Detecting and predicting septic shock early is crucial for the best possible outcome for patients. Accurately forecasting the vital signs of patients with sepsis provides valuable insights to clinicians for timely interventions, such as administering stabilizing drugs or optimizing infusion strategies. Our research examines N-BEATS, an interpretable deep-learning forecasting model that can forecast 3 hours of vital signs for sepsis patients in intensive care units (ICUs). In this work, we use the N-BEATS interpretable configuration to forecast the vital sign trends and compare them with the actual trend to understand better the patient's changing condition and the effects of infused drugs on their vital signs. We evaluate our approach using the publicly available eICU Collaborative Research Database dataset and rigorously evaluate the vital sign forecasts using out-of-sample evaluation criteria. We present the performance of our model using error metrics, including mean squared error (MSE), mean average percentage error (MAPE), and dynamic time warping (DTW), where the best scores achieved are 18.52e-4, 7.60, and 17.63e-3, respectively. We analyze the samples where the forecasted trend does not match the actual trend and study the impact of infused drugs on changing the actual vital signs compared to the forecasted trend. Additionally, we examined the mortality rates of patients where the actual trend and the forecasted trend did not match. We observed that the mortality rate was higher (92%) when the actual and forecasted trends closely matched, compared to when they were not similar (84%).

翻译：早期检测和预测脓毒性休克对患者获得最佳预后至关重要。准确预测脓毒症患者的生命体征可为临床医生提供重要洞察，以便及时采取干预措施，如使用稳定药物或优化输液策略。本研究探讨了N-BEATS这一可解释的深度学习预测模型，该模型可预测重症监护病房（ICU）脓毒症患者未来3小时的生命体征。工作中，我们采用N-BEATS可解释配置预测生命体征趋势，并将其与实际趋势进行对比，以更好地理解患者病情变化及输注药物对生命体征的影响。我们使用公开的eICU合作研究数据库数据集评估该方法，并通过样本外评价标准严格验证生命体征预测结果。我们采用包括均方误差（MSE）、平均绝对百分比误差（MAPE）和动态时间规整（DTW）在内的误差指标展示模型性能，最佳得分分别为18.52e-4、7.60和17.63e-3。我们分析了预测趋势与实际趋势不匹配的样本，并研究了输注药物对实际生命体征相较于预测趋势的影响。此外，我们还考察了实际趋势与预测趋势不匹配患者的死亡率。观察发现，当实际趋势与预测趋势高度吻合时，死亡率较高（92%），而两者差异较大时死亡率较低（84%）。