibrillation (PAF) were used to validate the model. While the performance of the parameter learning step was equivalent to those of IO-HMM and SAR-HMM models, our model forecasting ability was outperforming those two models. The merits of the PPGM model are its representation capability to capture the dynamics of pathogenesis and perform medical inferences and its interpretability for physicians. The model has been used to perform medical queries and forecast the acute onset of OSA and PAF. Additional applications of the model include prognostic healthcare and preventive personalized treatments.Artificial Intelligence is the capability of a machine to imitate intelligent human behavior. An important impact can be expected from Artificial Intelligence throughout the workflow of radiotherapy (such as automated organ segmentation, treatment planning, prediction of outcome and quality assurance). However, ethical concerns regarding the binding agreement between the patient and the physician have followed the introduction of artificial intelligence. Through the recording of personal and social moral values in addition to the usual demographics and the implementation of these as distinctive inputs to matching algorithms, ethical concerns such as consistency, applicability and relevance can be solved. In the meantime, physicians' awareness of the ethical dimension in their decision-making should be challenged, so that they prioritize treating their patients and not diseases, remain vigilant to preserve patient safety, avoid unintended harm and establish institutional policies on these issues.We develop a predictive prognosis model to support medical experts in their clinical decision-making process in Intensive Care Units (ICUs) (a) to enhance early mortality prediction, (b) to make more efficient medical decisions about patients at higher risk, and (c) to evaluate the effectiveness of new treatments or detect changes in clinical practice. It is a machine learning hierarchical model based on Bayesian classifiers built from some recorded features of a real-world ICU cohort, to bring about the assessment of the risk of mortality, also predicting destination at ICU discharge if the patient survives, or the cause of death otherwise, constructed as an ensemble of five base Bayesian classifiers by using the average ensemble criterion with weights, and we name it the Ensemble Weighted Average (EWA). We compare EWA against other state-of-the-art machine learning predictive models. https://www.selleckchem.com/products/picropodophyllin-ppp.html Our results show that EWA outperforms its competitors, presenting in addition the advantage over the ensemble using the majority vote criterion of allowing to associate a confidence level to the provided predictions. We also prove the convenience of locally recalibrate from data the standard model used to predict the mortality risk based on the APACHE II score, although as a predictive model it is weaker than the other.The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns (i) a single mutation, which provides a large resistance benefit, or (ii) multiple mutations, each conferring a small benefit, which combine to yield high-level resistance. Using stochastic modeling, we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the extent of this limitation depends on the combination of drug type and pharmacokinetic profile. Further, if multiple mutations are necessary, adaptive treatment, which only suppresses the bacterial population, delays treatment failure due to resistance for a longer time than aggressive treatment, which aims at eradication. Systemic sclerosis associated pulmonary arterial hypertension (SSc-PAH) is of clinical significance owing to its poor outcome. One of the explanations for the outcome is the co-presence of left heart disease (LHD). The aim of this study is to assess LHD phenotype in patients with SSc and pulmonary hypertension (PH). This study included consecutive patients with SSc who underwent right heart catheterisation to diagnose PAH. Heart failure with preserved ejection fraction (HFpEF) was evaluated according to the recommendation of 6th WSPH and to the Framingham criteria. In total, 76 patients were enrolled in this study. Of them, 42 had PH (mPAP >20 mmHg) with a normal left ventricle ejection fraction (≥50%). Among the 42 patients, four and three patients were classified "HFpEF not excluded" and "HFpEF confirmed" whereas 10 had a clinical diagnosis of HFpEF according to 6th WSPH and Framingham criteria, respectively. These differences were due mainly to relatively low PAWP (<13 mmHg). By a combination of ROC curve and logistic regression analyses, left atrial dimension and left ventricular end-diastolic volume index assessed with echocardiography and cardiac MRI, respectively, had significantly higher predictive values for detecting the complication of HFpEF rather than PAWP.