Reasoning about Lava effusion: from Geographical Information Systems to Answer Set Programming

This article describes our implementation in Answer Set Programming of a reasoning system that models the flow of lava in volcanic eruptions. Our system can be employed in the validation of evacuation plans. To demonstrate the feasibility of such approach, we adopt a simplified yet realistic model of how lava flows, and apply it to altitude data from the Etna volcano

A Novel Approach to the Problem of Non-uniqueness of the Solution in Hierarchical Clustering

Abstract—The existence of multiple solutions in clustering, and in hierarchical clustering in particular, is often ignored in practical applications. However, this is a non-trivial problem, as different data orderings can result in different interpretations of the same data. The method presented here offers a solution to this issue. It is based on the definition of an equivalence relation over dendrograms that allows developing all and only the significantly different dendrograms for the same dataset, thus reducing to polynomial the computational complexity from the exponential one obtained when all possible dendrograms are considered. Experimental results in the neuroimaging and bioinformatics domains show the effectiveness of the proposed method. Index Terms—Hierarchical clustering, dendrogram equivalence relation, bioinformatics, neuroimaging I

Development of an Artificial Intelligence Model to Guide the Management of Blood Pressure, Fluid Volume, and Dialysis Dose in End-Stage Kidney Disease Patients: Proof of Concept and First Clinical Assessment

International audienceBackground: Fluid volume and blood pressure (BP) management are crucial endpoints for end-stage kidney disease patients. BP control in clinical practice mainly relies on reducing extracellular fluid volume overload by diminishing targeted postdialysis weight. This approach exposes dialysis patients to intradialytic hypotensive episodes.Summary: Both chronic hypertension and intradialytic hypotension lead to adverse long-term outcomes. Achieving the optimal trade-off between adequate fluid removal and the risk of intradialytic adverse events is a complex task in clinical practice given the multiple patient-related and dialysis-related factors affecting the hemodynamic response to treatment. State-of-the-art artificial intelligence has been adopted in other complex decision-making tasks for dialysis patients and may help personalize the multiple dialysis-related prescriptions affecting patients' intradialytic hemodynamics. As a proof of concept, we developed a multiple-endpoint model predicting session-specific Kt/V, fluid volume removal, heart rate, and BP based on patient characteristics, historic hemodynamic responses, and dialysis-related prescriptions.Key messages: The accuracy and precision of this preliminary model is extremely encouraging. Such analytic tools may be used to anticipate patients' reactions through simulation so that the best strategy can be chosen based on clinical judgment or formal utility functions

Kinetic of lactic acid formation in kefir

This bachelor thesis deals with the kinetic production of lactic acid and consumption of lactose in the kefir fermentation from one type of milk under the same conditions. The theoretical part contains information on kefir and kefir grains, lactose intolerance, antimicrobial properties and high-performance liquid chromatography. The experimental part describes the methods and procedures used in the manufacture of kefir itself, the determination of lactic acid and lactose. Kefir samples were tested at precise time intervals for lactose, lactic acid and lactobacilli. The data was graphically evaluated and commented. The results of this work may be beneficial for consumers with lactose maldigestion and for consumers searching for qualite source of lactobacilli for enhancing their gut microflora

An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients

International audienceManaging anemia in hemodialysis patients can be challenging because of competing therapeutic targets and individual variability. Because therapy recommendations provided by a decision support system can benefit both patients and doctors, we evaluated the impact of an artificial intelligence decision support system, the Anemia Control Model (ACM), on anemia outcomes. Based on patient profiles, the ACM was built to recommend suitable erythropoietic-stimulating agent doses. Our retrospective study consisted of a 12-month control phase (standard anemia care), followed by a 12-month observation phase (ACM-guided care) encompassing 752 patients undergoing hemodialysis therapy in 3 NephroCare clinics located in separate countries. The percentage of hemoglobin values on target, the median darbepoetin dose, and individual hemoglobin fluctuation (estimated from the intrapatient hemoglobin standard deviation) were deemed primary outcomes. In the observation phase, median darbepoetin consumption significantly decreased from 0.63 to 0.46 μg/kg/month, whereas on-target hemoglobin values significantly increased from 70.6% to 76.6%, reaching 83.2% when the ACM suggestions were implemented. Moreover, ACM introduction led to a significant decrease in hemoglobin fluctuation (intrapatient standard deviation decreased from 0.95 g/dl to 0.83 g/dl). Thus, ACM support helped improve anemia outcomes of hemodialysis patients, minimizing erythropoietic-stimulating agent use with the potential to reduce the cost of treatment

Bland-Altman analysis of observed/predicted Hb values.

<p>Bland-Altman analysis of observed/predicted Hb values.</p

Anemia management profile and hemoglobin concentrations.

<p>Anemia management profile and hemoglobin concentrations.</p

ANN anemia modeling performances in training and test phases.

<p>ANN anemia modeling performances in training and test phases.</p

Baseline patient characteristics.

<p>Baseline patient characteristics.</p

Steps involved in the development and validation of the Artificial Neural Network anemia modeling.

<p>Steps involved in the development and validation of the Artificial Neural Network anemia modeling.</p