The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases

Clinical decision support systems (CDSS) aid health care professionals (HCP) in evaluating large sets of information and taking informed decisions during their clinical routine. CDSS are becoming particularly important in the perspective of precision medicine, when HCP need to consider growing amounts of data to create precise patient profiles for personalized diagnosis, treatment and outcome monitoring. In allergy care, several CDSS are being developed and investigated, mainly for respiratory allergic diseases. Although the proposed solutions address different stakeholders, the majority aims at facilitating evidence-based and shared decision-making, incorporating guidelines, and real-time clinical data. We offer here an overview on existing tools, new developments and novel concepts and discuss the potential of digital CDSS in improving prevention, diagnosis and monitoring of allergic diseases

Decision support systems for adoption in dental clinics: a survey

While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic

Signal Fusion and Semantic Similarity Evaluation for Social Media Based Adverse Drug Event Detection

Recent advancements in pharmacovigilance tasks have shown the usage of social media as a resource to obtain real-time signals for drug surveillance. Researchers demonstrated a good potential for the detection of Adverse Drug Events (ADEs) using social media much earlier than the traditional reporting systems maintained by official regulatory authorities like the United States Food and Drug Administration (FDA). Existing automated drug surveillance systems have used various types of social media channels and search query logs for monitoring ADE signals.;In this thesis, we address two key performance issues related to automated drug surveillance systems. The first is to improve the ADE signal detection by analyzing signals from multiple social media channels, and the second is usage of semantic similarity to evaluate ADE narratives detected by drug surveillance systems. Most current approaches for detecting ADEs from social media rely on a single channel: forums or microblogs or query logs. In this study we propose a new methodology to fuse signals from different social media channels. We use graphical causal models to discover potentially hidden connections between data channels, and then use such associations to generate signals for ADEs. Further, prior work have not emphasized much on the language of healthcare consumers, which is often casual and informal in expressing health issues on social media. There is a high potential to miss the semantic similarity between ADE terms extracted from social media and terms from formal official narratives when the two sets of terms do not share exact text. Thus, we exhibit the usage of semantic similarity to enhance accuracy of detected ADEs, and evaluated similarity measurement algorithms developed over biomedical vocabularies in ADE surveillance domain. We experimented on a dataset of drugs which had FDA black box warnings with a retrospective analysis spanning years 2008 to 2015. The results show a better detection rate and an improved performance in terms of precision, recall and timeliness using our proposed methods

MuCIGREF: multiple computer-interpretable guideline representation and execution framework for managing multimobidity care

Clinical Practice Guidelines (CPGs) supply evidence-based recommendations to healthcare professionals (HCPs) for the care of patients. Their use in clinical practice has many benefits for patients, HCPs and treating medical centres, such as enhancing the quality of care, and reducing unwanted care variations. However, there are many challenges limiting their implementations. Initially, CPGs predominantly consider a specific disease, and only few of them refer to multimorbidity (i.e. the presence of two or more health conditions in an individual) and they are not able to adapt to dynamic changes in patient health conditions. The manual management of guideline recommendations are also challenging since recommendations may adversely interact with each other due to their competing targets and/or they can be duplicated when multiple of them are concurrently applied to a multimorbid patient. These may result in undesired outcomes such as severe disability, increased hospitalisation costs and many others. Formalisation of CPGs into a Computer Interpretable Guideline (CIG) format, allows the guidelines to be interpreted and processed by computer applications, such as Clinical Decision Support Systems (CDSS). This enables provision of automated support to manage the limitations of guidelines. This thesis introduces a new approach for the problem of combining multiple concurrently implemented CIGs and their interrelations to manage multimorbidity care. MuCIGREF (Multiple Computer-Interpretable Guideline Representation and Execution Framework), is proposed whose specific objectives are to present (1) a novel multiple CIG representation language, MuCRL, where a generic ontology is developed to represent knowledge elements of CPGs and their interrelations, and to create the multimorbidity related associations between them. A systematic literature review is conducted to discover CPG representation requirements and gaps in multimorbidity care management. The ontology is built based on the synthesis of well-known ontology building lifecycle methodologies. Afterwards, the ontology is transformed to a metamodel to support the CIG execution phase; and (2) a novel real-time multiple CIG execution engine, MuCEE, where CIG models are dynamically combined to generate consistent and personalised care plans for multimorbid patients. MuCEE involves three modules as (i) CIG acquisition module, transfers CIGs to the personal care plan based on the patient’s health conditions and to supply CIG version control; (ii) parallel CIG execution module, combines concurrently implemented multiple CIGs by performing concurrency management, time-based synchronisation (e.g., multi-activity merging), modification, and timebased optimisation of clinical activities; and (iii) CIG verification module, checks missing information, and inconsistencies to support CIG execution phases. Rulebased execution algorithms are presented for each module. Afterwards, a set of verification and validation analyses are performed involving real-world multimorbidity cases studies and comparative analyses with existing works. The results show that the proposed framework can combine multiple CIGs and dynamically merge, optimise and modify multiple clinical activities of them involving patient data. This framework can be used to support HCPs in a CDSS setting to generate unified and personalised care recommendations for multimorbid patients while merging multiple guideline actions and eliminating care duplications to maintain their safety and supplying optimised health resource management, which may improve operational and cost efficiency in real world-cases, as well

