A Survey of AI-Driven Techniques for Enhancing Clinical Decision Support in Modern Healthcare Systems
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P129Keywords:
Healthcare, Clinical Decision Support Systems, Machine Learning, Predictive Analytics, Diagnosis, Deep Learning, Electronic Health RecordsAbstract
Modern healthcare would not be possible without clinical decision support systems (CDSS), which aid doctors in making quick and accurate choices. By incorporating AI, CDSS has evolved into a sophisticated system that can sift through mountains of patient data, spot trends that might not be immediately obvious, and soffer recommendations supported by evidence. AI-powered CDSS can enhance diagnostic precision, facilitate early disease forecasting, and aid in individual treatment plans. This study suggests a model that combines a Recurrent Neural Network with a Gated Recurrent Unit (RNN+GRU) to improve the accuracy (ACC) of clinical predictions. The model tested on the MIMIC-IV dataset. The proposed hybrid model is successful in capturing the sequential clinical patterns and is able to learn without overfitting. The proposed model showed to be more accurate than the other models used, with a value of 99.71% ACC, which is higher than the ACC of the LSTM model with 82% ACC, Adaboost model with 92.9% ACC, and XGboost model with 95.8% ACC. The comparison shows that the proposed framework has higher prediction performance, reliability and generalization ability. The developed model has the potential to be a powerful tool in the era of modern healthcare, enabling intelligent healthcare analytics, patient risk assessment, and effective clinical decision-making.
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