This research is concerned with some aspects of the retrieval of information from database systems by casual, naive computer users. A "casual user" is defined as an individual who only wishes to execute queries perhaps once or twice a month, and a "naive user" is someone who has little or no expertise in operating a computer and, more specifically for the purposes of this study, is not practiced at querying a database. The research initially focuses on a specific group of casual, naive users, namely a group of clinicians, and analyzes their characteristics as they pertain to the retrieval of information from a computer database. The characteristics thus elicited are then used to create the requirements for a database interface that would, potentially, be acceptable to this group. An interface having the desired requirements is then proposed. This interface consists, from a user's perspective, of three basic components. A graphical model gives a picture of the database structure. Windows give the ability to view different areas of the database, physically group together items that come under one logical heading and provide the user with immediate access to the data item names used by the system. Finally, a natural language query language provides a means of entering a query in a syntax (that of ordinary English) which is familiar to the user. The graphical model is a logical abstraction of the database. Unlike other database interfaces, it is not constrained by the model (relational, hierarchical, network) underlying the database management system, with the one caveat that the graphical model should not imply any connections which cannot be supported by the management system. Versions of the interface are implemented on both eight-bit and sixteen-bit microcomputers, and testing is conducted in order to validate the acceptability of the interface and to discover the level of graphical model which the users find most acceptable. The results of this testing are reported and further areas for research suggested.

INTRODUCTION: The legal and illicit use of cannabinoid-containing products is accelerating worldwide and is accompanied by increasing abuse problems. Due to legal issues, the USA will be entering a period of rapidly expanding recreational use of cannabinoids without the benefit of needed basic or clinical research. Most clinical cannabinoid research is focused on adults. However, the pediatric population is particularly vulnerable since the central nervous system is still undergoing developmental changes and is potentially susceptible to cannabinoid-induced alterations. RESEARCH DESIGN AND METHODS: This review focuses on the systems medicine of cannabinoids with emphasis on the need for future studies to include pediatric populations and mother-infant dyads. RESULTS AND CONCLUSION: Systems medicine integrates omics-derived data with traditional clinical medicine with the long-term goal of optimizing individualized patient care and providing proactive medical advice. Omics refers to large-scale data sets primarily derived from genomics, epigenomics, proteomics, and metabolomics.

Clinical diagnosis of chronic disease is a vital and challenging research problem which requires intensive clinical practice guidelines in order to ensure consistent and efficient patient care. Conventional medical diagnosis systems inculcate certain limitations, like complex diagnosis processes, lack of expertise, lack of well described procedures for conducting diagnoses, low computing skills, and so on. Automated clinical decision support system (CDSS) can help physicians and radiologists to overcome these challenges by combining the competency of radiologists and physicians with the capabilities of computers. CDSS depend on many techniques from the fields of image acquisition, image processing, pattern recognition, machine learning as well as optimization for medical data analysis to produce efficient diagnoses. In this dissertation, we discuss the current challenges in designing an efficient CDSS as well as a number of the latest techniques (while identifying best practices for each stage of the framework) to meet these challenges by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest and thus aiding in medical diagnosis. To meet these challenges, we propose an extension of conventional clinical decision support system framework, by incorporating artificial immune network (AIN) based hyper-parameter optimization as integral part of it. We applied the conventional as well as optimized CDSS on four case studies (most of them comprise medical images) for efficient medical diagnosis and compared the results. The first key contribution is the novel application of a local energy-based shape histogram (LESH) as the feature set for the recognition of abnormalities in mammograms. We investigated the implication of this technique for the mammogram datasets of the Mammographic Image Analysis Society and INbreast. In the evaluation, regions of interest were extracted from the mammograms, their LESH features were calculated, and they were fed to support vector machine (SVM) and echo state network (ESN) classifiers. In addition, the impact of selecting a subset of LESH features based on the classification performance was also observed and benchmarked against a state-of-the-art wavelet based feature extraction method. The second key contribution is to apply the LESH technique to detect lung cancer. The JSRT Digital Image Database of chest radiographs was selected for research experimentation. Prior to LESH feature extraction, we enhanced the radiograph images using a contrast limited adaptive histogram equalization (CLAHE) approach. Selected state-of-the-art cognitive machine learning classifiers, namely the extreme learning machine (ELM), SVM and ESN, were then applied using the LESH extracted features to enable the efficient diagnosis of a correct medical state (the existence of benign or malignant cancer) in the x-ray images. Comparative simulation results, evaluated using the classification accuracy performance measure, were further benchmarked against state-of-the-art wavelet based features, and authenticated the distinct capability of our proposed framework for enhancing the diagnosis outcome. As the third contribution, this thesis presents a novel technique for detecting breast cancer in volumetric medical images based on a three-dimensional (3D) LESH model. It is a hybrid approach, and combines the 3D LESH feature extraction technique with machine learning classifiers to detect breast cancer from MRI images. The proposed system applies CLAHE to the MRI images before extracting the 3D LESH features. Furthermore, a selected subset of features is fed to a machine learning classifier, namely the SVM, ELM or ESN, to detect abnormalities and to distinguish between different stages of abnormality. The results indicate the high performance of the proposed system. When compared with the wavelet-based feature extraction technique, statistical analysis testifies to the significance of our proposed algorithm. The fourth contribution is a novel application of the (AIN) for optimizing machine learning classification algorithms as part of CDSS. We employed our proposed technique in conjunction with selected machine learning classifiers, namely the ELM, SVM and ESN, and validated it using the benchmark medical datasets of PIMA India diabetes and BUPA liver disorders, two-dimensional (2D) medical images, namely MIAS and INbreast and JSRT chest radiographs, as well as on the three-dimensional TCGA-BRCA breast MRI dataset. The results were investigated using the classification accuracy measure and the learning time. We also compared our methodology with the benchmarked multi-objective genetic algorithm (ES)-based optimization technique. The results authenticate the potential of the AIN optimised CDSS.

