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Updated: Sep 12, 2025

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

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Improving Spinal Cord Stimulation Patient Triage: Random Forest Model with Custom Evaluation Functions.

Chen-Ta Lin¹, Wei-Lin Chao¹, Yu-Li Huang²

¹Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, R.O.C.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

This summary is machine-generated.

This study optimized a Random Forest classifier to accurately recreate healthcare referral notes and identify patients needing Spinal Cord Stimulation. The model achieved 0.74 accuracy, improving timely patient scheduling.

Keywords:

Referral SCS Spinal Cord Stimulation optimization random forest classifier

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Area of Science:

Healthcare informatics
Machine learning in medicine
Neurosurgery

Background:

Healthcare referral triage is complex, often delayed by inefficient note processing.
Timely extraction of referral notes is critical for patient scheduling and care coordination.

Purpose of the Study:

To develop an optimized Random Forest classifier for reconstructing referral note content.
To accurately identify patients eligible for Spinal Cord Stimulation (SCS) procedures.

Main Methods:

An optimized Random Forest classifier was developed and tuned.
A performance index was introduced to enhance classification accuracy.
A consistency index was implemented to mitigate overfitting.

Main Results:

The optimized classifier achieved an accuracy of 0.74.
Optimal parameters included 5 trees and a tree depth of 4.
The model demonstrated reliability and effectiveness with a lower standard deviation.

Conclusions:

The optimized Random Forest classifier effectively recreates referral note content and identifies SCS candidates.
This approach can improve the efficiency of healthcare referral triage and patient scheduling.