Version 1
: Received: 11 January 2024 / Approved: 11 January 2024 / Online: 11 January 2024 (14:52:37 CET)
How to cite:
Jeong, S.; Pyo, H.; Park, W.; Han, Y. Prediction of Stress in Radiation Therapy: Integrating Artificial Intelligence with Biological Signals. Preprints2024, 2024010941. https://doi.org/10.20944/preprints202401.0941.v1
Jeong, S.; Pyo, H.; Park, W.; Han, Y. Prediction of Stress in Radiation Therapy: Integrating Artificial Intelligence with Biological Signals. Preprints 2024, 2024010941. https://doi.org/10.20944/preprints202401.0941.v1
Jeong, S.; Pyo, H.; Park, W.; Han, Y. Prediction of Stress in Radiation Therapy: Integrating Artificial Intelligence with Biological Signals. Preprints2024, 2024010941. https://doi.org/10.20944/preprints202401.0941.v1
APA Style
Jeong, S., Pyo, H., Park, W., & Han, Y. (2024). Prediction of Stress in Radiation Therapy: Integrating Artificial Intelligence with Biological Signals. Preprints. https://doi.org/10.20944/preprints202401.0941.v1
Chicago/Turabian Style
Jeong, S., Won Park and Youngyih Han. 2024 "Prediction of Stress in Radiation Therapy: Integrating Artificial Intelligence with Biological Signals" Preprints. https://doi.org/10.20944/preprints202401.0941.v1
Abstract
Stress can reduce the accuracy of radiation therapy owing to muscle contraction and changes in breathing patterns. This study aimed to predict stress in patients before each treatment session using artificial intelligence (AI) from biological signals to enhance treatment accuracy. We measured 123 stress cases in 41 patients and calculated stress scores by analyzing seven stress-related features derived from heart rate variability obtained from photoplethysmography. The study analyzed stress score distribution and observed its trend changes throughout the treatment. Before-treatment information was used to predict changes in stress features during treatment. AI models included both non-pre-trained (Decision Tree, Random Forest, Support Vector Machine, Long Short-Term Memory (LSTM), Transformer) and pre-trained (ChatGPT) models. The performance was evaluated using 10-fold cross-validation, exact match ratio, accuracy, recall, precision, and F1 score. More than 90% of the patients experienced stress during radiation therapy. From all the classifications, LSTM and prompt engineering GPT4.0 had the highest accuracy (feature classification, LSTM: 0.703, GPT4.0: 0.659, stress classification: LSTM: 0.846, GPT4.0: 0.769). Our research pioneers the use of AI and biological signals for stress prediction in radiation therapy, potentially identifying patients needing psychological support, and suggesting methods to improve radiotherapy effectiveness through stress management.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
