The Application of Deep Learning in Analysing Electronic Health Records for Improved Patient Outcomes.


  • Sathishkumar Chintala Senior Engineer AI and ML Texas, USA.


Deep Learning, Electronic Health Records, Clinical Decision Support, Patient Stratification, Neural Networks, GAN, CNN, RNN


Deep learning techniques like neural networks show promise for extracting insights from electronic health records (EHRs) to enhance clinical decision-making and improve patient outcomes. Recurrent and convolutional approaches demonstrate particular efficacy for predictive tasks based on longitudinal EHR data. However, significant barriers around model interpretability, data constraints, and real-world integration must still be addressed for broader adoption. This paper reviews recent literature on deep learning for EHR analysis including predictive modelling, imaging, and patient risk stratification. Based on promise but with challenges remaining, recommendations focus on methods to enable translation into clinical practice through improved user-centered design. If key next steps around transparency and standards are achieved, hybrid deep learning EHR systems hold immense potential to augment data-driven precision medicine.


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How to Cite

Chintala, S. (2024). The Application of Deep Learning in Analysing Electronic Health Records for Improved Patient Outcomes. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 223–228. Retrieved from



Research Article