On Applications of Relational Data

With the advances of technology and the popularity of the Internet, a large amount of data is being generated and collected. Much of these data is relational data, which describe how people and things, or entities, are related to one another. For example, data from sale transactions on e-commerce websites tell us which customers buy or view which products. Analyzing the known relationships from relational data can help us to discover knowledge that can benefit businesses, organizations, and our lives. For instance, learning the products that are commonly bought together allows businesses to recommend products to customers and increase their sales. Hidden or new relationships can also be inferred based on relational data. In addition, based on the connections among the entities, we can approximate the level of relatedness between two entities, even though their relationship may be hard to observe or quantify. This research aims to explore novel applications of relational data that will help to improve our life in various aspects, such as improving business operations, improving experiences in using online services, and improving health care services. In applying relational data in any domain, there are two common challenges. First, the size of the data can be massive, but many applications require that results are obtained within a short time. Second, relational data are often noisy and incomplete. Many relationships are extracted automatically from text resources, and hence they are prone to errors. Our goal is not only to propose novel applications of relational data but also to develop techniques and algorithms that will facilitate and make such applications practical. This work addresses three novel applications of relational data. The first application is to use relational data to improve user experiences in online video sharing services. Second, we propose the use of relational data to find entities that are closely related to one another. Such problems arise in various domains, such as product recommendation and query suggestion. Third, we propose the use of relational data to assist medical practitioners in drug prescription. For these applications, we introduce several techniques and algorithms to address the aforementioned challenges in using relational data. Our approaches are evaluated extensively to demonstrate their effectiveness. The approaches proposed in this work not only can be used in the specific applications we discuss but also can help to facilitate and promote the use of relational data in other application domains

Tiny microbes, enormous impacts: what matters in gut microbiome studies?

Many factors affect the microbiomes of humans, mice, and other mammals, but substantial challenges remain in determining which of these factors are of practical importance. Considering the relative effect sizes of both biological and technical covariates can help improve study design and the quality of biological conclusions. Care must be taken to avoid technical bias that can lead to incorrect biological conclusions. The presentation of quantitative effect sizes in addition to P values will improve our ability to perform meta-analysis and to evaluate potentially relevant biological effects. A better consideration of effect size and statistical power will lead to more robust biological conclusions in microbiome studies

Adverse Drug Event Detection, Causality Inference, Patient Communication and Translational Research

Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the FDA Adverse Event Reporting System reports. The ADEtector system employs novel natural language processing approaches for ADE detection and provides a user interface to display ADE information. The ADEtector employs machine learning techniques to automatically processes the narrative text and identify the adverse event (AE) and medication entities that appear in that narrative text. The system will analyze the entities recognized to infer the causal relation that exists between AEs and medications by automating the elements of Naranjo score using knowledge and rule based approaches. The Naranjo Adverse Drug Reaction Probability Scale is a validated tool for finding the causality of a drug induced adverse event or ADE. The scale calculates the likelihood of an adverse event related to drugs based on a list of weighted questions. The ADEtector also presents the user with evidence for ADEs by extracting figures that contain ADE related information from biomedical literature. A brief summary is generated for each of the figures that are extracted to help users better comprehend the figure. This will further enhance the user experience in understanding the ADE information better. The ADEtector also helps patients better understand the narrative text by recognizing complex medical jargon and abbreviations that appear in the text and providing definitions and explanations for them from external knowledge resources. This system could help clinicians and researchers in discovering novel ADEs and drug relations and also hypothesize new research questions within the ADE domain

