Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Comparison of different numerical methods for the solution of the time-fractional reaction-diffusion equation with variable diffusion coefficient

In this work we perform a comparison of two different numerical schemes for the solution of the time-fractional diffusion equation with variable diffusion coefficient and a nonlinear source term. The two methods are the implicit numerical scheme presented in [M.L. Morgado, M. Rebelo, Numerical approximation of distributed order reaction- diffusion equations, Journal of Computational and Applied Mathematics 275 (2015) 216-227] that is adapted to our type of equation, and a colocation method where Chebyshev polynomials are used to reduce the fractional differential equation to a system of ordinary differential equations

Influence of saliva and mucin on the adhesion of Candida oral clinical isolates

Objectives: This research work intends to clarify the role of artificial saliva, in particularly the role of mucin, a salivary protein, on the surface properties and adhesion ability of Candida spp. oral clinical isolates to abiotic surfaces. Methods: Four oral clinical isolates of Candida spp. were used: two Candida albicans strains (AC; AM) and two Candida parapsilosis strains (AD; AM2). The strains were isolated from patients using oral prosthesis. The microorganisms were cultured in the absence or presence of mucin and artificial saliva, and their adhesion to an abiotic surface (coated with mucin and artificial saliva) was evaluated. Results: The presence of mucin per se onto the abiotic surface decreased the adhesion of all strains, although the combination of mucin with artificial saliva had reduced this effect. No direct correlation between adhesion and the surface free energies of adhesion of the microorganisms was found. Significance: Candida spp. were human commensal microorganisms that became pathogenic when the host immune defenses were compromised. Medical devices were colonized by Candida spp. particularly, oral prostheses, which might lead to the degradation of the prostheses and systemic infections. The salivary secretions that constantly cover the oral cavity influenced Candida spp. adhesion process. Therefore, it was important to understand the interactions between Candida spp., salivary proteins and the characteristic of oral prosthesis when developing materials for oral prostheses.

Polymer electrolytes for electrochromic devices

Polymer electrolytes are currently the focus of much attention as potential electrolytes in electrochemical devices such as batteries, display devices and sensors. Generically, solid polymer electrolytes (SPEs) are mixtures of salts with soft polar polymers. SPEs have many advantages including high energy density, no risk of leakage, no issues related to the presence of solvent, wide electrochemical stability windows, simplified processability and light weight. With the goal of developing a new family of environmentally friendly multifunctional biohybrid materials displaying high ionic conductivity we have produced in the present work, flexible films based on different polymers or hybrids incorporating different salts. The polymer electrolytes studied here have been characterized by means of Differential Scanning Calorimetry, Thermogravimetric Analysis, X-ray diffraction, Polarized Optical Microscopy, complex impedance spectroscopy and cyclic voltammetry. An evaluation of the performance of the sample with the highest conductivity as electrolyte in all solid-state ECDs was performed.

The diagnostic and prognostic role of liquid-based cytology: are we ready to monitor therapy and resistance?

Here, we evaluate the diagnostic and prognostic role of liquid-based cytology (LBC) in different body lesions, including thyroid, lung, effusions and malignant breast lesions. LBC has gained consensus after being applied to both non-gynecologic and fine-needle aspiration cytology. Although some remain sceptical regarding the diagnostic efficacy of LBC, mainly when used alone, in recent years, good results have been obtained as long as it showed a high diagnostic accuracy. Here, we discuss the additional possibility of storing material for the application of ancillary techniques (immunocytochemistry–molecular analysis) with several diagnostic and prognostic advantages, which may pave the way for the challenging evaluation of both monitoring responses to treatment and resistance to targeted therapies in thyroid, lung, breast carcinoma or malignant effusions. Furthermore, it provides the use of several molecular spots as specific targets for personalized therapy.

Impact of Delftia tsuruhatensis and Achromobacter xylosoxidans on Escherichia coli dual-species biofilms treated with antibiotic agents

Recently it was demonstrated that for urinary tract infections species with a lower or unproven pathogenic potential, such as Delftia tsuruhatensis and Achromobacter xylosoxidans, might interact with conventional pathogenic agents such as Escherichia coli. Here, single- and dual-species biofilms of these microorganisms were characterized in terms of microbial composition over time, the average fitness of E. coli, the spatial organization and the biofilm antimicrobial profile. The results revealed a positive impact of these species on the fitness of E. coli and a greater tolerance to the antibiotic agents. In dual-species biofilms exposed to antibiotics, E. coli was able to dominate the microbial consortia in spite of being the most sensitive strain. This is the first study demonstrating the protective effect of less common species over E. coli under adverse conditions imposed by the use of antibiotic agents.

Ingestão de plásticos por aves marinhas na Costa Norte de Portugal

Dissertação de mestrado em Ecologia