Deep Learning-Based Domain Adaptation Healthcare Method for Predicting Mortality Risk of ICU Patient
Keywords:
Deep Learning, Unsupervised, Domain Adaptation, Softmax, Cosine Margin Loss, BI-LSTM, MedicalAbstract
Deep learning models often encounter issues such as insufficient labelled training data, shifts in overall data distribution, and shifts in data distribution between categories, leading to a drop in model prediction accuracywhen employed in prediction tasks in the medical area. A deep learning-based domain adaptation marginal softmax aware additive cosine margin loss prediction (DDSCM) model is presented to address such issues. The presented approachutilizes a bidirectional memory network along with an attention modulein order to retrieve the crucial features. The approach offers the concept of generative adversarial networks to reduce data distribution shifts between comprehensive data in the form of domain adversarial. The idea of metric learning is then included in the method to reduce the data distribution shift between categories even further by optimizing the decision boundary which thereby increase the domain adaptation effect and prediction accuracy of the model. At last, real-world medical data collection is used to conduct the mortality risk prediction job. The experimental findings were compared to the baseline models of the other five. The proposed method solves the problem of data distribution shift better than existing baseline models and obtains a better classification result.
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