Observação e análise numa equipa de futebol de elite : relatório de estágio na area de observação e análise do Benfica LAB, Sport Lisboa e Benfica, equipas profissionais, época 2014-2015

Este relatório teve como objetivo a sumarização das práticas realizadas de um estagiário na área de Observação e Análise de Jogo do Benfica Lab-Sport Lisboa e Benfica. Consiste neste trabalho a descrição das diversas tarefas operacionais e complementares realizadas por um estagiário na área já referida. Acrescendo a essas descrições, é apresentado em detalhe um estudo de investigação iniciado durante o processo de estágio, tendo sido todos os dados recolhidos nesse período e fornecidos quase exclusivamente pela área de observação e análise do Benfica Lab. Este estudo demonstrou existirem diferenças significativas em medidas de centralidade individuais (nível de intermediação numa rede social; influência numa rede social) da equipa A para a equipa B na mesma posição (posto especifico). A descrição detalhada de todo o processo envolvente à realização de uma conferência (relação com a comunidade), também está presente neste documento, evento esse que teve como seu principal objetivo demonstrar o “Processo de estágio Benfica Lab/ FMH – Observação e Análise de Jogo”.

Multiple time series anomaly detection based on compression and correlation analysis: a medical surveillance case study

In this paper, we present a novel anomaly detection framework for multiple heterogeneous yet correlated time series, such as the medical surveillance series data. In our framework, we propose an anomaly detection algorithm from the viewpoint of trend and correlation analysis. Moreover, to efficiently process huge amount of observed time series, a new clustering-based compression method is proposed. Experimental results indicate that our framework is more effective and efficient than its peers. © 2012 Springer-Verlag Berlin Heidelberg.

Integrating internet-of-things with the power of cloud computing and the intelligence of big Data analytics- a three layered approach

This paper is written through the vision on integrating Internet-of-Things (IoT) with the power of Cloud Computing and the intelligence of Big Data analytics. But integration of all these three cutting edge technologies is complex to understand. In this research we first provide a security centric view of three layered approach for understanding the technology, gaps and security issues. Then with a series of lab experiments on different hardware, we have collected performance data from all these three layers, combined these data together and finally applied modern machine learning algorithms to distinguish 18 different activities and cyber-attacks. From our experiments we find classification algorithm RandomForest can identify 93.9% attacks and activities in this complex environment. From the existing literature, no one has ever attempted similar experiment for cyber-attack detection for IoT neither with performance data nor with a three layered approach.

Can the direct medical cost of chronic disease be transferred across different countries? Using cost-of-illness studies on type 2 diabetes, epilepsy and schizophrenia as examples

OBJECTIVES: To systematically review cost of illness studies for schizophrenia (SC), epilepsy (EP) and type 2 diabetes mellitus (T2DM) and explore the transferability of direct medical cost across countries.

METHODS: A comprehensive literature search was performed to yield studies that estimated direct medical costs. A generalized linear model (GLM) with gamma distribution and log link was utilized to explore the variation in costs that accounted by the included factors. Both parametric (Random-effects model) and non-parametric (Boot-strapping) meta-analyses were performed to pool the converted raw cost data (expressed as percentage of GDP/capita of the country where the study was conducted).

RESULTS: In total, 93 articles were included (40 studies were for T2DM, 34 studies for EP and 19 studies for SC). Significant variances were detected inter- and intra-disease classes for the direct medical costs. Multivariate analysis identified that GDP/capita (p<0.05) was a significant factor contributing to the large variance in the cost results. Bootstrapping meta-analysis generated more conservative estimations with slightly wider 95% confidence intervals (CI) than the parametric meta-analysis, yielding a mean (95%CI) of 16.43% (11.32, 21.54) for T2DM, 36.17% (22.34, 50.00) for SC and 10.49% (7.86, 13.41) for EP.

CONCLUSIONS: Converting the raw cost data into percentage of GDP/capita of individual country was demonstrated to be a feasible approach to transfer the direct medical cost across countries. The approach from our study to obtain an estimated direct cost value along with the size of specific disease population from each jurisdiction could be used for a quick check on the economic burden of particular disease for countries without such data.

