Healthcare Predictive Analytics: A Deep Learning Models for Clinical Decision Support and Optimizing Patient Outcomes

Abstract

The direction of deep learning patient outcome prediction and clinical decision optimization is called healthcare predictive analytics. This study compares the effectiveness of CNN and LSTM on a database of 200 deanonymized hospital data and patient records chosen from MIMIC-III. The database contains information on the patient's demographics, medical history, lab findings, and treatment outcomes. Data quality was enhanced through preprocessing, which included encoding, normalization, and missing value imputation. LSTM and medical imaging CNN were trained on sequential patient data using the Adam optimizer and Cross-Entropy Loss, and the hyperparameters were adjusted. AUC-ROC, F1-score, accuracy, precision, and recall were used to gauge a model's performance. Given its 91.2% accuracy and 93.4 AUC-ROC, the results show that LSTM outperformed CNN and is more adept at processing sequential health data. According to the feature importance analysis, the two most important predictors are blood pressure (22.1%) and glucose (18.7%). The article demands the future healthcare predictive capabilities of deep learning and suggests that more research should be conducted on the combination of multimodal data and real-time patient monitoring to further offer clinical uses.

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