Necesidades de información de los médicos de atención primaria: análisis de preguntas y su resolución

González‐González, Ana Isabel; Mayor, Esperanza Escortell; Fernández, Tomás Hernández; Matéos, José; Sanz-Cuesta, Teresa; Fuertes, Rosario Riesgo

doi:10.1157/13074799

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…ej., dermatología, nutrición, etc.) mediante el uso de la taxonomía desarrollada por Ely et al 2 y traducida y adaptada por González-González et al 3,4 . No se incluyeron aquellas cuestiones verificables mediante la entrevista o la revisión de la historia clínica.…”

Section: Materials Y Métodosunclassified

Análisis de las dudas de los médicos de atención primaria

González¹,

Obanza²,

López³

et al. 2009

Atención Primaria

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Section: Materials Y Métodosunclassified

Análisis de las dudas de los médicos de atención primaria

González¹,

Obanza²,

López³

et al. 2009

Atención Primaria

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“…often less than 10 minutes). On the other hand, the required information by them is stored in many and different sources [3]. Envision yourself in a primary health care consultation.…”

Section: Introductionmentioning

confidence: 99%

“…(http://www.aemps.gob.es/). 3 MEDLINE comprises more than 26 million citations (January 2017). 4 Scopus includes more than 60 million records from journals and books (January 2017).…”

Section: Introductionmentioning

confidence: 99%

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DrugSemantics: A corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics

Moreno

Boldrini

Moreda

et al. 2017

Journal of Biomedical Informatics

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For the healthcare sector, it is critical to exploit the vast amount of textual health-related information. Nevertheless, healthcare providers have difficulties to benefit from such quantity of data during pharmacotherapeutic care. The problem is that such information is stored in different sources and their consultation time is limited. In this context, Natural Language Processing techniques can be applied to efficiently transform textual data into structured information so that it could be used in critical healthcare applications, being of help for physicians in their daily workload, such as: decision support systems, cohort identification, patient management, etc. Any development of these techniques requires annotated corpora. However, there is a lack of such resources in this domain and, in most cases, the few ones available concern English. This paper presents the definition and creation of DrugSemantics corpus, a collection of Summaries of Product Characteristics in Spanish. It was manually annotated with pharmacotherapeutic named entities, detailed in DrugSemantics annotation scheme. Annotators were a Registered Nurse (RN) and two students from the Degree in Nursing. The quality of DrugSemantics corpus has been assessed by measuring its annotation reliability (overall F=79.33% [95%CI: 78.35-80.31]), as well as its annotation precision (overall P=94.65% [95%CI: 94.11-95.19]). Besides, the gold-standard construction process is described in detail. In total, our corpus contains more than 2000 named entities, 780 sentences and 226,729 tokens. Last, a Named Entity Classification module trained on DrugSemantics is presented aiming at showing the quality of our corpus, as well as an example of how to use it.

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“…A fundamental bottleneck preventing large-scale automatic reuse of this information is that it is mostly encoded in natural language, i.e. free text written by medical practitioners in EHRs [3]. The traditional approach for knowledge extraction was, until very recently, to prestructure certain EHR systems so that only certain type of information is allowed in certain fields.…”

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confidence: 99%

Savana: Re-using Electronic Health Records with Artificial Intelligence

Medrano¹,

Guijarro²,

Belda³

et al. 2018

IJIMAI

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Past attempts to apply Artificial Intelligence (AI) to medical decision support systems have traditionally encountered a strong limitation in the complexity of human language [8]. Today, the state of the art of Natural KeywordsNatural Language Processing, Artificial Intelligence, E-Health, Machine Learning, Electronic Health Records. AbstractHealth information grows exponentially (doubling every 5 years), thus generating a sort of inflation of science, i.e. the generation of more knowledge than we can leverage. In an unprecedented data-driven shift, today doctors have no longer time to keep updated. This fact explains why only one in every five medical decisions is based strictly on evidence, which inevitably leads to variability. A good solution lies on clinical decision support systems, based on big data analysis. As the processing of large amounts of information gains relevance, automatic approaches become increasingly capable to see and correlate information further and better than the human mind can. In this context, healthcare professionals are increasingly counting on a new set of tools in order to deal with the growing information that becomes available to them on a daily basis. By allowing the grouping of collective knowledge and prioritizing "mindlines" against "guidelines", these support systems are among the most promising applications of big data in health. In this demo paper we introduce Savana, an AI-enabled system based on Natural Language Processing (NLP) and Neural Networks, capable of, for instance, the automatic expansion of medical terminologies, thus enabling the re-use of information expressed in natural language in clinical reports. This automatized and precise digital extraction allows the generation of a real time information engine, which is currently being deployed in healthcare institutions, as well as clinical research and management.

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Necesidades de información de los médicos de atención primaria: análisis de preguntas y su resolución

Abstract: Restrictions: Hawthorne effect or bias in the person observed and participants stage fright. Applicability: the results of the research will be used to plan the resources needed for doctors to enjoy and put into practice the best scientific knowledge.

Cited by 5 publications

References 8 publications

Análisis de las dudas de los médicos de atención primaria

Análisis de las dudas de los médicos de atención primaria

DrugSemantics: A corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics

Savana: Re-using Electronic Health Records with Artificial Intelligence

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