Over the years, research studies have shown there is a key connection between the microbial community in the gut, genes, and immune system. Understanding this association may help discover the cause of complex chronic idiopathic disorders such as inflammatory bowel disease. Even though important efforts have been put into the field, the functions, dynamics, and causation of dysbiosis state performed by the microbial community remains unclear. Machine learning models can help elucidate important connections and relationships between microbes in the human host. Our study aims to extend the current knowledge of associations between the human microbiome and health and disease through the application of dynamic Bayesian networks to describe the temporal variation of the gut microbiota and dynamic relationships between taxonomic entities and clinical variables. We develop a set of preprocessing steps to clean, filter, select, integrate, and model informative metagenomics, metatranscriptomics, and metabolomics longitudinal data from the Human Microbiome Project. This study accomplishes novel network models with satisfactory predictive performance (accuracy = 0.648) for each inflammatory bowel disease state, validating Bayesian networks as a framework for developing interpretable models to help understand the basic ways the different biological entities (taxa, genes, metabolites) interact with each other in a given environment (human gut) over time. These findings can serve as a starting point to advance the discovery of novel therapeutic approaches and new biomarkers for precision medicine.

The purpose of this work is to study the gap between the research evidence and the clinical practice in the physical rehabilitation of people with cerebral palsy. A review process was performed to (1) identify physical therapies to improve postural control in children with cerebral palsy and (2) determine the scientific evidence supporting the effectiveness of those therapies. A Likert-based survey addressing a total of 43 healthcare professionals involved in pediatric physical therapy departments in Spain was carried out. The discussion was mainly supported by studies of level I or II evidence (according to the Oxford scale). The search process yielded 50 studies reporting 16 therapies. A strong positive correlation between the most used treatments and elevated levels of satisfaction was found. Some well-known but not often used techniques, such as hippotherapy, were identified. The treatment with the highest degree of use and satisfaction—neurodevelopment therapy (Bobath)—and some emerging techniques, such as virtual reality, were also identified. The fact that there is a meaningful gap between clinical practice and the scientific evidence was confirmed. The identified gap brings a certain degree of controversy. While some classic and well-known therapies had poor levels of supporting evidence, other relatively new approaches showed promising results.

Unplanned hospital readmission is a problem that affects hospitals worldwide and is due to different factors. The identification of those factors can help determine which patients are at greater risk of hospital readmission for early intervention. Our end goal is to predict and identify patterns to (i) feed a decision support system for efficient management of patients and resources and (ii) detect patients at high risk of 30-days readmission enabling preventive actions to improve management of hospital discharges. This study aims to analyze whether natural language processing and specifically keyword extractions tools and sentiment analysis can support 30-days readmission prediction. Features extracted from medical history notes and discharge reports were used to train a Logistic Regression model. The resulting model obtains an AUC of 0.63 indicating that the sentiment polarity score of the discharge report and several of the extracted keywords are representative features to consider.