Abstract

Abstract
Numerous diagnostic decisions are made every day by healthcare professionals. Bayesian networks can provide a useful aid to the process, but learning their structure from data generally requires the absence of missing data, a common problem in medical data. We have studied missing data imputation using a step-wise nearest neighbors' algorithm, which we recommended given its limited impact on the assessed validity of structure learning Bayesian network classifiers for Obstructive Sleep Apnea diagnosis. © 2018 European Federation for Medical Informatics (EFMI) and IOS Press.

Abstract
Abiding to the law is, in some cases, a delicate balance between the rights of different players. Re-using health records is such a case. While the law grants reuse rights to public administration documents, in which health records produced in public health institutions are included, it also grants privacy to personal records. To safeguard a correct usage of data, public hospitals in Portugal employ jurists that are responsible for allowing or withholding access rights to health records. To help decision making, these jurists can consult the legal opinions issued by the national committee on public administration documents usage. While these legal opinions are of undeniable value, due to their doctrine contribution, they are only available in a format best suited from printing, forcing individual consultation of each document, with no option, whatsoever of clustered search, filtering or indexing, which are standard operations nowadays in a document management system. When having to decide on tens of data requests a day, it becomes unfeasible to consult the hundreds of legal opinions already available. With the objective to create a modern document management system, we devised an open, platform agnostic system that extracts and compiles the legal opinions, ex-tracts its contents and produces metadata, allowing for a fast searching and filtering of said legal opinions.

Abstract
Obstructive sleep apnea (OSA) is a significant sleep problem with various clinical presentations that have not been formally characterized. This poses critical challenges for its recognition, resulting in missed or delayed diagnosis. Recently, cluster analysis has been used in different clinical domains, particularly within numeric variables. We applied an extension of k-means to be used in categorical variables: k-modes, to identify groups of OSA patients. Demographic, physical examination, clinical history, and comorbidities characterization variables (n=46) were collected from 318 patients; missing values were all imputed with k-nearest neighbors (k-NN). Feature selection, through Chi-square test, was executed and 17 variables were inserted in cluster analysis, resulting in three clusters. Cluster 1 having an age between 65 and 90 years (54%), 78% of males, with the presence of diabetes and gastroesophageal reflux, and high OSA prevalence; Cluster 2 presented a lower percentage of OSA (46%), with middle-aged women without comorbidities, but with gastroesophageal reflux; and Cluster 3 was very similar to cluster 1, only differing in age (45-64) and comorbidities were not present. Our results suggest that there are different groups of OSA patients, creating the need to rethink the baseline characteristics of these patients before being sent to perform polysomnography (gold standard exam for diagnosis). © 2018 IEEE.

Abstract
In ubiquitous streaming data sources, such as sensor networks, clustering nodes by the data they produce gives insights on the phenomenon being monitored. However, centralized algorithms force communication and storage requirements to grow unbounded. This article presents L2GClust, an algorithm to compute local clusterings at each node as an approximation of the global clustering. L2GClust performs local clustering of the sources based on the moving average of each node's data over time: the moving average is approximated using memory-less statistics; clustering is based on the furthest-point algorithm applied to the centroids computed by the node's direct neighbors. Evaluation is performed both on synthetic and real sensor data, using a state-of-the-art sensor network simulator and measuring sensitivity to network size, number of clusters, cluster overlapping, and communication incompleteness. A high level of agreement was found between local and global clusterings, with special emphasis on separability agreement, while an overall robustness to incomplete communications emerged. Communication reduction was also theoretically shown, with communication ratios empirically evaluated for large networks. L2GClust is able to keep a good approximation of the global clustering, using less communication than a centralized alternative, supporting the recommendation to use local algorithms for distributed clustering of streaming data sources.

Abstract
The aim of this scoping review is to give an overview and appraisal of the development of outcome measurement throughout time and its present importance to healthcare and specifically to palliative care clinical practice. It is based on a search and search results of a published systematic review on implementing patient reported outcome measures in palliative care clinical practice. Medline, PsycInfo, Cumulative Index to Nursing and Allied Health Literature, Embase and British Nursing Index were systematically searched from 1985. Hand searching of reference lists for all included articles and relevant review articles was performed. A total of 3,863 articles were screened. Sixty were included in this scoping review. Outcome measurement has a long history in health care and some of the strongest advocates were Florence Nightingale for using patient outcomes besides mortality rates, Codman for the "end result idea" of evaluating the patient status one year after orthopaedic surgery, and Donabedian for taking Codman's work further and developing the structure-process-outcome model. The contribution of patient-centred outcome measurement is vast and paramount in education, audit and as an informative tool for healthcare professionals and decision makers. It is possible to collect these data nationwide which would then allow for cross country comparisons, as well as, economic evaluations in palliative care interventions to contribute to appropriate resource allocation.