Deep Learning for Mortality Forecasts
A recent study, featured in Scientific Reports, has harnessed advanced data-synthesis techniques and machine learning models to significantly enhance the accuracy of mortality predictions in emergency departments (EDs). With a special focus on improving the F1 score while maintaining a high Area Under the Curve (AUC) score, this research utilized data from Yonsei Severance Hospital’s ED.
Background
Every year, U.S. emergency departments grapple with 130 million patient visits, leading to resource constraints and overcrowding—a situation further exacerbated by the challenges posed by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic.
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Current triage systems suffer from subjectivity and are prone to errors. While machine learning (ML) has the potential to enhance the accuracy of patient outcome predictions, early models had limitations. Further research is crucial to optimize ML and data-synthesis algorithms for mortality predictions in EDs, tackling issues like dataset imbalances and feature effectiveness.
The Study
The study drew on data from 7,325 individuals who sought treatment at Yonsei Severance Hospital’s ED in Seoul, South Korea, between January and June 2020. This hospital served as a designated coronavirus disease 2019 (COVID-19) screening clinic and managed severe cases. Authorized medical personnel collected the data through the hospital’s electronic system.
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Twenty-one features were analyzed, including six indicating whether patients received specific treatments post-initial evaluation. Medical history information, such as high blood pressure status (HiBP), diabetes mellitus (DM), allergies, pulmonary tuberculosis (Pul. Tbc), hepatitis, and other medications, was also considered. Additionally, seven metrics, including mental status and vital signs, were collected during the initial evaluation.
The initial dataset included 7,325 patients, but 1,543 records had missing features, resulting in a refined dataset of 5,782 records. Data was divided into training and test sets for machine learning system training and evaluation.
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Given the dataset’s inherent imbalance, data-synthesis techniques like Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Tabular Generative Adversarial Network (CTGAN) were employed to generate synthetic data for deceased patients.
For prediction, four machine learning algorithms were used, ranging from traditional machine learning to Deep Neural Network (DNN)-based learning. TabNet, known for its recent superior performance, was chosen for its potential despite DNN’s general underperformance for tabular datasets. The prediction model framework included preprocessing, data division, augmentation through data-synthesis algorithms, and training multiple classification models. An ensemble approach was used to merge model predictions, with a focus on the F1 score due to its relevance in imbalanced medical datasets.
Study Results
The study’s primary objective was to identify the most effective combination of machine learning classification models and data synthesis techniques for accurately predicting mortality rates in the ED. Given the dataset’s imbalance, the F1 score was chosen as the primary performance metric.
The top five models exhibited impressive performance metrics, including the F1 score, AUC, accuracy, precision, and recall. The leading model, which used the Gaussian Copula for data synthesis combined with the CatBoost classifier, particularly excelled in predictive capability.
This model achieved an AUC of 0.9731, an F1 score of 0.7059, and a remarkable accuracy of 0.9914. Moreover, it boasted a precision of 0.8000 and a recall of 0.6316, emphasizing its proficiency in identifying urgent patients requiring immediate medical intervention.
Across various performance metrics, different combinations of ML algorithms and data-synthesis methods consistently produced impressive results in predicting patient mortality in the ED. These findings underscore the promising potential of ML models to enhance predictions regarding patient outcomes in emergency healthcare settings. Such advancements can empower healthcare practitioners to make timely and informed decisions, ultimately leading to more suitable medical interventions.
This not only enhances the efficiency of medical procedures but also has the potential to save lives, highlighting the significant impact of incorporating advanced predictive models into the healthcare sector.
Conclusions
In summary, this study introduced twenty-one distinct features that outperformed previous benchmarks in predicting mortality in emergency departments. Despite challenges associated with imbalanced datasets, the model achieved a notably high F1 score, indicating its reliable predictive capabilities.
When compared to conventional triage systems and prior research, this study’s models, especially those leveraging synthetic data from the Gaussian Copula method, demonstrated superior performance.
The variability in traditional triage scores underscores the importance of consistent, intelligent systems in healthcare. The data-synthesis algorithm employed in this study effectively bolstered model predictions, emphasizing its significance in training machine learning models.
