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 Video

Updated: Mar 27, 2026

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

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

Published on: April 13, 2013

A cost-sensitive multiclass machine learning framework for postoperative neurosurgical triage (Neuro-TACTIC).

Paul Vincent Naser^1,2,3,4, Maximilian Fischer^5,6,7, Roberto Diaz Peregrino⁸

¹Department of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany. paul.naser@med.uni-heidelberg.de.

Scientific Reports

|March 25, 2026

Summary

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

Neurosurgical Endovascular Credentialing in Europe and the United Kingdom for the "Complete" Neurovascular Surgeon: The Time has Come.

Neurosurgery·2026

Same author

Revision periacetabular osteotomy enhances joint function and activity level after failure of redirectional pelvic osteotomy at skeletal maturity.

Bone & joint open·2026

Same author

Automated and personalized glioblastoma tumor organoid drug screening platform exposes sensitivity to proteasome and HDAC inhibitors.

NPJ precision oncology·2026

Same author

Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.

Nature cancer·2026

Same author

Timing of Redox Imbalances in Cardiac Surgery.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery·2026

Same author

From Anatomy to Outcome: Linking Glioma Location Patterns to Survival Using Non-Negative Matrix Factorization.

Clinical neuroradiology·2026

A new machine learning model, Neuro-TACTIC, stratifies neurosurgical patients into three postoperative care levels, improving patient safety and resource allocation. This cost-sensitive framework balances risks, offering a more nuanced approach than binary ICU decisions.

Area of Science:

Neurosurgery
Machine Learning
Healthcare Resource Management

Background:

Postoperative patient placement is crucial for balancing safety and resources.
Current models often use a binary ICU vs. non-ICU decision, lacking adaptability.
Existing methods fail to account for local resource constraints or specific definitions of critical care events.

Purpose of the Study:

To develop a cost-sensitive machine learning framework (Neuro-TACTIC) for stratifying neurosurgical patients into three postoperative monitoring levels.
To create a model that can adjust risk thresholds based on local resource availability and definitions of critical care.
To move beyond binary ICU/non-ICU decisions for more precise patient care allocation.

Main Methods:

Developed Neuro-TACTIC, an XGBoost-based classifier utilizing 27 features (demographic, intraoperative, imaging).

Keywords:

Artificial intelligence Cost-sensitive classification Intensive care unit Machine learning Neurosurgical intensive care Neurosurgical triage

Related Experiment Videos

Last Updated: Mar 27, 2026

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

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

Published on: April 13, 2013

Trained on a retrospective cohort of 1072 patients undergoing elective craniotomy.

Incorporated a tunable parameter (ζ) to balance resource costs and harm costs, enabling adjustment of over- and under-triage.

Main Results:

The framework demonstrated stable performance across various cost settings in cross-validation and bootstrap analyses.
At ζ=0.975, performance metrics included AUCμ=0.67±0.03 and weighted F1=0.49±0.03 in the development cohort.
Independent validation showed AUCμ=0.60±0.04 and weighted F1=0.44±0.06, with operative duration, tumor volume, and surgical position as key predictors.

Conclusions:

Neuro-TACTIC shows the feasibility of cost-sensitive, three-tier postoperative triage modeling in neurosurgery.
The model offers a more nuanced approach to postoperative care allocation than traditional binary methods.
Prospective validation and multicenter evaluation are necessary before clinical implementation.