Medical burden, body mass index and the outcome of psychosocial interventions for bipolar depression

Objective: Individuals with bipolar disorder experience a disproportionately high incidence of medical co-morbidity and obesity. These health-related problems are a barrier to recovery from mood episodes and have been linked with unfavorable responses to pharmacological treatment. However, little is known about whether and how these characteristics affect responses to adjunctive psychotherapy. Method: Embedded in the Systematic Treatment Enhancement Program for Bipolar Disorder was a randomized controlled trial of psychotherapy for bipolar depression comparing the efficacy of intensive psychotherapy plus pharmacotherapy with collaborative care (a three-session psycho-educational intervention) plus pharmacotherapy. We conducted a post-hoc analysis to evaluate whether medical burden and body mass index predicted and/or moderated the likelihood of recovery and time until recovery from a depressive episode among patients in the two treatments. Results: Participants who had medical co-morbidity and body mass index data constituted 199 of the 293 patients in the original Systematic Treatment Enhancement Program for Bipolar Disorder trial. Higher medical burden predicted a lower likelihood of recovery from depression in both treatment conditions (odds ratio = 0.89), but did not moderate responses to intensive psychotherapy vs collaborative care. Intensive psychotherapy yielded superior recovery rates for individuals of normal body mass index (odds ratio= 2.39) compared with collaborative care, but not among individuals who were overweight or obese. Conclusion: Medical co-morbidity and body weight impacts symptom improvement and attention to this co-morbidity may inform the development of more personalized treatments for bipolar disorder.

Risk factors for incident delirium in an acute general medical setting: a retrospective case – control study

Aims and ObjectivesTo determine predisposing and precipitating risk factors for incident delirium in medical patients during an acute hospital admission.BackgroundIncident delirium is the most common complication of hospital admission for older patients. Up to 30% of hospitalised medical patients experience incident delirium. Determining risk factors for delirium is important for identifying patients that are most susceptible to incident delirium.DesignRetrospective case-control study with two controls per case.MethodsAn audit tool was used to review medical records of patients admitted to acute medical units for data regarding potential risk factors for delirium. Data was collected between August 2013 and March 2014 at three hospital sites of a healthcare organisation in Melbourne, Australia. Cases were 161 patients admitted to an acute medical ward and diagnosed with incident delirium between 1st January 2012 and 31st December 2013. Controls were 321 patients sampled from the acute medical population admitted within the same time range, stratified for admission location and who did not develop incident delirium during hospitalisation.ResultsIdentified using logistic regression modelling, predisposing risk factors for incident delirium were: dementia, cognitive impairment, functional impairment, previous delirium, and fracture on admission. Precipitating risk factors for incident delirium were: use of an indwelling catheter, adding more than three medications during admission and having an abnormal sodium level during admission.ConclusionsMultiple risk factors for incident delirium exist; patients with a history of delirium, dementia and cognitive impairment are at greatest risk of developing delirium during hospitalisation.Relevance to clinical practiceNurses and other health care professionals should be aware of patients that have one or more risk factors for incident delirium. Knowledge of risk factors for delirium has the potential to increase the recognition and understanding of patients who are vulnerable to delirium. Early recognition and prevention of delirium can contribute to improved patients safety and reduction in harm.

Predicting risk of suicide attempt using history of physical illnesses from electronic medical records

BACKGROUND: Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk.

OBJECTIVE: The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data.

METHODS: We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC).

RESULTS: The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians.

CONCLUSIONS: This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk.

Shining the light on the dark side of medical leadership - a qualitative study in Australia

