AIMC Topic: Machine Learning

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Localization of Ventricular Activation Origin from the 12-Lead ECG: A Comparison of Linear Regression with Non-Linear Methods of Machine Learning.

Annals of biomedical engineering
We have previously developed an automated localization method based on multiple linear regression (MLR) model to estimate the activation origin on a generic left-ventricular (LV) endocardial surface in real time from the 12-lead ECG. The present stud...

Machine learning based classification of cells into chronological stages using single-cell transcriptomics.

Scientific reports
Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular...

Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals.

PloS one
BACKGROUND: The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a ...

The application of machine learning algorithms in understanding the effect of core/shell technique on improving powder compactability.

International journal of pharmaceutics
This study systemically investigated the application of core/shell technique to improve powder compactability. A 28-run Design-of-Experiment (DoE) was conducted to evaluate the effects of the type of core and shell materials and their concentrations ...

Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units.

Scientific reports
Unplanned extubation (UE) can be associated with fatal outcome; however, an accurate model for predicting the mortality of UE patients in intensive care units (ICU) is lacking. Therefore, we aim to compare the performances of various machine learning...

Machine learning approaches for predicting high cost high need patient expenditures in health care.

Biomedical engineering online
BACKGROUND: This paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients.

Machine learning in critical care: state-of-the-art and a sepsis case study.

Biomedical engineering online
BACKGROUND: Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the dauntin...

A dataset of clinically generated visual questions and answers about radiology images.

Scientific data
Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer. Automated systems could help clinicians cope with large amounts of images by answering questions about the image contents. An emerging area...

Automated classification of benign and malignant lesions in F-NaF PET/CT images using machine learning.

Physics in medicine and biology
PURPOSE: F-NaF PET/CT imaging of bone metastases is confounded by tracer uptake in benign diseases, such as osteoarthritis. The goal of this work was to develop an automated bone lesion classification algorithm to classify lesions in NaF PET/CT image...

Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image.

Physics in medicine and biology
In some clinical applications, prior normal-dose CT (NdCT) images are available, and the valuable textures and structure features in them may be used to promote follow-up low-dose CT (LdCT) reconstruction. This study aims to learn texture information...