AIMC Topic: Humans

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Improving rare disease classification using imperfect knowledge graph.

BMC medical informatics and decision making
BACKGROUND: Accurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the lack of historical data poses...

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text.

BMC medical informatics and decision making
BACKGROUND: To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present ...

A study of deep learning methods for de-identification of clinical notes in cross-institute settings.

BMC medical informatics and decision making
BACKGROUND: De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in de...

Combining entity co-occurrence with specialized word embeddings to measure entity relation in Alzheimer's disease.

BMC medical informatics and decision making
BACKGROUND: Extracting useful information from biomedical literature plays an important role in the development of modern medicine. In natural language processing, there have been rigorous attempts to find meaningful relationships between entities au...

Natural language processing for populating lung cancer clinical research data.

BMC medical informatics and decision making
BACKGROUND: Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual extra...

Computational analysis of non-invasive deep brain stimulation based on interfering electric fields.

Physics in medicine and biology
Neuromodulation modalities are used as effective treatments for some brain disorders. Non-invasive deep brain stimulation (NDBS) via temporally interfering electric fields has emerged recently as a non-invasive strategy for electrically stimulating d...

A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks.

Physics in medicine and biology
Compared with conventional gastroscopy which is invasive and painful, wireless capsule endoscopy (WCE) can provide noninvasive examination of gastrointestinal (GI) tract. The WCE video can effectively support physicians to reach a diagnostic decision...

Removal of computed tomography ring artifacts via radial basis function artificial neural networks.

Physics in medicine and biology
Ring artifacts in computed tomography (CT) images are caused by the undesirable response of detector pixels, which leads to the degradation of CT images. Accordingly, it affects the image interpretation, post-processing, and quantitative analysis. In...

CNN-based PET sinogram repair to mitigate defective block detectors.

Physics in medicine and biology
Positron emission tomography (PET) scanners continue to increase sensitivity and axial coverage by adding an ever expanding array of block detectors. As they age, one or more block detectors may lose sensitivity due to a malfunction or component fail...