Evidence map›Paper›PMID 39305449›Full record

ReviewJournal of clinical monitoring and computing2025

A review of machine learning methods for non-invasive blood pressure estimation.

Ravi Pal, Joshua Le, Akos Rudas, Jeffrey N Chiang, Tiffany Williams, Brenton Alexander, Alexandre Joosten, Maxime Cannesson

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of clinical monitoring and computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ravi PalDepartment of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Los Angeles, CA, 90095, USA. RPal@mednet.ucla.edu.
Joshua LeLarner College of Medicine, University of Vermont, Burlington, USA.
Akos RudasDepartment of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Los Angeles, CA, 90095, USA.
Jeffrey N ChiangDepartment of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Tiffany WilliamsDepartment of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Los Angeles, CA, 90095, USA.
Brenton AlexanderDepartment of Anesthesiology & Perioperative Medicine, University of California San Diego, San Diego, CA, USA.
Alexandre JoostenDepartment of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Los Angeles, CA, 90095, USA.
Maxime CannessonDepartment of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Los Angeles, CA, 90095, USA.

Funding

Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical PatientsR01HL144692 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Maxime Cannesson · 2022 to 2022
$747k
Personalized Risk Prediction for Prevention and Early Detection of Postoperative Failure to RescueR01EB035028 · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2025 to 2025
$611k
NHLBI NIH HHS R01 HL144692NIBIB NIH HHS R01 EB029751NIBIB NIH HHS R01 EB035028
6 · The paper itself

Abstract

Blood pressure is a very important clinical measurement, offering valuable insights into the hemodynamic status of patients. Regular monitoring is crucial for early detection, prevention, and treatment of conditions like hypotension and hypertension, both of which increasing morbidity for a wide variety of reasons. This monitoring can be done either invasively or non-invasively and intermittently vs. continuously. An invasive method is considered the gold standard and provides continuous measurement, but it carries higher risks of complications such as infection, bleeding, and thrombosis. Non-invasive techniques, in contrast, reduce these risks and can provide intermittent or continuous blood pressure readings. This review explores modern machine learning-based non-invasive methods for blood pressure estimation, discussing their advantages, limitations, and clinical relevance.

Indexed as

Blood PressureBlood Pressure DeterminationMachine LearningAlgorithmsHemodynamicsHumansHypertensionHypotensionMonitoring, PhysiologicSignal Processing, Computer-AssistedHypertensionHypotensionMachine learningNon-invasive blood pressure

Identifiers

PMID39305449
PMCPMC12704448

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.