Rule-based Programming for Building Expert Systems: a Comparison in the Microbiological Data Validation and Surveillance Domain

Abstract In this work, we compare three rule-based programming tools used for building an expert system for microbiological laboratory data validation and bacteria infections monitoring. The first prototype of the system was implemented in KAPPA-PC. We report on the implementation and performance by comparing KAPPA-PC with two other more recent tools, namely JESS and ILOG JRULES. In order to test each tool we realized three simple test applications capable to perform some tasks that are peculiar of our expert system

Prevalence and incidence of low back pain among runners: A systematic review

Background: Running is one of the most popular sports worldwide. Despite low back pain (LBP) represents the most common musculoskeletal disorder in population and in sports, there is currently sparse evidence about prevalence, incidence and risk factors for LBP among runners. The aims of this systematic review were to investigate among runners: prevalence and incidence of LBP and specific risk factors for the onset of LBP. Methods: A systematic review has been conducted according to the guidelines of the PRISMA statement. The research was conducted in the following databases from their inception to 31st of July 2019: PubMed; CINAHL; Google Scholar; Ovid; PsycINFO; PSYNDEX; Embase; SPORTDiscus; Scientific Electronic Library Online; Cochrane Library and Web of Science. The checklists of The Joanna Briggs Institute Critical Appraisal tools were used to investigate the risk of bias of the included studies. Results: Nineteen studies were included and the interrater agreement for full-text selection was good (K = 0.78; 0.61-0.80 IC 95%). Overall, low values of prevalence (0.7-20.2%) and incidence (0.3-22%) of LBP among runners were reported. Most reported risk factors were: running for more than 6 years; body mass index > 24; higher physical height; not performing traditional aerobics activity weekly; restricted range of motion of hip flexion; difference between leg-length; poor hamstrings and back flexibility. Conclusions: Prevalence and incidence of LBP among runners are low compared to the others running related injuries and to general, or specific population of athletes. View the low level of incidence and prevalence of LBP, running could be interpreted as a protective factor against the onset of LBP. Systematic review registration: PROSPERO CRD42018102001

Ultrasonography of quadriceps femoris muscle and subcutaneous fat tissue and body composition by BIVA in chronic dialysis patients

Protein Energy Wasting (PEW) in hemodialysis (HD) patients is a multifactorial condition due to specific pathology-related pathogenetic mechanisms, leading to loss of skeletal muscle mass in HD patients. Computed Tomography and Magnetic Resonance Imaging still represent the gold standard techniques for body composition assessment. However, their widespread application in clinical practice is difficult and body composition evaluation in HD patients is mainly based on conventional anthropometric nutritional indexes and bioelectrical impedance vector analysis (BIVA). Little data is currently available on ultrasound (US)-based measurements of muscle mass and fat tissue in this clinical setting. The purpose of our study is to ascertain: (1) if there are differences between quadriceps rectus femoris muscle (QRFM) thickness and abdominal/thigh subcutaneous fat tissue (SFT) measured by US between HD patients and healthy subjects; (2) if there is any correlation between QRFM and abdominal/thigh SFT thickness by US, and BIVA/conventional nutritional indexes in HD patients. We enrolled 65 consecutive HD patients and 33 healthy subjects. Demographic and laboratory were collected. The malnutrition inflammation score (MIS) was calculated. Using B-mode US system, the QRFM and SFT thicknesses were measured at the level of three landmarks in both thighs (superior anterior iliac spine, upper pole of the patella, the midpoint of the tract included between the previous points). SFT was also measured at the level of the periumbilical point. The mono frequency (50 KHz) BIVA was conducted using bioelectrical measurements (Rz, resistance; Xc, reactance; adjusted for height, Rz/H and Xc/H; PA, phase angle). 58.5% were men and the mean age was 69 (SD 13.7) years. QRFM and thigh SFT thicknesses were reduced in HD patients as compared to healthy subjects (p < 0.01). Similarly, also BIVA parameters, expression of lean body mass, were lower (p < 0.001), except for Rz and Rz/H in HD patients. The average QRFM thickness of both thighs at top, mid, lower landmarks were positively correlated with PA and body cell mass (BCM) by BIVA, while negatively correlated with Rz/H (p < 0.05). Abdominal SFT was positively correlated with PA, BCM and basal metabolic rate (BMR) (p < 0.05). Our study shows that ultrasound QRFM and thigh SFT thicknesses were reduced in HD patients and that muscle ultrasound measurements were significantly correlated with BIVA parameters

