Deep Learning Approaches to Optimizing Dosage and Treatment Schedules in Kidney Disease Management

The objective of this research is to investigate the optimal dosage and treatment schedules in kidney disease by means of various deep learning algorithms. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and Deep Reinforcement Learning (DRL) are applied on a dataset containing records of 1258 patients. The results show the overall promising potential of the developed methodologies. The accuracy of CNNs was 0.85, with sensitivity and specificity equal to 0.78 and 0.88, respectively. The Mean Squared Error (MSE) was characterized by low value in the case of RNNs, equal to 0.015. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were also close to zero, a phenomenon indicative of precise prediction of disease trajectories and response to the defined treatment schedules. Finally, LSTM networks showed balanced precision of 0.79 and recall of 0.82; the F1-score was equal to 0.80, indicating that the proper optimization of designed treatment strategies to specific patient conditions was possible. To illustrate, GANs returned a reasonably good Wasserstein distance of 0.035 and Jensen-Shannon Divergence of 0.052. The Fréchet Inception Distance was, however, quite high, amounting to 78.2. In turn, Deep Reinforcement Learning (DRL) algorithms achieved a reward sum of 2350 and 45.6 for the average per episode reward. The exploration rate used in the DRL model was 0.15. Therefore, the dosing regimen was optimized effectively. As a result, the outcomes described above testify to the effectiveness of deep learning-based tools in healthcare and kidney disease.