AIMC Topic: Machine Learning

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Predicting Future Perceived Wellness in Professional Soccer: The Role of Preceding Load and Wellness.

International journal of sports physiology and performance
PURPOSE: The influence of preceding load and future perceived wellness of professional soccer players is unexamined. This paper simultaneously evaluates the external load (EL) and internal load (IL) for different time frames in combination with prese...

Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance.

Neurosurgery
BACKGROUND: Current outcomes prediction tools are largely based on and limited by regression methods. Utilization of machine learning (ML) methods that can handle multiple diverse inputs could strengthen predictive abilities and improve patient outco...

Prediction of amyloid aggregation rates by machine learning and feature selection.

The Journal of chemical physics
A novel data-based machine learning algorithm for predicting amyloid aggregation rates is reported in this paper. Based on a highly nonlinear projection from 16 intrinsic features of a protein and 4 extrinsic features of the environment to the protei...

Early Nephrosis Detection Based on Deep Learning with Clinical Time-Series Data.

Studies in health technology and informatics
Nephrosis is disease characterized by abnormal protein loss from impaired kidney. We constructed early prediction model using machine learning from clinical time series data, that can predict onset of nephrosis for more than one month. Long short-ter...

Predicting Disease-Free Lung Cancer Survival Using Patient Reported Outcome (PRO) Measurements with Comparisons of Five Machine Learning Techniques (MLT).

Studies in health technology and informatics
The study was to develop the lung cancer patients' prediction model for predicting 5-year survival after completion of treatment by using Machine Learning Technology (MLT), adding patient reporting (PRO) measurements of lung cancer survivors to a var...

An Improvised Classification Model for Predicting Delirium.

Studies in health technology and informatics
With the vast increase of digital healthcare data, there is an opportunity to mine the data for understanding inherent health patterns. Although machine-learning techniques demonstrated their applications in healthcare to answer several questions, th...

Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer.

Studies in health technology and informatics
Clinical and pathological stage are defining parameters in oncology, which direct a patient's treatment options and prognosis. Pathology reports contain a wealth of staging information that is not stored in structured form in most electronic health r...

An Adversorial Approach to Enable Re-Use of Machine Learning Models and Collaborative Research Efforts Using Synthetic Unstructured Free-Text Medical Data.

Studies in health technology and informatics
We leverage Generative Adversarial Networks (GAN) to produce synthetic free-text medical data with low re-identification risk, and apply these to replicate machine learning solutions. We trained GAN models to generate free-text cancer pathology repor...

Skin Lesion Detection with Support Vector Machines on iOS Devices.

Studies in health technology and informatics
Automated wound detection has become a common issue in health care. A broad variety of image processing algorithms already exist, but they are very power consuming on mobile devices. Meanwhile the use of machine learning algorithms is on the rise and...