AIMC Topic: Machine Learning

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Background selection schema on deep learning-based classification of dermatological disease.

Computers in biology and medicine
Skin diseases are one of the most common ailments affecting humans. Artificial intelligence based on deep learning can significantly improve the efficiency of identifying skin disorders and alleviate the scarcity of medical resources. However, the di...

AdaDiag: Adversarial Domain Adaptation of Diagnostic Prediction with Clinical Event Sequences.

Journal of biomedical informatics
Early detection of heart failure (HF) can provide patients with the opportunity for more timely intervention and better disease management, as well as efficient use of healthcare resources. Recent machine learning (ML) methods have shown promising pe...

Combining imaging flow cytometry and machine learning for high-throughput schistocyte quantification: A SVM classifier development and external validation cohort.

EBioMedicine
BACKGROUND: Schistocyte counts are a cornerstone of the diagnosis of thrombotic microangiopathy syndrome (TMA). Their manual quantification is complex and alternative automated methods suffer from pitfalls that limit their use. We report a method com...

Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records.

Journal of biomedical informatics
Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large ...

Artificial Intelligence and Machine Learning in Patient Blood Management: A Scoping Review.

Anesthesia and analgesia
Machine learning (ML) and artificial intelligence (AI) are widely used in many different fields of modern medicine. This narrative review gives, in the first part, a brief overview of the methods of ML and AI used in patient blood management (PBM) an...

Failure Mode Detection and Validation of a Shaft-Bearing System with Common Sensors.

Sensors (Basel, Switzerland)
Failure mode detection is essential for bearing life prediction to protect the shafts on the machinery. This work demonstrates the rolling bearing vibration measurement, signals converting and analysis, feature extraction, and machine learning with n...

On the Problem of State Recognition in Injection Molding Based on Accelerometer Data Sets.

Sensors (Basel, Switzerland)
The last few decades have been characterised by a very active application of smart technologies in various fields of industry. This paper deals with industrial activities, such as injection molding, where it is required to monitor continuously the ma...

Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods.

Sensors (Basel, Switzerland)
Prediabetes and diabetes are becoming alarmingly prevalent among adolescents over the past decade. However, an effective screening tool that can assess diabetes risks smoothly is still in its infancy. In order to contribute to such significant gaps, ...

Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning.

Sensors (Basel, Switzerland)
Most commercial cadence-measurement systems in road cycling are strictly limited in their function to the measurement of cadence. Other relevant signals, such as roll angle, inclination or a round kick evaluation, cannot be measured with them. This w...

An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis.

Sensors (Basel, Switzerland)
This work presents a novel Automated Machine Learning (AutoML) approach for Real-Time Fault Detection and Diagnosis (RT-FDD). The approach's particular characteristics are: it uses only data that are commonly available in industrial automation system...