Purpose The paper aims to explore the beliefs of doctors in leadership roles of the concept of "the dark side", using data collected from interviews carried out with 45 doctors in medical leadership roles across Australia. The paper looks at the beliefs from the perspectives of doctors who are already in leadership roles themselves; to identify potential barriers they might have encountered and to arrive at better-informed strategies to engage more doctors in the leadership of the Australian health system. The research question is: "What are the beliefs of medical leaders that form the key themes or dimensions of the negative perception of the 'dark side'?". Design/methodology/approach The paper analysed data from two similar qualitative studies examining medical leadership and engagement in Australia by the same author, in collaboration with other researchers, which used in-depth semi-structured interviews with 45 purposively sampled senior medical leaders in leadership roles across Australia in health services, private and public hospitals, professional associations and health departments. The data were analysed using deductive and inductive approaches through a coding framework based on the interview data and literature review, with all sections of coded data grouped into themes. Findings Medical leaders had four key beliefs about the "dark side" as perceived through the eyes of their own past clinical experience and/or their clinical colleagues. These four beliefs or dimensions of the negative perception colloquially known as "the dark side" are the belief that they lack both managerial and clinical credibility, they have confused identities, they may be in conflict with clinicians, their clinical colleagues lack insight into the complexities of medical leadership and, as a result, doctors are actively discouraged from making the transition from clinical practice to medical leadership roles in the first place. Research limitations/implications This research was conducted within the Western developed-nation setting of Australia and only involved interviews with doctors in medical leadership roles. The findings are therefore limited to the doctors' own perceptions of themselves based on their past experiences and beliefs. Future research involving doctors who have not chosen to transition to leadership roles, or other health practitioners in other settings, may provide a broader perspective. Also, this research was exploratory and descriptive in nature using qualitative methods, and quantitative research can be carried out in the future to extend this research for statistical generalisation. Practical implications The paper includes implications for health organisations, training providers, medical employers and health departments and describes a multi-prong strategy to address this important issue. Originality/value This paper fulfils an identified need to study the concept of "moving to the dark side" as a negative perception of medical leadership and contributes to the evidence in this under-researched area. This paper has used data from two similar studies, combined together for the first time, with new analysis and coding, looking at the concept of the "dark side" to discover new emergent findings.

User centric design, data analysis and performance of snowboard bindings

This study focuses on soft boot snowboard bindings by looking at how users interact with their binding and proposes a possible solution to overcome such issues. Snowboarding is a multibillion-dollar sport that has only reached mainstream in the last 30 years its levels of progression in technology have evolved in that time. However, snowboard bindings for the most part still consist of the same basic architecture in the last 20 years. This study was aimed at taking a user centric point of view and using additive manufacturing technologies to be able to generate a new snowboard binding that is completely adaptable to the user. The initial part of the study was a survey of 280 snowboarders focussing on preferences, style and habits. This survey was generated from over 15 nations with the vast majority of boarders on the snow for five to fifty days a year. Significant emphasis was placed on the relationship between boarder binding set-up and occurrence of pain and/or injury. From the detailed survey it was found that boarder's experienced pain in the front foot/toe area as a result from the toe strap being too tight. However boarders wanted tighter bindings to increase responsiveness. Survey data was compared to ankle and foot biomechanics to build a relationship to assess the problem of pain versus responsiveness. The design stage of the study was to develop a binding that overcame the over-tightening of the binding but still maintain equivalent or better responsiveness compared to traditional bindings. The resulting design integrated the snowboard boot much more into the design, by using the sole as a "semi-rigid" platform and locking it in laterally between the heel cup and the new toe strap arrangement. The new design developed using additive manufacturing techniques was tested via qualitative and quantitative measures in the snow and in the lab. It was found that using the new arrangement in a system resulted in no loss of performance or responsiveness to the user. Due to the design and manufacturing approach users have the ability to customise the design to their specific needs.

Secure and Privacy-aware Data Collection and Processing in Mobile Health Systems

Healthcare systems have assimilated information and communication technologies in order to improve the quality of healthcare and patient's experience at reduced costs. The increasing digitalization of people's health information raises however new threats regarding information security and privacy. Accidental or deliberate data breaches of health data may lead to societal pressures, embarrassment and discrimination. Information security and privacy are paramount to achieve high quality healthcare services, and further, to not harm individuals when providing care. With that in mind, we give special attention to the category of Mobile Health (mHealth) systems. That is, the use of mobile devices (e.g., mobile phones, sensors, PDAs) to support medical and public health. Such systems, have been particularly successful in developing countries, taking advantage of the flourishing mobile market and the need to expand the coverage of primary healthcare programs. Many mHealth initiatives, however, fail to address security and privacy issues. This, coupled with the lack of specific legislation for privacy and data protection in these countries, increases the risk of harm to individuals. The overall objective of this thesis is to enhance knowledge regarding the design of security and privacy technologies for mHealth systems. In particular, we deal with mHealth Data Collection Systems (MDCSs), which consists of mobile devices for collecting and reporting health-related data, replacing paper-based approaches for health surveys and surveillance. This thesis consists of publications contributing to mHealth security and privacy in various ways: with a comprehensive literature review about mHealth in Brazil; with the design of a security framework for MDCSs (SecourHealth); with the design of a MDCS (GeoHealth); with the design of Privacy Impact Assessment template for MDCSs; and with the study of ontology-based obfuscation and anonymisation functions for health data.