Primary and secondary immunodeficiencies of the IL-12/IFN-γ axis

[eng] IL-12/IFN-γ axis is a principal pathway for intramacrophagic pathogens immunity such as leishmania or mycobacteria. Alterations in this axis, being both congenic (causing the primary immunodeficiency Mendelian Susceptibility to Mycobacterial Disease, MSMD) or acquired (treatment with anti-TNF-α drugs) cause susceptibility to this type of microbes. MSMD causes susceptibility mainly to non-pathogenic mycobacteria, salmonella and candida; besides, MSMD- causing mutations have been detected in Mycobacterium tuberculosis and Leishmania patients. On the other hand, the death of an anti-TNF-α in-utero exposed infant after BCG vaccination together with the effect of anti-TNF-α drugs in tuberculosis reactivation in adults and the known tole of TNF-α in B cell maturation reveal the need for an in-depth study of in-utero exposition to anti-TNF-α drugs. With that our hypothesis is that patients with extrapulmonary Mycobacterium tuberculosis infection or visceral leishmaniasis have a primary dysfunction of the IL-12/IFN-γ axis and that exposure to anti-TNF-α antibodies during whole pregnancy in children born to mothers with inflammatory bowel disease affects the normal development of the neonatal immune system, conferring a secondary immunodeficiency, which includes a dysfunction of the IL- 12/IFN-γ axis. Both extrapulmonary tuberculosis (n=23) and visceral leishmaniasis (n=24) patients presented alterations in the IL-12/IFN-γ pathway; however, we did not detect any complete defect. Concretely, the patients with extrapulmonary tuberculosis had a diminished response to IFN-γ while visceral leishmaniasis patients had a diminished production of IFN-g. Genetic study of these patients to unravel mutations causing partial forms of susceptibility to intramacrophagic infections is then needed. Besides, we detected an IL-12Rβ1 defect in a Peruvian patient that was misdiagnosed as multi-resistant tuberculosis, being a disseminated infection by the vaccine strain BCG. After the detection of the genetic defect, the patient was transferred to the National Institute of Health in the USA, where she received the appropriate treatment and the microbiological diagnosis was corrected resulting in the resolution of the infection. This case remarks the fact that suspicion of this forms of immune deficiency and their detection changes the prognostics and outcome of the patient. The study of the effect of anti-TNF-α on the exposed infant immune system (n=7) revealed a T and B cell maturation defect that was corrected at 12 months, normal cell proliferation after mitogen stimulation and normal immunoglobulin production and vaccine response without an increase of severe infections. On the other hand, Treg cell frequency was low in exposed infants, without reaching normalization at 12 months of age. Treg cell frequency in neonates inversely correlated with anti-TNF-α through level in the mother during third trimester of pregnancy and with T cell proliferation after a mild mitogen stimulation. These data with the increased atopia/allergy in the studied infants suggest the need of a long-term follow-up for Treg cells and the advent of immune dysregulation events. Antimycobacterial response was diminished in exposed infants and not totally recovered after washing the drug from the blood in the culture. On the other hand, coinciding with the decrease of the drug levels in blood, the production of IL-12, IFN-γ and TNF-α increased. We conclude that the effects of anti-TNF-α exposure during pregnancy are not permanent and that BCG vaccination in these population should be avoided until, at least, 12 months of age. By last, the transition between the intra- and extra-uterine world is a special life-situation where the immune system plays a major role. We studied it in healthy cord blood donors, with special attention to the IL-12/IFN-γ pathway and B cell compartment, including regulatory B cells (Breg). Breg cells, defined as CD24hiCD38hi B cells, were expanded in cord blood, with capacity to produce IL-10 and to inhibit IL-4 and IFN-γ production by T cells with a similar phenotype when compared with adult Bregs. Besides, response to mycobacterial challenge was diminished. Interestingly, the diminished production of IFN-γ was associated with Breg cell frequency, opening the door to new research studying the role of these cells in different neonatal conditions as well as in cord blood derived stem cell transplantation.[spa] Esta tesis explora la vía de IL-12/IFN-g, central en la inmunidad a gérmenes intramacrofágicos, en el contexto de defectos primarios y secundarios. Los defectos primarios en esta vía causan susceptibilidad mendeliana a las micobacterias (MSMD), una inmunodeficiencia primaria que cursa con susceptibilidad a micobacterias no patogénicas principalmente, pero en la que se han descrito pacientes con infecciones por Mycobacterium tuberculosis y con leishmaniasis. En este escenario, la hemos estudiado en pacientes pediátricos con tuberculosis extrapulmonar y leishmaniasis visceral, revelando que no existían defectos completos de la vía, pero sí una alteración funcional en ésta en los dos grupos de pacientes estudiados. Esto reveló la necesidad de un estudio genético exhaustivo para revelar defectos parciales causantes de esta susceptibilidad. El diagnóstico la deficiencia de IL-12Rβ1 en una niña con infección diseminada por BCG, inicialmente diagnosticada como tuberculosis multirresistente, permitió el tratamiento adecuado que llevó a su curación, mostrando la relevancia del diagnóstico temprano del MSMD. Por otro lado, el hecho que se describiera un caso de muerte tras la vacunación con BCG de un neonato expuesto a fármacos anti-TNF-α durante el embarazo hizo pensar que la exposición a estos fármacos durante el embarazo pudiera llevar a defectos en el sistema inmunitario del neonato. Tras su estudio, observamos una inmadurez transitoria del compartimiento B y T; por otro lado, la disminución de la frecuencia de células T reguladoras que no normalizó con la edad juntamente con un aumento de la presencia de atopia o alergia en este grupo. Además, observamos una disminución de la respuesta a micobacterias en los niños expuestos, que mejoró con la edad. Concluimos que los efectos de los niños expuestos a anti-TNF-α durante el embarazo no parecen ser permanentes y que la vacunación con BCG de esta población debe ser evitada hasta los 12 meses de edad. El estudio de sangre de cordón de neonato sano reveló un aumento de la población de células B reguladoras. Además, la frecuencia de estas células se asoció inversamente con la producción de IFN-γ tras el estímulo con micobacterias, que se encontró disminuido en el neonato. Abriendo la puerta a nuevas investigaciones para estudiar su papel en diferentes condiciones del neonato, así como en el trasplante de progenitores hematopoyéticos