Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Dialysis01:27

Dialysis

264
Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
264
EPS and iPS Cells in Disease Research01:21

EPS and iPS Cells in Disease Research

2.8K
Embryonic and induced pluripotent stem cells are excellent models for disease research because of their ability to self-renew and differentiate into most cell types. Somatic cells from a patient are isolated and reprogrammed into induced pluripotent stem cells or iPSCs. These iPSCs are later differentiated into the desired cell type, which mirrors the diseased cell of the patient. In this way, disease models have been created for investigating diseases such as Down syndrome, type I diabetes,...
2.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Distinct longitudinal trajectories of alkaline phosphatase and parathyroid hormone at dialysis initiation predict mortality in incident hemodialysis patients.

BMC nephrology·2026
Same author

Exploring Machine Learning Approaches for Decision Support in Neoadjuvant Therapy of Locally Advanced Rectal Cancer.

Oncology research·2026
Same author

Barriers to treatment completion among drug-sensitive tuberculosis patients: evidence from Indonesia's tuberculosis surveillance system.

Osong public health and research perspectives·2026
Same author

Unraveling the Role of Gut Microbiota in Colorectal Cancer: A Global Perspectives and Biomarkers as Early Screening Tool for Colorectal Cancer.

Studies in health technology and informatics·2025
Same author

Monitoring Adherence to Growth Hormone Treatments Using Digital Health Solutions in Hong Kong: An Expert Panel Discussion.

Studies in health technology and informatics·2025
Same author

Endocrinologists' Perceived Benefits and Risks of Digital Transformation for Growth Disorders Care in Taiwan: An Expert Panel Discussion.

Studies in health technology and informatics·2025

Related Experiment Video

Updated: Jun 5, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Using machine learning models for predicting monthly iPTH levels in hemodialysis patients.

Chih-Chieh Hsieh1, Chin-Wen Hsieh2, Mohy Uddin3

  • 1Anhsin Health Care, Pingtung, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Division of Nephrology, Department of Internal Medicine, Pingtung Christian Hospital, Pingtung, Taiwan.

Computer Methods and Programs in Biomedicine
|December 5, 2024
PubMed
Summary

Machine learning accurately predicts intact parathyroid hormone (iPTH) levels in hemodialysis patients. This approach aids in early identification of high-risk individuals, reducing reliance on traditional blood tests.

Keywords:
HemodialysisMachine learningNephrologySecondary hyperparathyroidismTaiwan

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K
A Murine Model of Hemodialysis Access-Related Hand Dysfunction
08:39

A Murine Model of Hemodialysis Access-Related Hand Dysfunction

Published on: May 31, 2022

1.6K

Related Experiment Videos

Last Updated: Jun 5, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K
A Murine Model of Hemodialysis Access-Related Hand Dysfunction
08:39

A Murine Model of Hemodialysis Access-Related Hand Dysfunction

Published on: May 31, 2022

1.6K

Area of Science:

  • Nephrology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Secondary hyperparathyroidism (SHPT) monitoring in hemodialysis patients relies on intact parathyroid hormone (iPTH) levels.
  • Accurate and timely iPTH assessment is crucial for managing SHPT in this population.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting monthly iPTH levels in hemodialysis patients.
  • To identify high-risk patients for SHPT through predictive modeling.

Main Methods:

  • A retrospective study utilizing data from the TSN-KiDiT registration system and PTCH medication records.
  • Five ML models were employed to classify patients into three iPTH level categories (<150, 150-600, >600 pg/ml).
  • Data processing involved varying durations (1-month vs. 3-month continuous) and feature sets (52 features vs. 20 SHAP-identified features).

Main Results:

  • The XGBoost model, using three months of continuous data and all 52 features, achieved the highest Weighted AUROC of 0.922.
  • The model demonstrated particular accuracy in predicting high iPTH levels (≥600 pg/ml).

Conclusions:

  • Machine learning models are highly effective for predicting iPTH levels in hemodialysis patients.
  • This predictive capability facilitates early identification of high-risk patients, potentially improving SHPT management.
  • Future work should focus on explainable AI and integrating ML frameworks into clinical workflows.