AIMC Topic: Machine Learning

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Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast over computed tomographies (CTs), without the ionizing radiation exposure. However, it requires the generation of a s...

A contemporary review of machine learning in otolaryngology-head and neck surgery.

The Laryngoscope
One of the key challenges with big data is leveraging the complex network of information to yield useful clinical insights. The confluence of massive amounts of health data and a desire to make inferences and insights on these data has produced a sub...

Contribution of Cardiovascular Reserve to Prognostic Categories of Heart Failure With Preserved Ejection Fraction: A Classification Based on Machine Learning.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
BACKGROUND: The authors used cluster analysis of data from cardiovascular domains associated with exercise intolerance to help define prognostic phenotypes of patients with heart failure with preserved ejection fraction (HFpEF).

Effect of incremental feature enrichment on healthcare text classification system: A machine learning paradigm.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Healthcare tweets are particularly challenging due to its sparse layout and its limited character size. Compared to previous method based on "bag of words" (BOW) model, this study uniquely identifies the enrichment protocol ...

Spiking Neural P Systems With Learning Functions.

IEEE transactions on nanobioscience
Spiking neural P systems (SN P systems) are a class of distributed and parallel neural-like computing models, inspired from the way neurons communicate by means of spikes. In this paper, a new variant of the systems, called SN P systems with learning...

An automated data verification approach for improving data quality in a clinical registry.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The quality of data is crucial for clinical registry studies as it impacts credibility. In the regular practice of most such studies, a vulnerability arises from researchers recording data on paper-based case report forms (C...

Artificial Intelligence Transforms the Future of Health Care.

The American journal of medicine
Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data...

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach.

Journal of neural engineering
OBJECTIVE: Sedation of neurocritically ill patients is one of the most challenging situation in ICUs. Quantitative knowledge on the sedation effect on brain activity in that complex scenario could help to uncover new markers for sedation assessment. ...

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification...

Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling.

Scientific reports
PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called "Iterative ...