Detailed Investigation of the use of Text Mining and Natural Language Processing (NLPs) for Cancer Detection

Clinical decision-making can be improved by analyzing patient characteristics and results. However, most medical data is only presented in narratively written reports, making efficient data collection and analysis impossible. Manual medical record evaluation requires a significant investment of time and labor, and clinical reviewers' differences might result in inconsistent data gathering, leading to biased study results and incorrect assumptions. Using natural language processing (NLP), medical data can automatically and deterministically be extracted from free-text clinical reports. Furthermore, it can potentially increase the pace and scope of clinical research. Comparing machine learning versus non-machine learning approaches, machine learning has proven more effective in solving many issues in various medical fields. This review explains how clinical note databases have been used to apply machine learning-based "Natural Language Processing (ML-NLP)". Examples of optimization algorithms are given to illustrate how straightforward and useful they are in the context of clinical note databases. A secondary data collection method is used for this research to gather relevant data and statistical data related to the research topic.