AIMC Topic: Machine Learning

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Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: With the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of ...

Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.

Scientific reports
Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery ...

Prognostic assessment capability of a five-gene signature in pancreatic cancer: a machine learning based-study.

BMC gastroenterology
BACKGROUND: A prognostic assessment method with good sensitivity and specificity plays an important role in the treatment of pancreatic cancer patients. Finding a way to evaluate the prognosis of pancreatic cancer is of great significance for the tre...

Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach.

Proteomics
The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, the...

Uncertainty maximization in partially observable domains: A cognitive perspective.

Neural networks : the official journal of the International Neural Network Society
Faced with an ever-increasing complexity of their domains of application, artificial learning agents are now able to scale up in their ability to process an overwhelming amount of data. However, this comes at the cost of encoding and processing an in...

How can machine learning and multiscale modeling benefit ocular drug development?

Advanced drug delivery reviews
The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug del...

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.

Cell
Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniq...

Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
The last decade has seen significant advances in the accumulation of medical data, the computational techniques to analyze that data, and corresponding improvements in management. Interventions such as thrombolytics and mechanical thrombectomy improv...

Machine learning for decision-making in cardiology: a narrative review to aid navigating the new landscape.

Revista espanola de cardiologia (English ed.)
Machine learning in cardiology is becoming more commonplace in the medical literature; however, machine learning models have yet to result in a widespread change in practice. This is partly due to the language used to describe machine, which is deriv...

QuantumTox: Utilizing quantum chemistry with ensemble learning for molecular toxicity prediction.

Computers in biology and medicine
Molecular toxicity prediction plays an important role in drug discovery, which is directly related to human health and drug fate. Accurately determining the toxicity of molecules can help weed out low-quality molecules in the early stage of drug disc...