ClinicalTrials.gov
public full text
Public Domain
Physiological Validation of Current Machine Learning Models for Hemodynamic Instability in Humans
This study will be collecting data on participants undergoing lower body negative pressure (LBNP) to simulate progressive blood loss. The goal of the study is to collect data to allow for development of an algorithm with machine learning to predict blood pressure responses to hyporvolemia by analyzing the arterial waveforms collected during LBNP.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Exploring hemodynamic measurements from the Tromsø Study for prediction of cardiovascular disease using traditional statistical models and machine learning approaches.#610349
- Prediction of Patient Deterioration Using Machine Learning#143702
- Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration#1078971
- Machine learning-based evaluation of lipid biomarkers for cardiovascular risk prediction in chronic kidney disease: A cross-sectional study.#368327
- Early Prediction of Sepsis#1161454
Record · ID 916306
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive