Disease Ontology: a backbone for disease semantic integration

Schriml, Lynn M.; Arze, Cesar; Nadendla, Suvarna; Chang, Yu‐Wei; Mazaitis, Mark J.; Felix, Victor; Feng, Gang; Kibbe, Warren A.

doi:10.1093/nar/gkr972

Cited by 780 publications

(599 citation statements)

References 24 publications

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“…To obtain only the diseaserelated terms, we exploit the human disease terms and their synonyms from the Disease-Ontology (DO) [40], a collection of 8,707 unique disease-related terms. While the sentences referring to an image and their adjacent sentences have 50.08 words on average, the number of disease-related terms in the three consecutive sentences is 5.17 on average with a standard deviation of 2.5.…”

Section: Image-to-description Relation Mining and Matchingmentioning

confidence: 99%

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Interleaved text/image Deep Mining on a large-scale radiology database

Shin

Lü

Kim

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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Despite tremendous progress in computer vision, effective learning on very large-scale (> 100K patients) medical image databases has been vastly hindered. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system. Instead of using full 3D medical volumes, we focus on a collection of representative~216K 2D key images/slices (selected by clinicians for diagnostic reference) with text-driven scalar and vector labels. Our system interleaves between unsupervised learning (e.g., latent Dirichlet allocation, recurrent neural net language models) on document-and sentence-level texts to generate semantic labels and supervised learning via deep convolutional neural networks (CNNs) to map from images to label spaces. Disease-related key words can be predicted for radiology images in a retrieval manner. We have demonstrated promising quantitative and qualitative results. The large-scale datasets of extracted key images and their categorization, embedded vector labels and sentence descriptions can be harnessed to alleviate the deep learning "datahungry" obstacle in the medical domain.

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Section: Image-to-description Relation Mining and Matchingmentioning

confidence: 99%

“…An example illustration of how word sequences are learned for an image. Bi-grams are selected from the image's reference sentences containing disease-related terms from the disease ontology (DO) [40]. Each bi-gram is converted to a vector of Z ∈ R 256×2 to learn from an image.…”

Section: Image-to-description Relation Mining and Matchingmentioning

confidence: 99%

Interleaved text/image Deep Mining on a large-scale radiology database

Shin

Lü

Kim

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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“…As ontologies are used to capture domain knowledge in a formal and explicit way, they are a natural choice in document classification process. Ontologies have been used in a diverse range of domains from cultural heritage [4] to 3D modeling [5], ecommerce [6] to health services [7], human anatomy [8] to fraud detection [9] and cyber warfare [10] to agriculture [11]. Punitha et al argue that ontology augmentation can improve the document classification process significantly [12].…”

Section: Related Workmentioning

confidence: 99%

A Disaster Document Classification Technique Using Domain Specific Ontologies

Ilyas¹

2015

ijacsa

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Abstract-Manual data collection and entry is one of the bottlenecks in conventional disaster management information systems. Time is a critical factor in emergency situations and timely data collection and processing may help in saving several lives. An effective disaster management system needs to collect data from World Wide Web automatically. A prerequisite for data collection process is document classification mechanism to classify a particular document into different categories. Ontologies are formal bodies of knowledge used to capture machine understandable semantics of a domain of interest and have been used successfully to support document classification in various domains. This paper presents an ontology-based document classification technique for automatic data collection in a disaster management system. A general ontology of disasters is used that contains the description of several natural and manmade disasters. The proposed technique augments the conventional classification measures with the ontological knowledge to improve the precision of classification. A preliminary implementation of the proposed technique shows promising results with up to 10% overall improvement in precision when compared with conventional classification methods.

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“…For instance, GALEN (Generalised Architecture for Languages, Encyclopedia and Nomenclature in Medicine) [8], SNOMED (Systematized Nomenclature of Medicine) [9], GO (Gene Ontology) [10], Disease Ontology [11] are examples of terms that can be used to integrate information. GALEN is designed to be a re-usable application-independent and language-independent model of medical concepts.…”

Section: Related Workmentioning

confidence: 99%

A system for geoanalysis of clinical and geographical data

Canino

Guzzi

Tradigo

et al. 2014

Proceedings of the Third ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health

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Patients enrolled in clinical trials are regularly subject to biological analyses and related data is included in Electronic Medical Records (EMRs) to summarize patient health status and to support administrative information. Well defined protocols guide the bioanalytes studies on patients. Often EMRs also contain geographical data about patients, i.e. place of birth and place of living. The integration of geographical data and biological analytes may represent a meaningful way to extract hidden information from data. For instance, possible correlations among outlier patients and some feature of areas they live in.In collaboration with the University Hospital of Catanzaro, we designed a framework able to integrate and analyze biological analytes. The system is able to relate biological data to diagnosis codes and to analyze integrated data against geographic areas of interest. The aim is to show correlations among patients features (e.g. cluster of patients with similar profiles or outlier patients) and areas features (e.g. presence of power grids or polluted sites). In addition we present a study on correlations between cardiovascular diseases and water quality in Calabria.

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Disease Ontology: a backbone for disease semantic integration

Cited by 780 publications

References 24 publications

Interleaved text/image Deep Mining on a large-scale radiology database

Interleaved text/image Deep Mining on a large-scale radiology database

A Disaster Document Classification Technique Using Domain Specific Ontologies

A system for geoanalysis of clinical and geographical data

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