Online health communities (OHCs) have become a medium for patients to share their personal experiences and interact with peers on topics related to a disease, medication, side effects, and therapeutic processes. Many studies show that using OHCs regularly decreases mortality and improves patients mental health. As a result of their benefits, OHCs are a popular place for patients to refer to, especially patients with a severe disease, and to receive emotional and informational support. The main reasons for developing OHCs are to present valid and high-quality information and to understand the mechanism of social support in changing patients' mental health. Given the purpose of OHC moderators for developing OHCs applications and the purpose of patients for using OHCs, there is no facility, feature, or sub-application in OHCs to satisfy patient and moderator goals. OHCs are only equipped with a primary search engine that is a keyword-based search tool. In other words, if a patient wants to obtain information about a side-effect, he/she needs to browse many threads in the hope that he/she can find several related comments. In the same way, OHC moderators cannot browse all information which is exchanged among patients to validate their accuracy. Thus, it is critical for OHCs to be equipped with computational tools which are supported by several sophisticated computational models that provide moderators and patients with the collection of messages that they need for making decisions or predictions. We present multiple computational models to alleviate the problem of OHCs in providing specific types of messages in response to the specific moderator and patient needs. Specifically, we focused on proposing computational models for the following tasks: identifying emotional support, which presents OHCs moderators, psychologists, and sociologists with insightful views on the emotional states of individuals and groups, and identifying informational support, which provides patients with an efficient and effective tool for accessing the best-fit messages from a huge amount of patient posts to satisfy their information needs, as well as provides OHC moderators, health-practitioners, nurses, and doctors with an insightful view about the current discussion under the topics of side-effects and therapeutic processes, giving them an opportunity to monitor and validate the exchange of information in OHCs. We proposed hybrid models that combine high-level, abstract features extracted from convolutional neural networks with lexicon-based features and features extracted from long short-term memory networks to capture the semantics of the data. We show that our models, with and without lexicon-based features, outperform strong baselines.

Despite major advances in treatment, pediatric cancers in the 5-16 age group remain the most common cause of disease death, and one out of eight children with cancer will not survive. Among children that do survive, some 60% suffer from late effects such as cancer recurrence and increased risk of obesity. This paper will provide a broad overview of pediatric oncology in the context of systems medicine. Systems medicine utilizes an integrative approach that relies on patient information gained from omics technology. A major goal of a systems medicine is to provide personalized medicine that optimizes positive outcomes while minimizing deleterious short and long-term sideeffects. There is an ever increasing development of effective cancer drugs, but a major challenge lies in picking the most effective drug for a particular patient. As detailed below, high-throughput omics technology holds the promise of solving this problem. Omics includes genomics, epigenomics, and proteomics. System medicine integrates omics information and provides detailed insights into disease mechanisms which can then inform the optimal treatment strategy.

This review will focus on a systems medicine approach to neonatal abstinence syndrome (NAS). Systems medicine utilizes information gained from the application of “omics” technology and bioinformatics (1). The omic approaches we will emphasize include genomics, epigenomics, proteomics, and metabolomics. The goals of systems medicine are to provide clinically relevant and objective insights into disease diagnosis, prognosis, and stratification as well as pharmacological strategies and evidence-based individualized clinical guidance. Despite the increasing incidence of NAS and its societal and economic costs, there has been only a very modest emphasis on utilizing a systems medicine approach, and this has been primarily in the areas of genomics and epigenomics. As detailed below, proteomics and metabolomics hold great promise in advancing our knowledge of NAS and its treatment. Metabolomics, in particular, can provide a quantitative assessment of the exposome, which is a comprehensive picture of both internal and external environmental factors affecting health.

