Utilizing Nlp And Machine Learning To Predict Patient Outcomes From Electronic Health Records In Cloud Environments

In stored in the cloud healthcare systems, this research investigates the incorporation of machine learning (ML) along with natural language processing (NLP) for statistical analysis employing electronic health records (EHRs). Using a design based on description and additional data collection, our own deductive approach adopts an interpretivist framework. Unstructured medical writing can yield insights that NLP techniques can use to inform the creation of machine learning models that are predictive. The outcomes demonstrate how well ML models perform in predicting patient outcomes and how useful NLP is in structuring EHR data. The study tackles cloud-based setting scalability issues and highlights the ability to interpret models by means of SHAP values.