9 research outputs found

    Informatics for Health 2017: Advancing both science and practice

    Full text link

    Informatics for Health 2017 : advancing both science and practice

    Get PDF
    Conference report, The Informatics for Health congress, 24-26 April 2017, in Manchester, UK.Introduction : The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress. Discussion : The twin programmes of “Big Data” and “Digital Health” are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes. Conclusions : Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.Publisher PDFPeer reviewe

    Natural language processing (NLP) for clinical information extraction and healthcare research

    Get PDF
    Introduction: Epilepsy is a common disease with multiple comorbidities. Routinely collected health care data have been successfully used in epilepsy research, but they lack the level of detail needed for in-depth study of complex interactions between the aetiology, comorbidities, and treatment that affect patient outcomes. The aim of this work is to use natural language processing (NLP) technology to create detailed disease-specific datasets derived from the free text of clinic letters in order to enrich the information that is already available. Method: An NLP pipeline for the extraction of epilepsy clinical text (ExECT) was redeveloped to extract a wider range of variables. A gold standard annotation set for epilepsy clinic letters was created for the validation of the ExECT v2 output. A set of clinic letters from the Epi25 study was processed and the datasets produced were validated against Swansea Neurology Biobank records. A data linkage study investigating genetic influences on epilepsy outcomes using GP and hospital records was supplemented with the seizure frequency dataset produced by ExECT v2. Results: The validation of ExECT v2 produced overall precision, recall, and F1 score of 0.90, 0.86, and 0.88, respectively. A method of uploading, annotating, and linking genetic variant datasets within the SAIL databank was established. No significant differences in the genetic burden of rare and potentially damaging variants were observed between the individuals with vs without unscheduled admissions, and between individuals on monotherapy vs polytherapy. No significant difference was observed in the genetic burden between people who were seizure free for over a year and those who experienced at least one seizure a year. Conclusion: This work presents successful extraction of epilepsy clinical information and explores how this information can be used in epilepsy research. The approach taken in the development of ExECT v2, and the research linking the NLP outputs, routinely collected health care data, and genetics set the way for wider research

    Biomedical Literature Mining and Knowledge Discovery of Phenotyping Definitions

    Get PDF
    Indiana University-Purdue University Indianapolis (IUPUI)Phenotyping definitions are essential in cohort identification when conducting clinical research, but they become an obstacle when they are not readily available. Developing new definitions manually requires expert involvement that is labor-intensive, time-consuming, and unscalable. Moreover, automated approaches rely mostly on electronic health records’ data that suffer from bias, confounding, and incompleteness. Limited efforts established in utilizing text-mining and data-driven approaches to automate extraction and literature-based knowledge discovery of phenotyping definitions and to support their scalability. In this dissertation, we proposed a text-mining pipeline combining rule-based and machine-learning methods to automate retrieval, classification, and extraction of phenotyping definitions’ information from literature. To achieve this, we first developed an annotation guideline with ten dimensions to annotate sentences with evidence of phenotyping definitions' modalities, such as phenotypes and laboratories. Two annotators manually annotated a corpus of sentences (n=3,971) extracted from full-text observational studies’ methods sections (n=86). Percent and Kappa statistics showed high inter-annotator agreement on sentence-level annotations. Second, we constructed two validated text classifiers using our annotated corpora: abstract-level and full-text sentence-level. We applied the abstract-level classifier on a large-scale biomedical literature of over 20 million abstracts published between 1975 and 2018 to classify positive abstracts (n=459,406). After retrieving their full-texts (n=120,868), we extracted sentences from their methods sections and used the full-text sentence-level classifier to extract positive sentences (n=2,745,416). Third, we performed a literature-based discovery utilizing the positively classified sentences. Lexica-based methods were used to recognize medical concepts in these sentences (n=19,423). Co-occurrence and association methods were used to identify and rank phenotype candidates that are associated with a phenotype of interest. We derived 12,616,465 associations from our large-scale corpus. Our literature-based associations and large-scale corpus contribute in building new data-driven phenotyping definitions and expanding existing definitions with minimal expert involvement

    Preface

    Get PDF

    Usability analysis of contending electronic health record systems

    Get PDF
    In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

    Étapes préliminaires à l’élaboration de systèmes d’aide au diagnostic automatisé de l’hypoxémie aigüe pédiatrique

