Towards Precision Diagnosis: Integrating Lexical Analysis and Deep Learning for Uncertainty Detection and Quantification in Clinical Reports

Uncertainty in medical reports poses significant challenges in clinical decision-making, patient management, and research. The presence of vague terms, ambiguous statements, or conflicting information can lead to misinterpretation and potential harm to patients. Quantifying this uncertainty is crucial for understanding the confidence level associated with clinical findings and recommendations. This study focuses on developing a Natural Language Processing (NLP) framework to quantify uncertainty in medical reports. Our approach involves the extraction of linguistic cues and patterns indicative of uncertainty, followed by a systematic classification and quantification process. By leveraging machine learning algorithms trained on lexicon proximity-based annotated medical texts, we aim to detect uncertain clinical reports. Additionally, we added an ensemble block to refine the dataset further to eradicate the false positive ratio within the dataset and feed the refined dataset to a BERT model which reduced performance error by 30% correspondingly ensuring a high prediction confidence score which is a very significant improvement in terms of medical application systems.