AIMC Topic: Reproducibility of Results

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Identifying mouse developmental essential genes using machine learning.

Disease models & mechanisms
The genes that are required for organismal survival are annotated as 'essential genes'. Identifying all the essential genes of an animal species can reveal critical functions that are needed during the development of the organism. To inform studies o...

A 6 Second Analytical Method for Quantitation of Tacrolimus in Whole Blood by Use of Laser Diode Thermal Desorption Tandem Mass Spectrometry.

The journal of applied laboratory medicine
BACKGROUND: Therapeutic drug monitoring of immunosuppressive drugs is imperative for organ transplant recipients. High-performance LC-MS/MS is considered gold standard; however, immunoassays provide rapid turnaround time. New technology was developed...

Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.

JAMA network open
IMPORTANCE: Accurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, usin...

Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Accurate image-based medical diagnosis relies upon adequate image quality and clarity. This has important implications for clinical diagnosis, and for emerging methods such as telemedicine and computer-based image analysis. In this study, we trained ...

Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography.

Echocardiography (Mount Kisco, N.Y.)
BACKGROUND: Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically-ill patients and for guiding decision making. The existing models, however, cannot be used during init...

Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes.

Journal of translational medicine
BACKGROUND: Increased systemic and local inflammation play a vital role in the pathophysiology of acute coronary syndrome. This study aimed to assess the usefulness of selected machine learning methods and hematological markers of inflammation in pre...

Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI.

European radiology
OBJECTIVE: To investigate the value of machine learning (ML)-based high-dimensional quantitative texture analysis (qTA) on T2-weighted magnetic resonance imaging (MRI) in predicting response to somatostatin analogues (SA) in acromegaly patients with ...