AIMC Topic: Machine Learning

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Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.

Scientific reports
Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination o...

Protein transfer learning improves identification of heat shock protein families.

PloS one
Heat shock proteins (HSPs) play a pivotal role as molecular chaperones against unfavorable conditions. Although HSPs are of great importance, their computational identification remains a significant challenge. Previous studies have two major limitati...

Learning on knowledge graph dynamics provides an early warning of impactful research.

Nature biotechnology
The scientific ecosystem relies on citation-based metrics that provide only imperfect, inconsistent and easily manipulated measures of research quality. Here we describe DELPHI (Dynamic Early-warning by Learning to Predict High Impact), a framework t...

Machine learning for sperm selection.

Nature reviews. Urology
Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has rem...

Learning adaptive representations for entity recognition in the biomedical domain.

Journal of biomedical semantics
BACKGROUND: Named Entity Recognition is a common task in Natural Language Processing applications, whose purpose is to recognize named entities in textual documents. Several systems exist to solve this task in the biomedical domain, based on Natural ...

Automated Left Ventricular Dimension Assessment Using Artificial Intelligence Developed and Validated by a UK-Wide Collaborative.

Circulation. Cardiovascular imaging
BACKGROUND: requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardization of s...

Generative Adversarial Networks for Creating Synthetic Free-Text Medical Data: A Proposal for Collaborative Research and Re-use of Machine Learning Models.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Restrictions in sharing Patient Health Identifiers (PHI) limit cross-organizational re-use of free-text medical data. We leverage Generative Adversarial Networks (GAN) to produce synthetic unstructured free-text medical data with low re-identificatio...

Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, su...