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In the medical field, a doctor must have a comprehensive knowledge by reading and writing narrative documents, and he is responsible for every decision he takes for patients. Unfortunately, it is very tiring to read all necessary information about drugs, diseases and patients due to the large amount of documents that are increasing every day. Consequently, so many medical errors can happen and even kill people. Likewise, there is such an important field that can handle this problem, which is the information extraction. There are several important tasks in this field to extract the important and desired information from unstructured text written in natural language. The main principal tasks are named entity recognition and relation extraction since they can structure the text by extracting the relevant information. However, in order to treat the narrative text we should use natural language processing techniques to extract useful information and features. In our paper, we introduce and discuss the several techniques and solutions used in these tasks. Furthermore, we outline the challenges in information extraction from medical documents. In our knowledge, this is the most comprehensive survey in the literature with an experimental analysis and a suggestion for some uncovered directions.
RESUMEN
Objectives: The whole-genome sequence (WGS) of Klebsiella pneumoniae KP3771 isolate was characterized. This strain was recovered from the urine sample of an 80-year-old man hospitalized in an intensive care unit of the University Hospital Tahar Sfar in Tunisia. Materials and Methods: WGS using a MiSeq platform was used. The assembled genome was subjected to several software analyses. Results: K. pneumoniae KP3771 was resistant to all antibiotics but colistin and tigecycline. WGS analysis found 18 transmissible genes encoding resistance markers, including blaNDM-1 and blaCTX-M-15 genes, which were carried by four plasmids belonging to the Inc Ib, IIk, and R groups. Three families of genes encoding virulence factors were detected, including adhesins (fimH, fimA, fimB, fimC, mrkD, Kpn, and ycfM), siderophores (enterobactin, aerobactin, and yersiniabactin siderophores), and protectin/invasin (traT). The strain was assigned to the sequence type 147. Conclusions: This study describes the genome of a carbapenemase-producing K. pneumoniae clinical isolate recovered in Tunisia. Bacteria WGS has become the reference technology to address epidemiological issues; this high level of information is particularly well suited to enrich epidemiological workflows' output.
Asunto(s)
Farmacorresistencia Bacteriana Múltiple/genética , Genoma Bacteriano , Infecciones por Klebsiella/epidemiología , Klebsiella pneumoniae/genética , beta-Lactamasas/genética , Adhesinas Bacterianas/genética , Adhesinas Bacterianas/metabolismo , Antibacterianos/farmacología , Colistina/farmacología , Femenino , Expresión Génica , Hospitales , Humanos , Infecciones por Klebsiella/tratamiento farmacológico , Infecciones por Klebsiella/microbiología , Klebsiella pneumoniae/efectos de los fármacos , Klebsiella pneumoniae/aislamiento & purificación , Masculino , Pruebas de Sensibilidad Microbiana , Plásmidos/química , Plásmidos/metabolismo , Sideróforos/biosíntesis , Tigeciclina/farmacología , Túnez/epidemiología , Factores de Virulencia/genética , Factores de Virulencia/metabolismo , Secuenciación Completa del Genoma , beta-Lactamasas/metabolismoRESUMEN
This paper extends a neural model for causal reasoning to mechanize the monotonic class. Hence, the resulting model is able to solve multiple, varied causal problems in the open, independent, incompatibility and monotonic classes. First, additivity between causes is formalized as a fuzzy AND-ing process. Second, an activation mechanism called the "softmin" is developed to solve additive interactions. Third, the softmin is implemented within a neural architecture. Experimental results on real-world and artificial problems reveal a good performance of the model and should stimulate future research.