Evaluation of word embedding models to extract and predict surgical data in breast cancer - Giuseppe Sgroi; Giulia Russo; Anna Maglia; Giuseppe Catanuto; Peter Barry; Andreas Karakatsanis; Nicola Rocco; ETHOS Working Group; Francesco Pappalardo (BMC Bioinformatics, 2022)
Abstract Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data.