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Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.

BMC medical informatics and decision making
BACKGROUND: Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predi...

Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data.

BMC medical informatics and decision making
BACKGROUND: With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only 'known' risk factors in pre...

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A.

BMC cancer
BACKGROUND: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence...

Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision.

PLoS computational biology
Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. The primate visual system, by contrast, contains abundant recurrent connections. Recurrent signal flow enables recycling of limited computat...

Engaging proactive control: Influences of diverse language experiences using insights from machine learning.

Journal of experimental psychology. General
We used insights from machine learning to address an important but contentious question: Is bilingual language experience associated with executive control abilities? Specifically, we assess proactive executive control for over 400 young adult biling...

Machine Learning Outcome Prediction in Dilated Cardiomyopathy Using Regional Left Ventricular Multiparametric Strain.

Annals of biomedical engineering
The clinical presentation of idiopathic dilated cardiomyopathy (IDCM) heart failure (HF) patients who will respond to medical therapy (responders) and those who will not (non-responders) is often similar. A machine learning (ML)-based clinical tool t...

Hybrid Assistive Limb® for sporadic inclusion body myositis: A case series.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
We evaluated the efficacy of rehabilitation therapy with Hybrid Assistive Limb® (HAL; hereafter HAL therapy) in three patients diagnosed with sporadic inclusion body myositis (sIBM) who were hospitalized to undergo HAL therapy. Among them, one patien...

Effectiveness of Radiofrequency Ablation in the Treatment of Painful Osseous Metastases: A Correlation Meta-Analysis with Machine Learning Cluster Identification.

Journal of vascular and interventional radiology : JVIR
A systematic review and meta-analysis of pain response after radiofrequency (RF) ablation over time for osseous metastases was conducted in 2019. Analysis used a random-effects model with GOSH plots and meta-regression. Fourteen studies comprising 42...