AIMC Topic: Machine Learning

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LiSep LSTM: A Machine Learning Algorithm for Early Detection of Septic Shock.

Scientific reports
Sepsis is a major health concern with global estimates of 31.5 million cases per year. Case fatality rates are still unacceptably high, and early detection and treatment is vital since it significantly reduces mortality rates for this condition. Appr...

Predicting rice blast disease: machine learning versus process-based models.

BMC bioinformatics
BACKGROUND: In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent...

Human mitochondrial genome compression using machine learning techniques.

Human genomics
BACKGROUND: In recent years, with the development of high-throughput genome sequencing technologies, a large amount of genome data has been generated, which has caused widespread concern about data storage and transmission costs. However, how to effe...

Applying Machine Learning Techniques in Nomogram Prediction and Analysis for SMILE Treatment.

American journal of ophthalmology
PURPOSE: To analyze the outcome of machine learning technique for prediction of small incision lenticule extraction (SMILE) nomogram.

Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.

The Lancet. Digital health
BACKGROUND: Cardiovascular outcomes for people with familial hypercholesterolaemia can be improved with diagnosis and medical management. However, 90% of individuals with familial hypercholesterolaemia remain undiagnosed in the USA. We aimed to accel...

Discrimination of the behavioural dynamics of visually impaired infants via deep learning.

Nature biomedical engineering
Sensory loss is associated with behavioural changes, but how behavioural dynamics change when a sensory modality is impaired remains unclear. Here, by recording under a designed standardized scenario, the behavioural phenotypes of 4,196 infants who e...

A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking.

Cells
P-glycoprotein (P-gp) is an important determinant of multidrug resistance (MDR) because its overexpression is associated with increased efflux of various established chemotherapy drugs in many clinically resistant and refractory tumors. This leads to...

The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system.

Scientific data
Machine learning has the potential to identify novel biological factors underlying successful antibody responses to influenza vaccines. The first attempts have revealed a high level of complexity in establishing influenza immunity, and many different...

An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data.

Sensors (Basel, Switzerland)
In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user's emotions from physiological data. Among a myriad of target emotions, boredom, ...

The diagnosticity of psychophysiological signatures: Can we disentangle mental workload from acute stress with ECG and fNIRS?

International journal of psychophysiology : official journal of the International Organization of Psychophysiology
The ability to identify reliable and sensitive physiological signatures of psychological dimensions is key to developing intelligent adaptive systems that may in turn help to mitigate human error in complex operations. The challenge of this endeavor ...