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Related Experiment Video

Updated: Apr 3, 2026

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

Hematocrit estimation using online sequential extreme learning machine.

Hieu Trung Huynh¹, Yonggwan Won², Jinsul Kim²

¹Faculty of Information Technology, Industrial University of Ho Chi Minh City, Viet Nam.

Bio-Medical Materials and Engineering

|September 26, 2015

Summary

This summary is machine-generated.

This study presents a novel method for estimating hematocrit, a key blood indicator, using transduced current curves and neural networks. This approach enhances glucose measurement accuracy in handheld devices and allows for continuous device training.

Keywords:

OS-ELM extreme learning machine hematocrit neural network online training

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Last Updated: Apr 3, 2026

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Area of Science:

Biomedical Engineering
Clinical Diagnostics
Machine Learning

Background:

Hematocrit, the volume percentage of red blood cells, is crucial for clinical decisions.
Accurate hematocrit estimation is vital for reliable glucose measurements using handheld devices.
Current methods may lack adaptability and efficiency in real-time applications.

Purpose of the Study:

To develop an advanced method for hematocrit estimation.
To improve the accuracy and efficiency of glucose measurement in handheld devices.
To enable continuous learning and adaptation of devices during use.

Main Methods:

Utilizing transduced current curves as input data.
Employing a neural network trained with the online sequential extreme learning machine (OS-ELM).
Incorporating extended features to optimize data processing.

Main Results:

The OS-ELM allows for dynamic training with new samples during device operation.
The use of extended features reduces the number of required current points.
This leads to faster measurements and conserves device battery power.

Conclusions:

The proposed method offers an efficient and adaptive approach to hematocrit estimation.
This technique enhances the performance and usability of handheld diagnostic devices.
Continuous learning capabilities ensure sustained accuracy and efficiency over time.