Information retrieval in medicine: The electronic medical record as a new domain

Smith, Catherine Arnott

doi:10.1002/meet.1450430190

Cited by 5 publications

(4 citation statements)

References 47 publications

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“…Pour qu'elles soient utilisables elles doivent être facilement retrouvées au mieux par des fonctions de recherche d'information qui font actuellement défaut. En effet, Smith [17] en 2008 ne retrouve dans la littérature que deux articles centrés sur la RI dans le DPI. Plus récemment, les travaux de Dinh [18,19] et de Currie [20] concernent cette problématique.…”

Section: Discussionunclassified

Un modèle de données adapté à la recherche d’information dans le dossier patient informatisé: Étude, conception et évaluation

Dibad

Soualmia

Merabti

et al. 2011

Informatique Et Santé

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Background: Electronic Health Records (EHRs) have an important role in medical decision making and are considered as being a major part of medical support system. To ensure this role effectively, the information must be easily found, even in voluminous and large number of EHRs. This requires to develop search capabilities for EHR information retrieval (IR). Methods: To perform this, we propose a data model adapted to IR. The data analysis of EHRs of the Rouen University Hospital led us to consider them as being a set of informational elements linked by temporal and conceptual relationships. This model consists of two main entities to manage the information elements and their metadata. After implementation, we have evaluated the capacity of the proposed model to take into account all data from the EHRs and its adaptation to IR. Results: We have performed 25 queries on EHR (20 correspond to single patient and 5 to multi-patients). The results in 19 cases

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Section: Discussionunclassified

Un modèle de données adapté à la recherche d’information dans le dossier patient informatisé: Étude, conception et évaluation

Dibad

Soualmia

Merabti

et al. 2011

Informatique Et Santé

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“…Annotators also identified specific concept attributes describing contextual features [ 12 ] (Figure 3 ): 1) negation (found, negated, hypothetical); 2) temporality (historic, recent); 3) patient experiencer (patient, 1 st degree relative, 2 nd degree relative); 4) reason for service (acute, chronic, unknown); 5) the specific note section in which the concept was found; 5) three concept attributes describing granularity, relevance, and ambiguity [ 13 ]. We extend these last three additional properties from the information retrieval [ 14 , 15 ] and terminology literature [ 16 , 17 ] and define them as they were applied to the annotation task as follows:…”

Section: Methodsmentioning

confidence: 99%

Developing a manually annotated clinical document corpus to identify phenotypic information for inflammatory bowel disease

et al. 2009

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Background: Natural Language Processing (NLP) systems can be used for specific Information Extraction (IE) tasks such as extracting phenotypic data from the electronic medical record (EMR). These data are useful for translational research and are often found only in free text clinical notes. A key required step for IE is the manual annotation of clinical corpora and the creation of a reference standard for (1) training and validation tasks and (2) to focus and clarify NLP system requirements. These tasks are time consuming, expensive, and require considerable effort on the part of human reviewers.

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“…Within the medical field, information retrieval is involved with the extraction of information related to the patient's records. In the modern-day context, medical information is obtained from the web, social media, hospital records, and journal articles [2]. The amount of data found in Electronic Health Records (EHRs) gives rich information about diseases and historical medical information that could be used to understand better and treat many medical cases [3].…”

Section: Introductionmentioning

confidence: 99%

Natural Language Processing and Parallel Computing for Information Retrieval from Electronic Health Records

2022

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In this paper, we review the literature to find suitable information retrieval techniques for EHealth. Also discussed NLP techniques that have been proved their capability to extract valuable information in unstructured data from EHR. One of the best NLP techniques used for searching free text is LSI, due to its capability of finding semantic terms and in rich the search results by finding the hidden relations between terms. LSI uses a mathematical model called SVD, which is not scalable for large amounts of data due to its complexity and exhausts the memory, and a review for recent applications of LSI was discussed.

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Information retrieval in medicine: The electronic medical record as a new domain

Cited by 5 publications

References 47 publications

Un modèle de données adapté à la recherche d’information dans le dossier patient informatisé: Étude, conception et évaluation

Un modèle de données adapté à la recherche d’information dans le dossier patient informatisé: Étude, conception et évaluation

Developing a manually annotated clinical document corpus to identify phenotypic information for inflammatory bowel disease

Natural Language Processing and Parallel Computing for Information Retrieval from Electronic Health Records

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