AIMC Topic: Deep Learning

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Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method.

Scientific reports
Understanding genetic mechanism of complex diseases is a serious challenge. Existing methods often neglect the heterogeneity phenomenon of complex diseases, resulting in lack of power or low reproducibility. Addressing heterogeneity when detecting ep...

Deep Convolutional Hashing for Low-Dimensional Binary Embedding of Histopathological Images.

IEEE journal of biomedical and health informatics
Compact binary representations of histopa-thology images using hashing methods provide efficient approximate nearest neighbor search for direct visual query in large-scale databases. They can be utilized to measure the probability of the abnormality ...

Deep learning massively accelerates super-resolution localization microscopy.

Nature biotechnology
The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PA...

Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering pro...

Accurate identification of RNA editing sites from primitive sequence with deep neural networks.

Scientific reports
RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, tim...

Predicting Hospital Readmission via Cost-Sensitive Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
With increased use of electronic medical records (EMRs), data mining on medical data has great potential to improve the quality of hospital treatment and increase the survival rate of patients. Early readmission prediction enables early intervention,...

Clinical Named Entity Recognition Using Deep Learning Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. ...

A Multi-Task Framework for Monitoring Health Conditions via Attention-based Recurrent Neural Networks.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Monitoring the future health status of patients from the historical Electronic Health Record (EHR) is a core research topic in predictive healthcare. The most important challenges are to model the temporality of sequential EHR data and to interpret t...

Deep Learning Meets Biomedical Ontologies: Knowledge Embeddings for Epilepsy.

AMIA ... Annual Symposium proceedings. AMIA Symposium
While biomedical ontologies have traditionally been used to guide the identification of concepts or relations in biomedical data, recent advances in deep learning are able to capture high-quality knowledge from textual data and represent it in graphi...