AIMC Topic: Machine Learning

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Dynamic HGI trajectories and their impact on survival in patients with sepsis: a machine learning prognostic model.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]
BACKGROUND: Previous studies have indicated a correlation between the glycosylated hemoglobin index (HGI) and the prognosis of patients with sepsis. However, the impact of its dynamic fluctuations on patient outcomes remains insufficiently explored. ...

Lactate/albumin ratio predicts mortality in critically ill COVID-19 patients: a retrospective machine learning study.

Scientific reports
Severe COVID-19 often progresses to critical illness, requiring accurate prognostic biomarkers. Lactate-to-albumin ratio (LAR) has been proposed as a novel indicator to estimate the likelihood of death. Using data from the MIMIC database, this retros...

Raman spectroscopy and machine learning for early detection of gastric cancer and Helicobacter pylori with gastric juice.

Scientific reports
Gastric cancer is a leading cause of cancer-related mortality and highlights the need for early detection of gastric cancer and Helicobacter pylori (HP) infection, which is a major risk factor. Early non-invasive and convenient diagnostic tools capab...

AQuaRef: machine learning accelerated quantum refinement of protein structures.

Nature communications
Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical data, which, in addition to being limited to known chemical enti...

Burden and risk factors of depression in seniors from 1990 to 2021: a multi-database study based on EMR mining methods.

Translational psychiatry
Depression in seniors is a growing public health concern worldwide. Despite the rising prevalence of depression in this demographic, comprehensive data on its burden and trends over an extended period remain limited. This study aims to assess the tre...

Rapid discrimination of and non-tuberculous mycobacteria disease via interpretive machine learning analysis of routine laboratory tests.

BMJ health & care informatics
OBJECTIVES: Rapid discrimination of infections caused by (MTB) and non-tuberculous mycobacteria (NTM) is crucial in clinical settings. Despite overlapping clinical and radiological features, the two require markedly different therapeutic approaches ...

Machine learning predictive system to predict the risk of developing pre-eclampsia.

BMJ health & care informatics
OBJECTIVES: To develop a machine learning (ML)-based predictive model for assessing the risk of pre-eclampsia using routinely collected clinical data.

Rapid and accurate prediction of cycloplegic refraction in Chinese children: development and validation of machine learning models.

Journal of global health
BACKGROUND: Uncorrected refractive error affects approximately 19 million children globally, resulting in preventable vision loss. However, cycloplegic refraction, the gold standard for assessment, remains largely inaccessible in low-resource setting...

E-Sort: empowering end-to-end neural network for multi-channel spike sorting with transfer learning and fast post-processing.

Journal of neural engineering
Spike sorting, which involves detecting and attributing spikes to their putative neurons from extracellular recordings, is a common process in electrophysiology and brain-computer interface systems. Recent advances in large-scale neural recording tec...

High throughput machine learning pipeline to characterize larval zebrafish motor behavior.

PloS one
Using machine learning, we developed models that rigorously detect and classify larval zebrafish spontaneous and stimulus-evoked behaviors in various well plate formats. Zebrafish are an ideal model system for investigating the neural substrates unde...