Wang Yaohua, Van Dijk Lisanne, Mohamed Abdallah S R, Fuller Clifton David, Zhang Xinhua, Marai G Elisabeta, Canahuate Guadalupe
Electrical and Computer Engineering University of Iowa.
Anderson Cancer Center.
Proc Int Database Eng Appl Symp. 2021 Jul;2021:273-279. doi: 10.1145/3472163.3472177. Epub 2021 Sep 7.
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.
患者报告结局（PRO）调查用于监测癌症治疗期间及之后患者的症状。急性症状指治疗期间经历的症状，晚期症状指治疗后经历的症状。虽然大多数患者在治疗期间会出现严重症状，但这些症状通常在晚期会消退。然而，对一些患者来说，晚期毒性持续存在，对患者的生活质量（QoL）产生负面影响。对于头颈癌患者，在患者到诊所就诊期间每周记录PRO调查情况，并在治疗结束后的不同随访时间进行记录。在本文中，我们将PRO数据建模为时间序列，并应用长短期记忆（LSTM）神经网络来预测晚期症状的严重程度。本项目中使用的PRO数据对应于从MD安德森癌症中心接受治疗的头颈癌患者收集的MD安德森症状量表（MDASI）问卷。我们表明，LSTM模型在均方根误差（RMSE）和归一化均方根误差（NRMSE）指标下能有效预测症状评分。我们的实验表明，对于这些数据，LSTM模型也优于其他机器学习模型和时间序列预测模型。
