Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes
| العنوان: | Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes |
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| المؤلفون: | Guadalupe Canahuate, Lisanne V. van Dijk, Clifton D. Fuller, Abdallah S.R. Mohamed, Yaohua Wang, Xinhua Zhang, Georgeta-Elisabeta Marai |
| المصدر: | IDEAS Proc Int Database Eng Appl Symp |
| بيانات النشر: | ACM, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Pediatrics, medicine.medical_specialty, business.industry, Term memory, Head and neck cancer, Late stage, Cancer, medicine.disease, Article, Cancer treatment, Quality of life, medicine, business, After treatment, Symptom ratings |
| الوصف: | Patient-Reported Outcome (PRO) surveys are used to monitor patients’ symptoms during and after cancer treatment. Acute symptoms refer to those experienced during treatment and late symptoms refer to those experienced after treatment. While most patients experience severe symptoms during treatment, these usually subside in the late stage. However, for some patients, late toxicities persist negatively affecting the patient’s quality of life (QoL). In the case of head and neck cancer patients, PRO surveys are recorded every week during the patient’s visit to the clinic and at different follow-up times after the treatment has concluded. In this paper, we model the PRO data as a time-series and apply Long-Short Term Memory (LSTM) neural networks for predicting symptom severity in the late stage. The PRO data used in this project corresponds to MD Anderson Symptom Inventory (MDASI) questionnaires collected from head and neck cancer patients treated at the MD Anderson Cancer Center. We show that the LSTM model is effective in predicting symptom ratings under the RMSE and NRMSE metrics. Our experiments show that the LSTM model also outperforms other machine learning models and time-series prediction models for these data. |
| DOI: | 10.1145/3472163.3472177 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae71804a53845d347ba692ab5c9a0d5b https://doi.org/10.1145/3472163.3472177 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....ae71804a53845d347ba692ab5c9a0d5b |
| قاعدة البيانات: | OpenAIRE |
| DOI: | 10.1145/3472163.3472177 |
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