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Information Storage and Retrieval

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Learning Inter-Sentence, Disorder-Centric, Biomedical Relationships from Medical Literature.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Relationships between disorders and their associated tests, treatments and symptoms underpin essential information needs of clinicians and can support biomedical knowledge bases, information retrieval and ultimately clinical decision support. These r...

Leveraging Contextual Information in Extracting Long Distance Relations from Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Relation extraction from biomedical text is important for clinical decision support applications. In post-marketing pharmacovigilance, for example, Adverse Drug Events (ADE) relate medical problems to the drugs that caused them and were the focus of ...

Achievability to Extract Specific Date Information for Cancer Research.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Accurate identification of temporal information such as date is crucial for advancing cancer research which often requires precise date information associated with related cancer events. However, there is a gap for existing natural language processin...

Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underreporting as current methodology relies heavily on manual chart review. The rapid adoption of electronic health records (EHRs) has the potential to allo...

Polyp fingerprint: automatic recognition of colorectal polyps' unique features.

Surgical endoscopy
BACKGROUND: Content-based image retrieval (CBIR) is an application of machine learning used to retrieve images by similarity on the basis of features. Our objective was to develop a CBIR system that could identify images containing the same polyp ('p...

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks.

Journal of chemical information and modeling
Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated, unsupervised method ...

Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition.

Journal of biomedical informatics
With the rapid advancement of technology and the necessity of processing large amounts of data, biomedical Named Entity Recognition (NER) has become an essential technique for information extraction in the biomedical field. NER, which is a sequence-l...

Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations.

Computational and mathematical methods in medicine
To achieve the robust high-performance computer-aided diagnosis systems for lymph nodes, CT images may be typically collected from multicenter data, which cause the isolated performance of the model based on different data source centers. The variabi...

The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports.

BMC medical informatics and decision making
BACKGROUND: There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management and prognosis of patients. ...