Finkelstein Joseph, Smiley Aref, Echeverria Christina, Mooney Kathi
Department of Biomedical Informatics, The University of Utah, Salt Lake City, UT 84108, USA.
College of Nursing, The University of Utah, Salt Lake City, UT 84112, USA.
Bioengineering (Basel). 2024 Nov 20;11(11):1172. doi: 10.3390/bioengineering11111172.
This study presents an advanced method for predicting symptom escalation in chemotherapy patients using Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). The accurate prediction of symptom escalation is critical in cancer care to enable timely interventions and improve symptom management to enhance patients' quality of life during treatment. The analytical dataset consists of daily self-reported symptom logs from chemotherapy patients, including a wide range of symptoms, such as nausea, fatigue, and pain. The original dataset was highly imbalanced, with approximately 84% of the data containing no symptom escalation. The data were resampled into varying interval lengths to address this imbalance and improve the model's ability to detect symptom escalation (n = 3 to n = 7 days). This allowed the model to predict significant changes in symptom severity across these intervals. The results indicate that shorter intervals (n = 3 days) yielded the highest overall performance, with the CNN model achieving an accuracy of 81%, precision of 87%, recall of 80%, and an F1 score of 83%. This was an improvement over the LSTM model, which had an accuracy of 79%, precision of 85%, recall of 79%, and an F1 score of 82%. The model's accuracy and recall declined as the interval length increased, though precision remained relatively stable. The findings demonstrate that both CNN's temporospatial feature extraction and LSTM's temporal modeling effectively capture escalation patterns in symptom progression. By integrating these predictive models into digital health systems, healthcare providers can offer more personalized and proactive care, enabling earlier interventions that may reduce symptom burden and improve treatment adherence. Ultimately, this approach has the potential to significantly enhance the overall quality of life for chemotherapy patients by providing real-time insights into symptom trajectories and guiding clinical decision making.
本研究提出了一种先进的方法，用于使用长短期记忆（LSTM）网络和卷积神经网络（CNN）预测化疗患者的症状升级。在癌症护理中，准确预测症状升级至关重要，以便能够及时进行干预并改善症状管理，从而在治疗期间提高患者的生活质量。分析数据集由化疗患者的每日自我报告症状日志组成，包括多种症状，如恶心、疲劳和疼痛。原始数据集严重失衡，约84%的数据没有症状升级。对数据进行重新采样，使其具有不同的时间间隔长度，以解决这种不平衡问题，并提高模型检测症状升级的能力（n = 3至n = 7天）。这使得模型能够预测这些时间间隔内症状严重程度的显著变化。结果表明，较短的时间间隔（n = 3天）产生了最高的整体性能，CNN模型的准确率为81%，精确率为87%，召回率为80%，F1分数为83%。这比LSTM模型有所改进，LSTM模型的准确率为79%，精确率为85%，召回率为79%，F1分数为82%。随着时间间隔长度的增加，模型的准确率和召回率下降，尽管精确率保持相对稳定。研究结果表明，CNN的时空特征提取和LSTM的时间建模都有效地捕捉了症状进展中的升级模式。通过将这些预测模型集成到数字健康系统中，医疗保健提供者可以提供更个性化和主动的护理，实现更早的干预，这可能会减轻症状负担并提高治疗依从性。最终，这种方法有可能通过提供症状轨迹的实时洞察并指导临床决策，显著提高化疗患者的整体生活质量。
