AIMC Topic: Machine Learning

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Integrated analysis of machine learning and deep learning in chili pest and disease identification.

Journal of the science of food and agriculture
BACKGROUND: Chili is one of the most important and high-value vegetable crops worldwide. However, pest and disease infections are among the main limiting factors in chili cultivation. These diseases cannot be eradicated but can be handled and monitor...

Applications of artificial intelligence in drug development using real-world data.

Drug discovery today
The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. M...

Machine Learning Algorithms Predict Clinically Significant Improvements in Satisfaction After Hip Arthroscopy.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To develop machine learning algorithms to predict failure to achieve clinically significant satisfaction after hip arthroscopy.

A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling.

International journal of medical informatics
BACKGROUND: Secondary use of Electronic Health Records (EHRs) has mostly focused on health conditions (diseases and drugs). Function is an important health indicator in addition to morbidity and mortality. Nevertheless, function has been overlooked i...

Intelligent Imaging: Developing a Machine Learning Project.

Journal of nuclear medicine technology
Artificial intelligence (AI) has rapidly progressed, with exciting opportunities that drive enthusiasm for significant projects. A sensible and sustainable approach would be to start building an AI footprint with smaller, machine learning (ML)-based ...

Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing.

Journal of emergency nursing
INTRODUCTION: Triage is critical to mitigating the effect of increased volume by determining patient acuity, need for resources, and establishing acuity-based patient prioritization. The purpose of this retrospective study was to determine whether hi...

The machine learning algorithm for the diagnosis of schizophrenia on the basis of gene expression in peripheral blood.

Neuroscience letters
BACKGROUND: Schizophrenia (SCZ) is a highly heritable mental disorder with a substantial disease burden. Machine learning (ML) method can be used to identify individuals with SCZ on the basis of blood gene expression data with high accuracy.

Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure.

Annals of emergency medicine
STUDY OBJECTIVE: We use variables from a recently derived acute heart failure risk-stratification rule (STRATIFY) as a basis to develop and optimize risk prediction using additional patient clinical data from electronic health records and machine-lea...

A community effort for automatic detection of postictal generalized EEG suppression in epilepsy.

BMC medical informatics and decision making
Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, medicine and data science are not synchronized. Oftentime...

Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network.

Neural networks : the official journal of the International Neural Network Society
In recent years, deep learning has emerged as a powerful tool for developing Brain-Computer Interface (BCI) systems. However, for deep learning models trained entirely on the data from a specific individual, the performance increase has only been mar...