Technol Health Care. 2024;32(S1):329-337. doi: 10.3233/THC-248029.
Heart failure poses a significant challenge in the global health domain, and accurate prediction of mortality is crucial for devising effective treatment plans. In this study, we employed a Seq2Seq model from deep learning, integrating 12 patient features. By finely modeling continuous medical records, we successfully enhanced the accuracy of mortality prediction.
The objective of this research was to leverage the Seq2Seq model in conjunction with patient features for precise mortality prediction in heart failure cases, surpassing the performance of traditional machine learning methods.
The study utilized a Seq2Seq model in deep learning, incorporating 12 patient features, to intricately model continuous medical records. The experimental design aimed to compare the performance of Seq2Seq with traditional machine learning methods in predicting mortality rates.
The experimental results demonstrated that the Seq2Seq model outperformed conventional machine learning methods in terms of predictive accuracy. Feature importance analysis provided critical patient risk factors, offering robust support for formulating personalized treatment plans.
This research sheds light on the significant applications of deep learning, specifically the Seq2Seq model, in enhancing the precision of mortality prediction in heart failure cases. The findings present a valuable direction for the application of deep learning in the medical field and provide crucial insights for future research and clinical practices.
心力衰竭在全球健康领域构成重大挑战，准确预测死亡率对于制定有效的治疗方案至关重要。在本研究中，我们使用了深度学习的 Seq2Seq 模型，整合了 12 个患者特征。通过精细地对连续的医疗记录进行建模，我们成功提高了死亡率预测的准确性。
本研究旨在利用 Seq2Seq 模型和患者特征，对心力衰竭病例进行精确的死亡率预测，超越传统机器学习方法的性能。
本研究使用深度学习中的 Seq2Seq 模型，整合 12 个患者特征，对连续的医疗记录进行深入建模。实验设计旨在比较 Seq2Seq 与传统机器学习方法在预测死亡率方面的性能。
实验结果表明，Seq2Seq 模型在预测准确性方面优于传统机器学习方法。特征重要性分析提供了关键的患者风险因素，为制定个性化治疗计划提供了有力支持。
本研究揭示了深度学习，特别是 Seq2Seq 模型在提高心力衰竭病例死亡率预测精度方面的重要应用。研究结果为深度学习在医学领域的应用提供了有价值的方向，并为未来的研究和临床实践提供了重要的见解。