A hybrid approach to clinical guideline and to basic medical knowledge conformance

Abstract. Several computer-based approaches to Clinical Guidelines have been developed in the last two decades. However, only recently the community has started to cope with the fact that Clinical Guidelines are just a part of the medical knowledge that physicians have to take into account when treating patients. The procedural knowledge in the guidelines have to be complemented by additional declarative medical knowledge. In this paper, we analyse such an interaction, by studying the conformance problem, defined as evaluating the adherence of a set of performed clinical actions w.r.t. the behaviour recommended by the guideline and by the medical knowledge

Enrollment of Neonates in More Than One Clinical Trial

Because the highest rates of morbidity and mortality in neonates are seen in those born at 1 clinical trial. Neonatal units that have the infrastructure and resources to engage in research frequently face the question of whether it is permissible to enroll a neonate in >1 trial. This article examines the pertinent scientific, ethical, regulatory, and industry issues that should be taken into account when considering enrolling neonates in multiple clinical studies

Supervised and unsupervised learning techniques for profiling SAGE results.

Using Serial Analysis it is now possible to obtain quantita­ tive measurements of the expression of thousands of genes present in a biological sample. Serial analysis yield a global view of gene expression that can be used in a number of interesting ways. In this paper we are investigating two different approaches for analyz­ ing the analysis of data obtained from SAGE experiments. The first one is a supervised learning process: a classification of cancer tissue using decision trees and Support Vector Machines (SVM). After that, we will analyze the results achieved by a unsupervised learning method: hierar­ chical clustering. Finally, we tried to characterize the groups found by clustering, using the classification techniques cited before

An Abductive Multi-Agent System for Medical Services Coordination

The paper presents MeSSyCo, a multi-agent system that integrates and coordi nates heterogeneous medical services. Agents in MeSSyCo may perform different tasks such as diagnosis and intelligent resource allocation and coordinate themselves through an infrastructure based on a combination of abductive and probabilistic reasoning. In this way a set of specialized medical service providers could be aggregated into a system able to perform more complex medical tasks

Applicazione di algoritmi di apprendimento automatico per la previsione di "trend" nel settore della moda

Nellʼarticolo si mostra come sia possibile ed utile applicare tecniche di Intelligenza Artificiale in un settore altamente creativo come quello della moda, con particolare riferimento alla previsione dei «trend». Il lavoro descrive un prototipo di sistema basato sulla conoscenza in grado di generare automaticamente le previsioni dei «trend» nel settore della moda. Nel nostro caso, ogni «trend» è rappresentato dai colori utilizzabili per le nuove collezioni che meglio rappresentano le parole chiave e le immagini sulle quali lo stilista ha deciso di incentrare una collezione. Per la rappresentazione in modelli della conoscenza utilizzata dal sistema per le previsioni sono state sperimentate due diverse metodologie: le reti Bayesiane e gli alberi decisionali. Questi modelli sono generati mediante lʼutilizzo di tecniche di apprendimento automatico a partire da dataset di previsioni effettuate negli anni passati. Nellʼarticolo vengono presentati il modo con cui utilizzare tali modelli per la previsione e gli esperimenti svolti al fine di valutare le loro performance

Exploiting Association and Correlation Rules Parameters for Improving the K2 Algorithm

A Bayesian network is an appropriate tool to deal with the uncertainty that is typical of real-life applications. Bayesian network arcs represent statistical dependence between different variables. In the data mining field, association and correlation rules can be interpreted as well as expressing statistical dependence relations. K2 is a well-known algorithm which is able to learn Bayesian network. In this paper we present two extensions of K2 called K2-Lift and K2-X2 that exploit two parameters normally defined in relation to association and correlation rules for learning Bayesian networks. The experiments performed show that K2-Lift and K2-X2 improve K2 with respect to both the quality of the learned network and the execution tim

A Knowledge-Based System for Fashion Trend Forecasting

In this paper, we show how artificial intelligence techniques can be applied for the forecasting of trends in the high creative domain of fashion. We describe a knowledge-based system that, starting from a set of keywords and pictures representing the concepts on which a fashion stylist chooses to base a new collection, is able to automatically create a trend forecast composed by the set of colors that better express these target concepts. In order to model the knowledge used by the system to forecast trends, we experimented Bayesian networks. This kind of model is learned from a dataset of past trends by using different algorithms. We show how Bayesian networks can be used to make the forecast and the experiments made in order to evaluate their performances