Journal of Engineering and Software Applications

Transfusion Medicine (TM) is a relatively young medical discipline supporting a large number of medical specialties on an almost daily basis. In de routine of TM large amounts of data are collected and processed in a standardized way and depending on the economy of scale practiced. Blood and its components are not without risk when transfused. 

The increasing prevalence of drug-resistant pathogens has highlighted the need for innovative approaches in drug discovery, including drug re-purposing. Artificial intelligence (AI) has emerged as a transformative tool in this endeavor, with the potential to revolutionize not only drug re-purposing for infectious diseases but also the field of hematology.

Machine learning (ML) is a branch of artificial intelligence that uses an algorithm to process the data, retrieve valuable information, learn from it, find a pattern, and make predictions. Manual data analyses suffer from several disadvantages including it is time-consuming and subject to error.

Modern advances in artificial intelligence are phenomenal as trained machine learning models are learning to solve humanities greatest problems. In addition to the many technical problems machine learning models are attempting to solve, there is a plethora of research on the possible clinical applications of machine learning.

Prostate cancer (Pca) is one of the most common cancers among men worldwide. The current screening methods lack effectiveness such as prostate-specific antigen (PSA) and Magnetic resonance imaging (MRI), and some others come with pain such as biopsy. Understanding the genomic behavior of the disease may play a key part in designing more effective, accurate, and less invasive diagnosis measures. Pca has many clinical features to describe the spread and the aggressiveness of the tumor including Gleason score, TNM staging system, and the location of the tumor in the prostate gland which is known as laterality.