Abstract
The rational development of new drugs is a complex and expensive process. A myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognised as the major hurdle behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship studies, using relational Machine Learning algorithms, proved already to be very useful in the complex process of rational drug design. However, a typical problem with those studies concerns the use of available repositories of previously studied molecules. It is quite often the case that those repositories are highly biased since they contain lots of molecules that are similar to each other. This results from the common practice where an expert chemist starts off with a lead molecule, presumed to have some potential, and then introduces small modifications to produce a set of similar molecules. Thus, the resulting sets have a kind of similarity bias. In this paper we assess the advantages of filtering out similar molecules in order to improve the application of relational learners in Structure-Activity Relationship (SAR) problems to predict toxicity. Furthermore, we also assess the advantage of using a relational learner to construct comprehensible models that may be quite valuable to bring insights into the workings of toxicity.

Abstract
Although IT governance practices (like ITIL, which recommends on the use of audit logs for proper service level management) are being introduced in many Hospitals to cope with increasing levels of information quality and safety requirements, the standard maturity levels of hospital IT departments is still not enough to reach the level of frequent use of audit logs. This paper aims to address the issues related to the existence of AT in patient records, describe the Hospitals scenario and to produce recommendations. Representatives from four hospitals were interviewed regarding the use of AT in their Hospital IS. Very few AT are known to exist in these hospitals (average of 1 per hospital in an estimate of 21 existing IS). CIOs should to be much more concerned with the existence and maintenance of AT. Recommendations include server clock synchronization and using advanced log visualization tools.

Abstract
The use of computer systems to aid clinical decision making is growing. Besides clinical practice, computer applications, decision support systems or technologies can be used during dentistry and periodontology learning. A research was made using 30 searching expressions, based on MeSH Terms. A total of 17 articles were selected from the initial 249. Dental Students' Ability to Assess Gingival Health Status Software (DAAGS) and Virtual Learning Environment (VLE) are two examples of computer programs used in Priodontology learning. 3D technologies, electronic devices and image analysis systems are tools used during periodontal diagnosis. Dental informatics and periodontology are extremely connected, because, many systems, technologies or electronic devices could be used during diagnose, treatment or pre-operative phase. Computer applications could also be used to improve learning skills during pre-clinical and clinical stages, and at same time other technologies as 3D can present more detailed data to clinician, leading to a correct decision.

Abstract
Prostate cancer is a common cancer worldwide and a leading cause of death. Radiotherapy is usually the first-line treatment for patients with slow-growing cancer that is confined to the prostate. In Radiation Therapy Planning (RTP), the recognition and outlining of clinical volumes in computed tomography (CT) images are one of the most time-consuming steps carried out by human experts. The aim of this review is to identify and summarize evidence of the use of automatic organ delineation of CT images for radiotherapy planning in prostate cancer. From the literature search, a total of seven studies, reported between 1994 and 2009, were selected. We associate the selected studies in order to compare results, in spite of their differences in methodology and outcome evaluators. Most of the studies conclude that the automatic approach is faster, while having equivalent accuracy to manual method. Concerning the observer's variability, automatic segmentation reaches significant gains in reproducibility. As future directions, it is recommended the improvement of the segmentation algorithms in the delineation of problematic soft tissues and future validation studies with large scale trials and possible studies of meta-analysis in the specific problems.

Abstract
The volume of health data is rising and health information technologies which include electronic health records are a promising solution, on data management and collection, to achieve greater quality outcomes. However, they often cause errors instead of preventing them. To study the main barriers to high quality data collection from electronic health records, a qualitative review study was conducted using 5 different database engines having only considered data quality and documentation issues, opportunities and challenges for proper data collection, electronic health records data and corresponding databases quality. It were included 16 articles from which data availability, format, accuracy and data accessibility were the most focused problems to address. Still, solutions are available: early recognition of those problems, well structured and designed EHRs, standard coding use, periodic accuracy monitoring and feedback and broad use of such systems for the most daily tasks possible, among others. Altogether they can improve EHR data quality for everyday use.

Abstract
With today's hospital demands and financial constraints, hospital inpatient bed management is becoming increasingly complex. The use of decision support systems could enable hospital staff and health decision makers to perform more focused management of the hospital inpatient beds, thus potentially reducing costs and inpatient length of stay. A literature review was carry out on both PubMed and ISI Web of Knowledge in order to identify studies evaluating the use of decision support systems when applied to hospital inpatient bed management. Two different approaches were identified: one approach based on the use of mathematical models to support the planning and allocation of hospital inpatient beds and another approach consisting in the utilization of information technologies to support timely inpatient placement. It was perceived that mathematical models could be safely used to model annual patient arrival rates and bed occupancy, thus forecasting hospital/department bed demand and underlying cost structures/revenues. It was also perceived that the use of bed management information systems provides hospital staff (administrative clerk, clinicians and housekeepers) with the necessary information to timely assess performance measures based on the hospital/department activity thus increasing resource effectiveness, optimizing established clinical pathways, reducing inpatient length of stay and associated costs.