There are several score-based models for predicting the mortality risk, such as SAPS (3), APACHE (20), OASIS (21) and Sequential Organ Failure Assessment (SOFA) (22). These models are all non-time series and based on statistical methods, the input data are static data or statistical data, such as comorbidities and the minimum of systolic pressure in the first 24 h, which make it impossible to predict the mortality risk in the first 24 h or to update data for predicting long-term mortality risk. Despite the AUCROCs of the score-based models were satisfied, either the sensitivity or the specificity was poor (23, 24). It’s not suprising that these models have been modified several times to improve their predictive performance since they first being published (25). Recently, for the complex, non-linear relationship between clinical variables and the outcome, non-time series AI methods, such as Artifical neural work (ANN), SVM, DT, RF, Naive Bayes, projective adaptive resonance theory (PART) and AutoTriage, were used to predict the mortality risk of patients in ICUs (5, 11, 24, 26, 27) with relatively satisfied model performance. However, due to the non-time series methods, all the variables are static or extracted from time series data, which makes it impossible to realize dynamic prediction. Herein, the AUCROCs and AUC-PRs of attention-based TCN model was larger than that of conventional score-based models in the same database according to Harutyunyan et al’s study (8). It is a pity that Harutyunyan et al did not show the sensitivity and specificity of conventional models. Regardless of the slight difference in AUCROCs and AUC-PRs among attention-based TCN and other non-time series ML methods, the sensitivity of attention-based TCN was much higher than that of others. In clinical works, when decision-making happens, doctors should take medical history, physical examination and trend of vital signs into consideration. The ideal model for predicting mortality risk is taking both time series and static clinical data into consideration, moreover simultaneously realize dynamic prediction. Furthermore, due to the instable status of ICU patients, the sensitivity seemed more important than the specificity, for missing the potential patients at risk might be fatal. In brief, attention-based TCN method was better than non-time series methods in predicting the motarlity risk of ICU patients. In addition, Hao et al (28) tried to apply attention-based TCN to language models resulting a significant elevation of model performance, which suggests attention-based TCN is a promissing method for Sequence Modeling.
Recently, Yu et al (7), Harutyunyan et al (8) and Song et al (16) combined two AI methods (including one time series method) to predict the mortality risk of ICU patients with large AUCROCs and AUC-PRs but lower sensitivity (the variables and sensitivity were not presented in Harutyunyan’s study). Despite the low sensitivity, there were other shortcomings of these studies. At first, Yu et al’s and Harutyunyan’s methods were based on LSTM, which deals with time series data sequentially from beginning to end, while TCN can do parallel processing by causal convolutions in the architecture (17). Due to the limitations of LSTM, attention-based TCN methods would be more proper for higher dimension and amounts of data and require less in hardware, which would be more appropriate for clinical extension. Secondly, Yu et’s study included vital signs HR, SBP and temperature, while ours included RR, HR, DBP, MBP, SBP and temperature. Nowadays MBP and DBP are widely accepted as important predictors for ICU patients (29–31). So, it may be insufficient to predict the mortality risk without MBP and DBP. Moreover, some of the variables such as urinary output in Yu et al’s study, which are sum or mean of clinical data in a set period time and have a longer acquisition time interval than that of vital signs. Vital signs in our study were more reasonable and easily to obtain than those in Yu et al’s, meanwhile variables more frequently collected would help more for dynamic prediction. Thirdly, Harutyunyan et al’s and Song et al’s study focused on the algorithms, the clinical value was a bit overlooked. Fourthly, these three studies combined attention mechanism mainly aimed to elevate the efficiency of computing rather than interpretability. Comparing with time series methods combined with other AI methods for predicting mortality risk of ICU patients, our attention-based TCN method also had advantages of higher efficiency, better interpretability and easier for promotion.
As shown in Figure.3, we drew a diagram for clinical use of predicting the mortality risk of ICU patients by attention-based TCN methods: For a new critical patient, patient’s baseline information and monitoring data would be put into the attention-based TCN model as data flow after automatically data preprocessing; Then the mortality risk will be predicted at different time points according to the patient’s specific condition (here we predict the mortality risk 48 h after ICU admission); If the estimated mortality risk is high, the patient will receive intensive monitoring and intensive treatment; if the estimated mortality risk is low, the patient will receive intensive monitoring and routine treatment. In brief, the whole process is Warning →Intervention →Warning →Intervention→……→Patient outcome.
There are several limitations in this study. First of all, though the variables in our study were routine and most of them were time series, some more routine and frequently collected variables, such as lactic acid and results of arterial blood gas analysis, should be included to help elevate the prediction accuracy. Secondly, clinical data are extracted from one medical center, so the generalization ability of the model and its possibility of clinical application is not validated. Prospective multi-center studies should be carried out to investigate the clinical value of combing TCN with attention mechanism to predict patient’s mortality risk using temporal clinical data.