AIMC Topic: Support Vector Machine

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Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and future prospects.

Biosensors & bioelectronics
Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial suscept...

Evaluating the performance of random forest, support vector machine, gradient tree boost, and CART for improved crop-type monitoring using greenest pixel composite in Google Earth Engine.

Environmental monitoring and assessment
The development of machine learning algorithms, along with high-resolution satellite datasets, aids in improved agriculture monitoring and mapping. Nevertheless, the use of high-resolution optical satellite datasets is usually constrained by clouds a...

Development and validation comparison of multiple models for perioperative neurocognitive disorders during hip arthroplasty.

Scientific reports
This study aims to develop optimal predictive models for perioperative neurocognitive disorders (PND) in hip arthroplasty patients, thereby advancing clinical practice. Data from all hip arthroplasty patients in the MIMIC-IV database were utilized to...

Detection of freely moving thoughts using SVM and EEG signals.

Journal of neural engineering
Freely moving thought is a type of thinking that shifts from one topic to another without any overarching direction or aim. The ability to detect when freely moving thought occurs may help us promote its beneficial outcomes, such as for creative thin...

Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.

Experimental biology and medicine (Maywood, N.J.)
Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overd...

Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.

European journal of medical research
OBJECTIVES: This study aimed to identify risk factors for diabetic retinopathy (DR) and develop machine learning (ML)-based predictive models using routine laboratory data in patients with type 2 diabetes mellitus (T2DM).

Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients.

Scientific reports
In this observational study, we used data from 59,936 hospitalized adults to construct a model. For the models constructed with all 53 variables, all five models achieved acceptable performance with the validation cohort, with the extreme gradient bo...

Vibrational spectroscopy of body fluids combined with machine learning for the early diagnosis of cystic echinococcosis.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Cystic echinococcosis (CE) is a globally prevalent zoonotic parasitic disease. Due to the covert symptoms and the inadequacies of screening technologies, accurate early diagnosis is crucial. This study explores the feasibility of employing body fluid...

Combining artificial intelligence assisted image segmentation and ultrasound based radiomics for the prediction of carotid plaque stability.

BMC medical imaging
PURPOSE: Utilizing artificial intelligence (AI) technology for the segmentation of plaques on ultrasound images to evaluate the stability of carotid artery plaques and analyze its diagnostic accuracy in differentiating vulnerable plaques from stable ...

A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections.

BMC pulmonary medicine
BACKGROUND: In hospitalized patients, inadequate antibiotic dosage leading to bacterial resistance and increased antimicrobial use intensity due to overexposure to antibiotics are common problems. In the present study, we constructed a machine learni...