Extraction of Unstructured Electronic Healthcare Records using Natural Language Processing

Artificial Intelligence in the healthcare sector is becoming increasingly essential to extract huge texts for decision-making. Extraction of clinical data is a fundamental task in Medical Natural language processing. This process is still challenging through deep learning due to critical medical data, lack of interpretability, and limited availability. Text extraction from Electronic Healthcare records is crucial for improving patient care and understanding clinical decision-making. It also supports analysing the patients’ feedback and physician notes to identify areas for improvement in patients’ satisfaction and care quality. This helps in drug discovery and development through clinical data patterns. The proposed research focuses on implementing Natural language processing methods for data processing like classification and prediction, Word Sense Disambiguation, Segmentation, and word Embedding. These methods can process vast amounts of medical text data for decision support, research, and drug discovery. It can increase the possibility of identifying the patients who may at risk for certain conditions and diseases related to cancer and comparing it with their medical history. The chief aim is to provide improvised data analyses that could further improve their treatment.