AIMC Topic: Machine Learning

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Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters.

Medical physics
PURPOSE/OBJECTIVE(S): Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution ...

Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Scientific reports
Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We firs...

Predicting translational progress in biomedical research.

PLoS biology
Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish thi...

Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification.

PloS one
OBJECTIVES: An extended-wavelength diffuse reflectance spectroscopy (EWDRS) technique was evaluated for its ability to differentiate between and classify different skin and tissue types in an in vivo pig model.

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches.

PloS one
In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), ...

Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?

PloS one
BACKGROUND: Rare diseases (RD) result in a wide variety of clinical presentations, and this creates a significant diagnostic challenge for health care professionals. We hypothesized that there exist a set of consistent and shared phenomena among all ...

Identification of postoperative complications using electronic health record data and machine learning.

American journal of surgery
BACKGROUND: Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for ...

Partial Policy-Based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images.

IEEE transactions on medical imaging
Utilizing the idea of long-term cumulative return, reinforcement learning (RL) has shown remarkable performance in various fields. We follow the formulation of landmark localization in 3D medical images as an RL problem. Whereas value-based methods h...

Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction.

JACC. Heart failure
OBJECTIVES: This study sought to develop models for predicting mortality and heart failure (HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with...

Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.

Computers in biology and medicine
BACKGROUND: Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.