AIMC Topic: Machine Learning

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Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success.

Current neurology and neuroscience reports
PURPOSE OF REVIEW: Neurocritical care combines the complexity of both medical and surgical disease states with the inherent limitations of assessing patients with neurologic injury. Artificial intelligence (AI) has garnered interest in the basic mana...

Prediction of marbling score and carcass traits in Korean Hanwoo beef cattle using machine learning methods and synthetic minority oversampling technique.

Meat science
Pricing of Hanwoo beef in the Korean market is primarily based on meat quality, and particularly on marbling score. The ability to accurately predict marbling score early in the life of an animal is extremely valuable for producers to meet the requir...

Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach.

Journal of applied microbiology
AIMS: Analysis and tracking of antimicrobial utilization (AU) are crucial in antimicrobial stewardship efforts which are used to find effective interventions for controlling antimicrobial resistance. In antimicrobial stewardship, standard risk adjust...

An agent-based lattice model for the emergence of anti-microbial resistance.

Journal of theoretical biology
This work introduces a new probabilistic agent-based lattice model for studying the emergence of anti-microbial resistance (AMR) and proposes a new proxy to measure it: the average death probability of the population under the action of the AMD. Both...

Mounting Behaviour Recognition for Pigs Based on Deep Learning.

Sensors (Basel, Switzerland)
For both pigs in commercial farms and biological experimental pigs at breeding bases, mounting behaviour is likely to cause damage such as epidermal wounds, lameness and fractures, and will no doubt reduce animal welfare. The purpose of this paper is...

Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico.

BMC medical informatics and decision making
BACKGROUND: The study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based ...

Comparative analysis on Facebook post interaction using DNN, ELM and LSTM.

PloS one
This study presents a novel research approach to predict user interaction for social media post using machine learning algorithms. The posts are converted to vector form using word2vec and doc2vec model. These two methods are used to analyse the best...

Using machine learning approaches to predict high-cost chronic obstructive pulmonary disease patients in China.

Health informatics journal
The accurate identification and prediction of high-cost Chronic obstructive pulmonary disease (COPD) patients is important for addressing the economic burden of COPD. The objectives of this study were to use machine learning approaches to identify an...

Early and Late Fusion Machine Learning on Multi-Frequency Electrical Impedance Data to Improve Radiofrequency Ablation Monitoring.

IEEE journal of biomedical and health informatics
Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-time monitoring of RFA within multiple tissue types is still an ongoing research topic. The objective of this study is to utilize multi-frequency elect...

Developing Children's Oral Health Assessment Toolkits Using Machine Learning Algorithm.

JDR clinical and translational research
OBJECTIVES: Evaluating children's oral health status and treatment needs is challenging. We aim to build oral health assessment toolkits to predict Children's Oral Health Status Index (COHSI) score and referral for treatment needs (RFTN) of oral heal...