AIMC Topic: Machine Learning

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An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
BACKGROUND: We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. In the present study we ex...

Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database.

Journal of cardiac failure
BACKGROUND: Traditional statistical approaches to prediction of outcomes have drawbacks when applied to large clinical databases. It is hypothesized that machine learning methodologies might overcome these limitations by considering higher-dimensiona...

Continual lifelong learning with neural networks: A review.

Neural networks : the official journal of the International Neural Network Society
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together c...

Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study.

JMIR mHealth and uHealth
BACKGROUND: Wearable accelerometers have greatly improved measurement of physical activity, and the increasing popularity of smartwatches with inherent acceleration data collection suggest their potential use in the physical activity research domain;...

Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.

PloS one
Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity...

Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data.

World neurosurgery
OBJECTIVE: Isocitrate dehydrogenase 1 (IDH1) mutation status is an independent favorable prognostic factor for glioblastoma (GBM) and is usually determined by sequencing or immunohistochemistry. An accurate prediction of IDH1 mutation status via noni...

RAMS: Remote and automatic mammogram screening.

Computers in biology and medicine
About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, the...

Sprint Assessment Using Machine Learning and a Wearable Accelerometer.

Journal of applied biomechanics
Field-based sprint performance assessments rely on metrics derived from a simple model of sprinting dynamics parameterized by 2 constants, v and τ, which indicate a sprinter's maximal theoretical velocity and the time it takes to approach v, respecti...

Optimal adaptive control of drug dosing using integral reinforcement learning.

Mathematical biosciences
In this paper, a reinforcement learning (RL)-based optimal adaptive control approach is proposed for the continuous infusion of a sedative drug to maintain a required level of sedation. To illustrate the proposed method, we use the common anesthetic ...