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Determining factors associated with Exercise in 3 months Following Serious Heart stroke as well as Temporary Ischemic Assault within Individuals With House Discharge: An airplane pilot Review.
Today, the demand for health-oriented systems to facilitate and improve treatment processes is growing. For different information systems with different structures and technologies to be able to communicate with each other, a single gateway is required. The gateway acts as an interface between information systems and unifies protocols, rules, and standards related to communication processes. Health-related systems need a unique regulator that explains data models, coding, and data exchange structures. Moreover, the gateway has control over information systems and the data transmitted between them. In this paper, we explain an integrated gateway of health information exchange named DITAS which is a bridging point between health-related systems.Emerging diseases are a major public health problem as illustrated by the current coronavirus disease (COVID-19) pandemic. To make the right decisions, public health departments need a decision-making system. In Africa few IT systems have been put in place to help managers of public health in the analysis of their multidisciplinary data. The majority of digital health solutions are operational databases, as well, focused on surveillance activities that do not include the laboratory component. This paper describes the design model and implementation of data warehouse for dangerous pathogen monitoring in a laboratories network. Talend data integration is used to extract data in Excel sheets, transform it and load it into a MySQL database.The eHealth Digital Service Infrastructure (eHDSI) is an infrastructure ensuring the continuity of care for European citizens while they are travelling abroad in the EU. We present the Finnish readiness of implementing datasets of diagnosis, vaccinations and medication summary in a case study, and discuss challenges emerging from the national perspective. International harmonized standards are a key element in the smooth development of European information exchange.Malaria is still a real public health concern in Sub-Saharan African countries such as Senegal where it represents approximately 35% of the consultation activities in the hospitals. This is mainly due to the lack of appropriate medical care support and often late and error-prone diagnosis of the disease. For instance, largely used tools like Rapid Diagnosis Test are not fully reliable. This study proposes an extensive study of the efficiency of the most popular machine learning models for the task of Malaria occurrence prediction. We have considered patients from Senegal and have evaluated the overall accuracy of each considered algorithm based on sign and symptom information. Our main result is that machine learning algorithms are promising, in particular Naive Bayesian presents a recall very close to that of a rapid diagnostic test while improving highly its precision by 9%.Named Entity Recognition (NER) aims to identify and classify entities into predefined categories is a critical pre-processing task in Natural Language Processing (NLP) pipeline. Readily available off-the-shelf NER algorithms or programs are trained on a general corpus and often need to be retrained when applied on a different domain. The end model's performance depends on the quality of named entities generated by these NER models used in the NLP task. To improve NER model accuracy, researchers build domain-specific corpora for both model training and evaluation. However, in the clinical domain, there is a dearth of training data because of privacy reasons, forcing many studies to use NER models that are trained in the non-clinical domain to generate NER feature-set. Thus, influencing the performance of the downstream NLP tasks like information extraction and de-identification. In this paper, our objective is to create a high quality annotated clinical corpus for training NER models that can be easily generalizable and can be used in a downstream de-identification task to generate named entities feature-set.Although colonoscopy is the most frequently performed endoscopic procedure, the lack of standardized reporting is impeding clinical and translational research. Selleck Barasertib Inadequacies in data extraction from the raw, unstructured text in electronic health records (EHR) pose an additional challenge to procedure quality metric reporting, as vital details related to the procedure are stored in disparate documents. Currently, there is no EHR workflow that links these documents to the specific colonoscopy procedure, making the process of data extraction error prone. We hypothesize that extracting comprehensive colonoscopy quality metrics from consolidated procedure documents using computational linguistic techniques, and integrating it with discrete EHR data can improve quality of screening and cancer detection rate. As a first step, we developed an algorithm that links colonoscopy, pathology and imaging documents by analyzing the chronology of various orders placed relative to the colonoscopy procedure. The algorithm was installed and validated at the University of Arkansas for Medical Sciences (UAMS). The proposed algorithm in conjunction with Natural Language Processing (NLP) techniques can overcome current limitations of manual data abstraction.
Although electronic health records have been facilitating the management of medical information, there is still room for improvement in daily production of medical report. Possible areas for improvement would be to improve reports quality (by increasing exhaustivity), to improve patients' understanding (by mean of a graphical display), to save physicians' time (by helping reports writing), and to improve sharing and storage (by enhancing interoperability). We set up the ICIPEMIR project (Improving the completeness, interoperability and patients explanation of medical imaging reports) as an academic solution to optimize medical imaging reports production. Such a project requires two layers one engineering layer to build the automation process, and a second medical layer to determine domain-specific data models for each type of report. We describe here the medical layer of this project.

We designed a reproducible methodology to identify -for a given medical imaging exam- mandatory fields, and describe a corresponding simple data model using validated formats.
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