Here we present the three models, efficient computational models for daily prediction of the development of AKI in critically ill middle-aged and older patients. The ConvLSTM models surpassed any previous model to predict whether older patients have AKI or not with solid evaluation metrics. Traditional statistical prediction models in medicine have made a great contribution to find related variables about the current treatment decision support. However, they have some limitations that could rarely be timeliness and accuracy. The proposed deep learning model was trained as an effort to overcome the limitations presented by traditional strategies of building prediction models. Through developing the deep neural networks, we demonstrate that deep learning can handle lots of variables which may be predictors. In addition, our results indicate that leveraging clinical data of time series as well as a deep learning model helps to efficiently learn whether AKI development in a specific group of people and boost the prediction performance. The ConvLSTM model using the daily laboratory examination and vitals can be used to predict real-time dynamics at different time points, facilitating decision-making for the physician in ICU throughout the patient’s entire hospitalization.
For over a decade, the study of biomarkers and urine volumes that can recognize AKI early and more reliably than serum creatinine elevated has been the focus of research[23–26] The machine learning model improves the accuracy of predicting AKI stages, which may be of great significance for middle-aged and older patients treated in ICU, especially for the patients with severe AKI in the future. For example, the model predicts that the patient would develop to AKI stage3 after 48 hours, based on which doctors in ICU could intervene in advance, such as disabling kidney damage drugs, rehydration therapy, or early CRRT. If the patient is identified stage 1 AKI by the model, then physicians can choose rehydration therapy and careful use of kidney damage drugs to protect kidney organs. About the application, the data input into these models is based on daily routine laboratory inspection and basic information, which can promote in most hospitals because the models don’t need the latest biological indicators.
The electronic health record (EHR) makes it possible to obtain more records, and the development of modern more powerful computing devices makes it possible to use machine learning-based models to calculate whether patients have diseases in the future. For example,
the simple real-time model launched by Yale School of Medicine trained discrete-time logistic-based machine learning models on clinical data from EHR and deployed the trained model as an embedded in EHR system service that can absorb real-time information from the hospitalized patient to provide a timely prediction of the probability of patient acquiring AKI. Similarly, the deep learning model we developed the performance is better than previous models and associated with the EHR system database, the model may provide hourly AKI stage assessment in the specific group for the next 24 and 48 hours.
Despite the accuracy advantages and potential clinical implications of deep learning demonstrated in these analyses, there are certain obstacles to the application of computer models in the clinic settings, such as doctors do not believe and do not understand algorithms. First, a most of clinicians are more familiar with linear regression and logistical regression models than deep learning techniques, and logistic modeling shows the regression coefficient can be converted into OR value, which helps doctors to understand the strength of the relationship between variables and the outcome of the patient[28, 29]. Despite deep neural network is excellent in building disease prediction models, the correlation between each variable and the outcome is poorly explained, so the deep learning algorithm is not good at giving suggestions of treatment advice based on the prediction. Reinforcement learning may be more suitable for giving better decisions in the clinical environment, in which we will study the application of reinforcement learning in clinical settings.
There are several limitations to this study. Firstly, it was a retrospective study, we did not combine with the EHR to automatically import data to our models. With the availability of time series data of examination items of patients and importing those data into models directly, fully automated prediction at different time points becomes possible and is an avenue for future work. Secondly, after making the prediction, the ConvLSTM model does not give the doctors what kind of decision should be made. In future work, models can be built to assist doctors in decision-making to help them rather than replace them. Thirdly, this study is not using all data during hospitalization, so as treatment and medication improve, the change of probability of mortality may not show up. This can be improved by collected more old patients with AKI.