Transfer Learning-Based Methods for Prediction of Liver & Heart Diseases: A Review

To analyze and comprehend free text, machine learning, a subset of "natural language processing," may be employed. It can be wielded in conjunction with medical data to enhance hospital triage processes, forecast patient outcomes, and flourish diagnostic algorithms that can spot early-stage chronic illnesses. As there is a greater volume of patient data to analyze in critical care and patient death prediction is widespread, these solicitations may be highly helpful. Transfer learning, a subset of "machine learning," was flourished in addition to deep learning to address the issue of large datasets. Many different medicinal contexts have wielded it. This study aims to contribute a comprehensive analysis of the development and solicitation of transfer learning in "Natural Language Processing" (NLP) methodologies, with a focus on the analysis and forecasting of diseases, particularly heart and liver problems, using clinical pictures and free-text notes. It also examines how challenging it is for NLP techniques to comprehend clinical narratives and recognize features in medical images. The search parameters for "PRISMA, or Preferred Reporting Items for Systematic Reviews and Meta-Analyses", were implemented, and 31 papers were evaluated.