Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Learning causal and predictive clinical practice guidelines from data.

Subramani Mani¹, Constantin Aliferis, Shanthi Krishnaswami

¹Department of Biomedical Informatics, Vanderbilt University, Nashville TN 37232, USA. subramani.mani@vanderbilt.edu

Studies in Health Technology and Informatics

|October 4, 2007

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Clinical and Economic Burden Following Percutaneous Coronary Intervention for STEMI: A US Real-World Evidence Study.

ClinicoEconomics and outcomes research : CEOR·2026

Same author

Knotless tissue control devices for hand-sewn gastrojejunostomy in robotic-assisted Roux-en-Y gastric bypass.

Obesity surgery·2026

Same author

Artificial intelligence can replace nursing tasks, but not nurses: Examining artificial intelligence's supports and threats to nursing practice through the lens of the 2025 Nursing Code of Ethics.

Nursing outlook·2026

Same author

A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).

Journal of the National Medical Association·2026

Same author

Posttraumatic stress in young children at risk for maltreatment: a causal data science analysis.

Frontiers in health services·2026

Same author

Ensemble of Time-Evolving SASP Gene Sets Identifies IGFBP7 and CDKN1A as a Potential Marker Pair for Senescent Fibroblast Subpopulations Across Tissues.

International journal of molecular sciences·2026

Same journal

The Essential Components and Critical Conditions for Success in a Learning Health System in Oncology.

Studies in health technology and informatics·2026

Same journal

Use of Artificial Intelligence in Screening for Adolescent Idiopathic Scoliosis: A Scoping Review.

Studies in health technology and informatics·2026

Same journal

Movement Related Biomechanics in Adolescent Idiopathic Scoliosis: A Review of Reviews.

Studies in health technology and informatics·2026

Same journal

The Impact of Surgical Correction of Adolescent Idiopathic Scoliosis Using Posterior Spinal Fusion on Selected Radiological Parameters and Respiratory Function.

Studies in health technology and informatics·2026

Same journal

Acute Effect of Physio-logic® Exercises on Muscle Tone and Stiffness in Adolescent Idiopathic Scoliosis Patients: A Preliminary Study.

Studies in health technology and informatics·2026

Same journal

Effects of Integrated Music and Occupational Therapy on Motor and Autonomic Function in Children with Neurogenic Scoliosis.

Studies in health technology and informatics·2026

See all related articles

This study explores generating clinical practice guidelines from data using machine learning and causal discovery. The aim is to standardize healthcare delivery for conditions like high blood pressure.

Area of Science:

Medical Informatics
Machine Learning
Causal Inference

Background:

Clinical practice guidelines (CPG) standardize healthcare based on evidence.
Evidence-based medicine relies on robust guidelines for consistent patient care.

Purpose of the Study:

To investigate the automated generation of CPG from data.
To apply machine learning and causal discovery for guideline creation.
To demonstrate the approach using high blood pressure management.

Main Methods:

Utilizing machine learning algorithms for data analysis.
Employing causal discovery methods to infer relationships.
Applying these techniques to a high blood pressure dataset.

Related Experiment Videos

Main Results:

Demonstrated feasibility of data-driven guideline generation.
Identified potential for enhanced standardization of care.
Showcased the application in a relevant clinical domain.

Conclusions:

Machine learning and causal discovery offer novel pathways for CPG development.
Automated guideline generation can improve evidence-based practice.
This approach holds promise for optimizing healthcare delivery.