AIMC Topic: Algorithms

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Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner.

BMC medical informatics and decision making
BACKGROUND: The medical community uses a variety of data standards for both clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) represent one such standard that provides robust data point definitions. Another standard is the ...

Detecting prolonged sitting bouts with the ActiGraph GT3X.

Scandinavian journal of medicine & science in sports
The ActiGraph has a high ability to measure physical activity; however, it lacks an accurate posture classification to measure sedentary behavior. The aim of the present study was to develop an ActiGraph (waist-worn, 30 Hz) posture classification to ...

Automatic cancer tissue detection using multispectral photoacoustic imaging.

International journal of computer assisted radiology and surgery
PURPOSE: In the case of multispecimen study to locate cancer regions, such as in thyroidectomy and prostatectomy, a significant labor-intensive processing is required at a high cost. Pathology diagnosis is usually done by a pathologist observing tiss...

Comparing different supervised machine learning algorithms for disease prediction.

BMC medical informatics and decision making
BACKGROUND: Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key tren...

Image processing techniques represent innovative tools for comparative analysis of proteins.

Computers in biology and medicine
Different bioinformatic and data-mining approaches have been used for the analysis of proteins. Here, we describe a novel, robust, and reliable approach for comparative analysis of a large number of proteins by combining Image Processing Techniques a...

DeepCQ: Deep multi-task conditional quantification network for estimation of left ventricle parameters.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic cardiac left ventricle (LV) quantification plays an important role in assessing cardiac function. Although many advanced methods have been put forward to quantify related LV parameters, automatic cardiac LV quantif...

The impact of extraneous features on the performance of recurrent neural network models in clinical tasks.

Journal of biomedical informatics
Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables or features are useful in predicting clinical outcomes can be chall...

A deep learning approach for converting prompt gamma images to proton dose distributions: A Monte Carlo simulation study.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: In proton therapy, imaging prompt gamma (PG) rays has the potential to verify proton dose (PD) distribution. Despite the fact that there is a strong correlation between the gamma-ray emission and PD, they are still different in terms of the ...

Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification.

Scientific reports
Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping...

Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data.

International journal of molecular sciences
Advances in flow cytometry enable the acquisition of large and high-dimensional data sets per patient. Novel computational techniques allow the visualization of structures in these data and, finally, the identification of relevant subgroups. Correct ...