AIMC Topic: Machine Learning

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Machine learning modeling for predicting hospital readmission following lumbar laminectomy.

Journal of neurosurgery. Spine
In BriefAuthors of this study analyzed hospital readmissions following laminectomy and developed predictive models to identify readmitted patients with an accuracy >95% when using all variables and >79% when using only predischarge variables. A model...

Contrast enhancement is a prognostic factor in IDH1/2 mutant, but not in wild-type WHO grade II/III glioma as confirmed by machine learning.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Mutation of the isocitrate dehydrogenase (IDH) gene and co-deletion on chromosome 1p/19q is becoming increasingly relevant for the evaluation of clinical outcome in glioma. Among the imaging parameters, contrast enhancement (CE) in WHO II...

Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning.

JAMA network open
IMPORTANCE: Despite data aggregation and removal of protected health information, there is concern that deidentified physical activity (PA) data collected from wearable devices can be reidentified. Organizations collecting or distributing such data s...

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

Nature communications
Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machi...

Discovering de novo peptide substrates for enzymes using machine learning.

Nature communications
The discovery of peptide substrates for enzymes with exclusive, selective activities is a central goal in chemical biology. In this paper, we develop a hybrid computational and biochemical method to rapidly optimize peptides for specific, orthogonal ...

SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fields.

BMC medical informatics and decision making
BACKGROUND: Disease named entity recognition (NER) is a fundamental step in information processing of medical texts. However, disease NER involves complex issues such as descriptive modifiers in actual practice. The accurate identification of disease...

An automated pipeline for analyzing medication event reports in clinical settings.

BMC medical informatics and decision making
BACKGROUND: Medication events in clinical settings are significant threats to patient safety. Analyzing and learning from the medication event reports is an important way to prevent the recurrence of these events. Currently, the analysis of medicatio...

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG.

BMC medical informatics and decision making
BACKGROUND: Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain ...

Correlation-Aware Sparse and Low-Rank Constrained Multi-Task Learning for Longitudinal Analysis of Alzheimer's Disease.

IEEE journal of biomedical and health informatics
Alzheimer's disease (AD), as a severe neurodegenerative disease, is now attracting more and more researchers' attention in the healthcare. With the development of magnetic resonance imaging (MRI), the neuroimaging-based longitudinal analysis is gradu...