A CNN-LSTM for predicting mortality in the ICU

An accurate predicted mortality is crucial to healthcare as it provides an empirical risk estimate for prognostic decision making, patient stratification and hospital benchmarking. Current prediction methods in practice are severity of disease scoring systems that usually involve a fixed set of admission attributes and summarized physiological data. These systems are prone to bias and require substantial manual effort which necessitates an updated approach which can account for most shortcomings. Clinical observation notes allow for recording highly subjective data on the patient that can possibly facilitate higher discrimination. Moreover, deep learning models can automatically extract and select features without human input.This thesis investigates the potential of a combination of a deep learning model and notes for predicting mortality with a higher accuracy. A custom architecture, called CNN-LSTM, is conceptualized for mapping multiple notes compiled in a hospital stay to a mortality outcome. It employs both convolutional and recurrent layers with the former capturing semantic relationships in individual notes independently and the latter capturing temporal relationships between concurrent notes in a hospital stay. This approach is compared to three severity of disease scoring systems with a case study on the MIMIC-III dataset. Experiments are set up to assess the CNN-LSTM for predicting mortality using only the notes from the first 24, 12 and 48 hours of a patient stay. The model is trained using K-fold cross-validation with k=5 and the mortality probability calculated by the three severity scores on the held-out set is used as the baseline. It is found that the CNN-LSTM outperforms the baseline on all experiments which serves as a proof-of-concept of how notes and deep learning can better outcome prediction

A novel and reliable framework of patient deterioration prediction in Intensive Care Unit based on long short-term memory-recurrent neural network

The clinical investigation explored that early recognition and intervention are crucial for preventing clinical deterioration in patients in Intensive Care units (ICUs). Deterioration of patients is predictable and can be preventable if early risk factors are recognized and developed in the clinical setting. Timely detection of deterioration in ICU patients may also lead to better health management. In this paper, a new model was proposed based on Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) to predict deterioration of ICU patients. An optimisation model based on a modified genetic algorithm (GA) has also been proposed in this study to optimize the observation window, prediction window, and the number of neurons in hidden layers to increase accuracy, AUROC, and minimize test loss. The experimental results demonstrate that the prediction model proposed in this study acquired a significantly better classification performance compared with many other studies that used deep learning models in their works. Our proposed model was evaluated for two tasks: mortality and sudden transfer of patients to ICU. Our results show that the proposed model could predict deterioration before one hour of onset and outperforms other models. In this study, the proposed predictive model is implemented using the state-of-the-art graphical processing unit (GPU) virtual machine provided by Google Colaboratory. Moreover, the study uses a novel time-series approach, which is minute-by-minute. This novel approach enables the proposed model to obtain highly accurate results (i.e., an AUROC of 0.933 and an accuracy of 0.921). This study utilizes the individual and combined effectiveness of different types of variables (i.e., vital signs, laboratory measurements, GCS, and demographic data). In this study, data was extracted from MIMIC-III database. The ad-hoc frameworks proposed by previous studies can be improved by the novel and reliable prediction framework proposed in this research, which will result in predictions of more accurate performance. The proposed predictive model could reduce the required observation window (i.e., a reduction of 83%) for the prediction task while improving the performance. In fact, the proposed significant small size of observation window could obtain higher results which outperformed all previous works that utilize different sizes of observation window (i.e., 48 hours and 24 hours). Moreover, this research demonstrates the ability of the proposed predictive model to achieve accurate results (>80%) on 'raw' data in an experimental work. This shows that the rule-based pre-processing of clinical features is unnecessary for deep learning predictive models

Generalizability of machine learning models in predicting patient deterioration

Predicting patient deterioration in an Intensive Care Unit (ICU) effectively is a critical health care task serving patient health and resource allocation. At times, the task may be highly complex for a physician, yet high-stakes and time-critical decisions need to be made based on it. In this work, we investigate the ability of a set of machine learning models to algorithimically predict future occurrence of in hospital death based on Electronic Health Record (EHR) data of ICU-patients. For one, we will assess the generalizability of the models. We do this by evaluating the models on hospitals the data of which has not been considered when training the models. For another, we consider the case in which we have access to some EHR data for the patients treated at a hospital of interest. In this setting, we assess how EHR data from other hospitals can be used in the optimal way to improve the prediction accuracy. This study is important for the deployment and integration of such predictive models in practice, e.g., for real-time algorithmic deterioration prediction for clinical decision support. In order to address these questions, we use the eICU collaborative research database, which is a database containing EHRs of patients treated at a heterogeneous collection of hospitals in the United States. In this work, we use the patient demographics, vital signs and Glasgow coma score as the predictors. We devise and describe three computational experiments to test the generalization in different ways. The used models are the random forest, gradient boosted trees and long short-term memory network. In our first experiment concerning the generalization, we show that, with the chosen limited set of predictors, the models generalize reasonably across hospitals but that only a small data mismatch is observed. Moreover, with this setting, our second experiment shows that the model performance does not significantly improve when increasing the heterogeneity of the training set. Given these observations, our third experiment shows tha