What Are Effective Program Characteristics of Self-Management Interventions in Patients With Heart Failure? : An Individual Patient Data Meta-analysis

Background To identify those characteristics of self-management interventions in patients with heart failure (HF) that are effective in influencing health-related quality of life, mortality, and hospitalizations. Methods and Results Randomized trials on self-management interventions conducted between January 1985 and June 2013 were identified and individual patient data were requested for meta-analysis. Generalized mixed effects models and Cox proportional hazard models including frailty terms were used to assess the relation between characteristics of interventions and health-related outcomes. Twenty randomized trials (5624 patients) were included. Longer intervention duration reduced mortality risk (hazard ratio 0.99, 95% confidence interval [CI] 0.97–0.999 per month increase in duration), risk of HF-related hospitalization (hazard ratio 0.98, 95% CI 0.96–0.99), and HF-related hospitalization at 6 months (risk ratio 0.96, 95% CI 0.92–0.995). Although results were not consistent across outcomes, interventions comprising standardized training of interventionists, peer contact, log keeping, or goal-setting skills appeared less effective than interventions without these characteristics. Conclusion No specific program characteristics were consistently associated with better effects of self-management interventions, but longer duration seemed to improve the effect of self-management interventions on several outcomes. Future research using factorial trial designs and process evaluations is needed to understand the working mechanism of specific program characteristics of self-management interventions in HF patients.

Big data, smart homes and ambient assisted living

Objectives: To discuss how current research in the area of smart homes and ambient assisted living will be influenced by the use of big data. Methods: A scoping review of literature published in scientific journals and conference proceedings was performed, focusing on smart homes, ambient assisted living and big data over the years 2011-2014. Results: The health and social care market has lagged behind other markets when it comes to the introduction of innovative IT solutions and the market faces a number of challenges as the use of big data will increase. First, there is a need for a sustainable and trustful information chain where the needed information can be transferred from all producers to all consumers in a structured way. Second, there is a need for big data strategies and policies to manage the new situation where information is handled and transferred independently of the place of the expertise. Finally, there is a possibility to develop new and innovative business models for a market that supports cloud computing, social media, crowdsourcing etc. Conclusions: The interdisciplinary area of big data, smart homes and ambient assisted living is no longer only of interest for IT developers, it is also of interest for decision makers as customers make more informed choices among today's services. In the future it will be of importance to make information usable for managers and improve decision making, tailor smart home services based on big data, develop new business models, increase competition and identify policies to ensure privacy, security and liability.

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.

Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge.

The evaluation of changes in Intervertebral Discs (IVDs) with 3D Magnetic Resonance (MR) Imaging (MRI) can be of interest for many clinical applications. This paper presents the evaluation of both IVD localization and IVD segmentation methods submitted to the Automatic 3D MRI IVD Localization and Segmentation challenge, held at the 2015 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2015) with an on-site competition. With the construction of a manually annotated reference data set composed of 25 3D T2-weighted MR images acquired from two different studies and the establishment of a standard validation framework, quantitative evaluation was performed to compare the results of methods submitted to the challenge. Experimental results show that overall the best localization method achieves a mean localization distance of 0.8 mm and the best segmentation method achieves a mean Dice of 91.8%, a mean average absolute distance of 1.1 mm and a mean Hausdorff distance of 4.3 mm, respectively. The strengths and drawbacks of each method are discussed, which provides insights into the performance of different IVD localization and segmentation methods.

Securely sharing dynamic medical information in e-health

This thesis has introduced an infrastructure to share dynamic medical data between mixed health care providers in a secure way, which could benefit the health care system as a whole. The study results of the universally data sharing into a varied patient information system prototypes.