Suite à l’avènement du Web 2.0, le rôle de l’internaute s’est vu modifier, passant de consommateur passif à acteur à part entière. De nouvelles fonctionnalités ont vu le jour augmentant considérablement les possibilités d’interaction avec le système. Parmi celles-ci, le collaborative tagging permet à l’utilisateur de décrire l’information en ligne par l’attribution de mots-clés (ou tags), la particularité étant que ces tags ne sont pas uniquement accessibles aux tagueurs eux-mêmes mais à l’ensemble des internautes. L’octroi de tags à une ressource lui offre donc de multiples chemins d’accès exploitables par la communauté internet tout entière. Régulièrement comparé à l’indexation « professionnelle », le collaborative tagging soulève une question essentielle :cette nouvelle pratique contribue-t-elle favorablement à la description et, par extension, à la recherche d’informations sur internet ?<p>Tous les types d’informations ne pouvant être étudiés, la présente dissertation se focalise sur l’information médicale scientifique utilisée par les médecins dans le cadre de leur pratique professionnelle. Elle propose, dans un premier temps, de mesurer le potentiel des tags assignés dans deux systèmes de collaborative tagging (Delicious et CiteULike) à décrire l’information en les comparant à des descripteurs attribués par des professionnels de l’information pour un même échantillon de ressources. La comparaison a mis en lumière l’exploitabilité des tags en termes de dispositifs de recherche d’informations mais a néanmoins révélé des faiblesses indéniables par rapport à une indexation réalisée par des professionnels à l’aide d’un langage contrôlé.<p>Dans un second temps, la dissertation s’est intéressée aux utilisateurs finaux en quête d’informations, c’est-à-dire les médecins, afin de déterminer dans quelle mesure un système de collaborative tagging (CiteULike) peut assister ces derniers lors de leur recherche d’informations scientifiques. Pour ce faire, des entretiens individuels combinant interview semi-structurée et expérimentation ont été organisés avec une vingtaine de médecins. Ils ont fourni des indications riches et variées quant à leur adoption effective ou potentielle d’un système de collaborative tagging dans le cadre de leurs pratiques informationnelles courantes.<p>Enfin, cette dissertation se propose d’aller au-delà de l’étude des tags et du phénomène de collaborative tagging dans son ensemble. Elle s’intéresse également aux compétences informationnelles des médecins observés en vue d’alimenter la réflexion sur les formations qui leur sont dispensées tout au long de leurs études mais également durant leur parcours professionnel. / Doctorat en Information et communication / info:eu-repo/semantics/nonPublished

The adoption of digital health technologies has dramatically changed the healthcare sector landscape and thus generates new opportunities to collect, capture, store, access and retrieve electronic personal health information (ePHI). With the introduction of digital health technologies and the digitisation of health data, an increasing number of hospitals and peripheral health facilities across the globe are transitioning from a paper-based environment to an electronic or paper-light environment. However, the growing use of digital health technologies within healthcare facilities has caused ePHI to be exposed to a variety of threats such as cyber security threats, human-related threats, technological threats and environmental threats. These threats have the potential to cause harm to hospital systems and severely compromise the integrity and confidentiality of ePHI. Because of the growing number of security threats, many hospitals, both private and public, are struggling to secure ePHI due to a lack of robust data security plans, systems and security control measures. The purpose of this study was to explore the security of electronic personal health information in a public hospital in South Africa. The study was underpinned by the interpretivism paradigm with qualitative data collected through semi-structured interviews with purposively selected IT technicians, network controllers’, administrative clerks and records management clerks, and triangulated with document and system analysis. Audio-recorded interviews were transcribed verbatim. Data was coded and analysed using ATLAS.ti, version 8 software, to generate themes and codes within the data, from which findings were derived. The key results revealed that the public hospital is witnessing a deluge of sophisticated cyber threats such as worm viruses, Trojan horses and shortcut viruses. This is compounded by technological threats such as power and system failure, network connection failure, obsolete computers and operating systems, and outdated hospital systems. However, defensive security measures such as data encryption, windows firewall, antivirus software and security audit log system exist in the public hospital for securing and protecting ePHI against threats and breaches. The study recommended the need to implement Intrusion Protection System (IPS), and constantly update the Windows firewall and antivirus program to protect hospital computers and networks against newly released viruses and other malicious codes. In addition to the use of password and username to control access to ePHI in the public hospital, the study recommends that the hospital should put in place authentication mechanisms such as biometric system and Radio Frequency Identification (RFID) system restrict access to ePHI, as well as to upgrade hospital computers and the Patient Administration and Billing (PAAB) System. In the absence of security policy, there is a need for the hospital to put in place a clear written security policy aimed at protecting ePHI. The study concluded that healthcare organisations should upgrade the security of their information systems to protect ePHI stored in databases against unauthorised access, malicious codes and other cyber-attacks. / Information Science / M. Inf. (Information Security)