AIMC Topic: Machine Learning

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Machine learning for contour classification in TG-263 noncompliant databases.

Journal of applied clinical medical physics
A large volume of medical data are labeled using nonstandardized nomenclature. Although efforts have been made by the American Association of Physicists in Medicine (AAPM) to standardize nomenclature through Task Group 263 (TG-263), there remain nonc...

Improved Prediction of Body Mass Index in Real-World Administrative Healthcare Claims Databases.

Advances in therapy
INTRODUCTION: To continue closing the gap between the predictive modeling and its real-world application, we report a new data-to-prediction pipeline that advanced the state-of-the-art predictive performance of body mass index (BMI) classifications b...

Screening membraneless organelle participants with machine-learning models that integrate multimodal features.

Proceedings of the National Academy of Sciences of the United States of America
Protein self-assembly is one of the formation mechanisms of biomolecular condensates. However, most phase-separating systems (PS) demand multiple partners in biological conditions. In this study, we divided PS proteins into two groups according to th...

DOC-IDS: A Deep Learning-Based Method for Feature Extraction and Anomaly Detection in Network Traffic.

Sensors (Basel, Switzerland)
With the growing diversity of cyberattacks in recent years, anomaly-based intrusion detection systems that can detect unknown attacks have attracted significant attention. Furthermore, a wide range of studies on anomaly detection using machine learni...

Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence.

Sensors (Basel, Switzerland)
Predicting the bulk-average velocity (U) in open channels with rigid vegetation is complicated due to the non-linear nature of the parameters. Despite their higher accuracy, existing regression models fail to highlight the feature importance or causa...

A machine learning approach to economic complexity based on matrix completion.

Scientific reports
This work applies Matrix Completion (MC) - a class of machine-learning methods commonly used in recommendation systems - to analyze economic complexity. In this paper MC is applied to reconstruct the Revealed Comparative Advantage (RCA) matrix, whose...

The Empirical Analysis of Bitcoin Price Prediction Based on Deep Learning Integration Method.

Computational intelligence and neuroscience
As a new type of electronic currency, bitcoin is more and more recognized and sought after by people, but its price fluctuation is more intense, the market has certain risks, and the price is difficult to be accurately predicted. The main purpose of ...

Oxygen delivery in pediatric cardiac surgery and its association with acute kidney injury using machine learning.

The Journal of thoracic and cardiovascular surgery
OBJECTIVE: Acute kidney injury (AKI) after pediatric cardiac surgery with cardiopulmonary bypass (CPB) is a frequently reported complication. In this study we aimed to determine the oxygen delivery indexed to body surface area (Doi) threshold associa...

Designing Equitable Health Care Outreach Programs From Machine Learning Patient Risk Scores.

Medical care research and review : MCRR
There is growing interest in ensuring equity and guarding against bias in the use of risk scores produced by machine learning and artificial intelligence models. Risk scores are used to select patients who will receive outreach and support. Inappropr...

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond.

Annual review of pharmacology and toxicology
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of sy...