Integrating images to patient electronic medical records through content-based retrieval techniques

Traina, Agma J. M.; Rosa, Natália Abdala; Traina, Caetano

doi:10.1109/cbms.2003.1212783

Cited by 15 publications

(3 citation statements)

References 12 publications

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“…A major goal remaining is to also integrate variables of features extracted from the imaging raw data, i.e., the pixel data. Several content-based image retrieval solutions in the context of dedicated image-oriented CDW and analysis systems have been developed [37][38][39][40][41][42]. However, such systems have not been comprehensively integrated into a productive hospital CDW, which remains an open task.…”

Section: Discussionmentioning

confidence: 99%

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

Kaspar

Liman²,

Morbach³

et al. 2022

J Digit Imaging

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This study aims to show the feasibility and benefit of single queries in a research data warehouse combining data from a hospital’s clinical and imaging systems. We used a comprehensive integration of a production picture archiving and communication system (PACS) with a clinical data warehouse (CDW) for research to create a system that allows data from both domains to be queried jointly with a single query. To achieve this, we mapped the DICOM information model to the extended entity–attribute–value (EAV) data model of a CDW, which allows data linkage and query constraints on multiple levels: the patient, the encounter, a document, and a group level. Accordingly, we have integrated DICOM metadata directly into CDW and linked it to existing clinical data. We included data collected in 2016 and 2017 from the Department of Internal Medicine in this analysis for two query inquiries from researchers targeting research about a disease and in radiology. We obtained quantitative information about the current availability of combinations of clinical and imaging data using a single multilevel query compiled for each query inquiry. We compared these multilevel query results to results that linked data at a single level, resulting in a quantitative representation of results that was up to 112% and 573% higher. An EAV data model can be extended to store data from clinical systems and PACS on multiple levels to enable combined querying with a single query to quickly display actual frequency data.

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Section: Discussionmentioning

confidence: 99%

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

Kaspar

Liman²,

Morbach³

et al. 2022

J Digit Imaging

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“…Traina and Rosa proposed a system that is used to retrieve the image-based content of electronic health records. The Similarity Retrieval of Images System (SRIS) is designed in such a way that it can perform a similarity query over images containing the patient's data [10].…”

Section: Information Retrieval and Electronic Health Recordsmentioning

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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“…So, the information can be viewed anytime, subjected to some processing or queries. After extracting all needed features from images, it will be stored in SQL database server to be used in searching process, and then the database is transported to computation nodes and partitioned to equally sized subsets [17,18]. Some extracted DICOM information can be observed in figure 4.…”

Section: Fig 4: Dicom Tagsmentioning

confidence: 99%

A Grid based Medical Image Management System using Alchemi

Maghraby¹,

Faheem²,

Roushdy³

2013

IJCA

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A large number of medical images in digital format is generated by hospitals every day. It is acknowledged that medical image databases are a key component in diagnosis. The increasing trend towards digitization of medical images creates a need of technologies for storage, and retrieval of medical images. The paper discusses how to query the medical images and presented a medical images management system based on the DICOM (Digital Imaging and Communications in Medicine) standard .Grids are a promising tool to build medical databases and to face health-related challenges involving computations over large datasets. Indeed, grids offer an infrastructure for sharing data and building virtual databases distributed over several medical sites and sharing processing power. Alchemi grid framework has been deployed to provide grid-based environment. Speeding up the retrieval and feature extraction processes was one of the major achievements of this work.

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Integrating images to patient electronic medical records through content-based retrieval techniques

Cited by 15 publications

References 12 publications

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

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

A Grid based Medical Image Management System using Alchemi

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