    Full text link
    L’insuffisance respiratoire hypoxémique aigüe (IRHA) est une des causes les plus fréquentes d’admission aux soins intensifs pédiatriques. Elle est liée à plusieurs mécanismes dont le plus grave est l’œdème pulmonaire lésionnel conduisant au syndrome de détresse respiratoire aigüe (SDRA) pédiatrique qui représente 5-10 % des patients admis aux soins intensifs. Actuellement, les recommandations internationales de prise en charge de l’IRHA et du SDRA sont sous-appliquées du fait d’un défaut de diagnostic ou d’un diagnostic tardif. Ceci est probablement en partie responsable d’une ventilation mécanique prolongée dans le SDRA pédiatrique. Afin d’améliorer les critères d’évaluation de l’IRHA chez les enfants et éventuellement leur devenir, les 3 objectifs de cette thèse sont d’améliorer le diagnostic précoce d’IRHA chez l’enfant, informatiser un score de gravité de défaillance d’organes (score PELOD-2) utilisable comme critère de jugement principal en recherche en remplacement de la mortalité qui est faible dans cette population et prédire la ventilation prolongée chez la population la plus fragile, les nouveau-nés. Pour réaliser ces objectifs, nous avons : 1) optimisé une base de données haute résolution temporelle unique au monde, 2) validé un indice continu d’oxygénation utilisable en temps réel et robuste à toutes les valeurs de saturations pulsées en oxygène, 3) validé une version informatisée du score PELOD-2 utilisable comme critère de jugement principal en recherche, 4) développé un modèle prédictif d’IRHA persistante dû à l’influenza et 5) proposé une définition de la ventilation prolongée en pédiatrie applicable quel que soit l’âge et le terme de l’enfant et 6) étudié le devenir des nouveau-nés ayant une ventilation prolongée et proposé un modèle prédictif du sous-groupe le plus grave. Les méthodes utilisées à travers ces différentes études ont associé la science des données massives pour le regroupement, la synchronisation et la normalisation des données continues. Nous avons également utilisé les statistiques descriptives, la régression linéaire et logistique, les forêts aléatoires et leurs dérivés, l’apprentissage profond et l’optimisation empirique d’équations mathématiques pour développer et valider des modèles prédictifs. L’interprétation des modèles et l’importance de chaque variable ont été quantifiées soit par l’analyse de leurs coefficients (statistiques conventionnelles) soit par permutation ou masquage des variables dans le cas de modèles d’apprentissage automatique. En conclusion, l’ensemble de ce travail, soit la reconnaissance et la pronostication automatique de l’IRHA chez l’enfant vont me permettre de développer, de valider et d’implanter un système d’aide à la décision en temps réel pour l’IRHA en pédiatrie.Acute hypoxemic respiratory failure (AHRF) is one of the most frequent causes of admission to pediatric intensive care units. It is related to several mechanisms, the most serious of which is lesional pulmonary edema leading to pediatric acute respiratory distress syndrome (ARDS), which accounts for 5–10% of patients admitted to intensive care. Currently, international guidelines for the management of ARDS are under-implemented due to failure to diagnose or late diagnosis. This is probably partly responsible for prolonged mechanical ventilation in pediatric ARDS. In order to improve the criteria for assessing AHRF in children and possibly their outcome, we aimed to improve the early diagnosis of ARDS in children, to automate an organ failure severity score (PELOD-2 score) that can be used as a primary endpoint in research to replace mortality, which is low in this population, and to predict prolonged ventilation in the most fragile population, neonates. To achieve these objectives, we have: 1) optimized a unique high temporal resolution database, 2) validated a continuous oxygenation index usable in real time and robust to all values of pulsed oxygen saturation, 3) validated a computerized version of the PELOD-2 score usable as a primary outcome in research, 4) developed a predictive model of persistent AHRF due to influenza and 5) proposed a definition of prolonged ventilation in pediatrics applicable regardless of the age and term of the child and 6) studied the outcome of newborns with prolonged ventilation and proposed a predictive model of the most severe subgroup. The methods used across these different studies combined big data science for clustering, synchronization, and normalization of continuous data. We also used descriptive statistics, linear and logistic regression, random forests and their derivatives, deep learning, and empirical optimization of mathematical equations to develop and validate predictive models. The interpretation of the models and the importance of each variable were quantified either by analyzing their coefficients (conventional statistics) or by permuting or masking the variables in the case of machine learning models. In conclusion, all this work, i.e. the recognition and automatic prognosis of AHRF in children will allow me to develop, validate and implement a real-time decision support system for AHRF in pediatrics
    corecore