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Related Experiment Video

Updated: Jan 2, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Addressing the Credit Assignment Problem in Treatment Outcome Prediction using Temporal Difference Learning.

Sahar Harati¹, Andrea Crowell, Helen Mayberg

¹Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA, harati@stanford.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|December 5, 2019

Summary

This summary is machine-generated.

This study uses machine learning on video interviews to predict Deep Brain Stimulation (DBS) treatment success for mental health patients weeks in advance. The approach accurately forecasts treatment outcomes, improving patient care.

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Last Updated: Jan 2, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Area of Science:

Neuroscience
Artificial Intelligence
Psychiatry

Background:

Mental health treatment selection is often lengthy, involving trial-and-error.
Predicting treatment response early can significantly shorten trial durations.
A challenge in predictive modeling is the delayed feedback on treatment effectiveness.

Purpose of the Study:

To develop a Machine Learning (ML) model for predicting Deep Brain Stimulation (DBS) treatment outcomes.
To extract predictive audio-visual features from weekly patient video interviews.
To address the issue of delayed feedback in supervised learning for treatment response.

Main Methods:

Utilized a joint state-estimation and temporal difference learning approach.
Extracted audio-visual features from longitudinal video recordings of patients.
Modeled patient response trajectories and accounted for delayed feedback signals.

Main Results:

The ML model successfully predicted long-term Deep Brain Stimulation (DBS) treatment success.
Learned state values from video interviews were predictive of treatment outcomes.
Achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.88, outperforming baseline methods.

Conclusions:

Machine learning analysis of video interviews can predict DBS treatment response in depression.
The proposed method effectively handles delayed feedback, offering a promising tool for personalized mental healthcare.
This approach has the potential to optimize treatment selection and improve patient outcomes.