Applications of Genetic Programming in Cancer

Artificial intelligence (AI) and machine learning (ML) methods have gained notable recognition for their innovative problem-solving approaches, which notably do not require understanding the problem’s physical underpinnings. AI applications in medicine herald a new era of digital health, assisting physicians in delivering optimal patient care. The experience and knowledge of physicians are undeniably crucial in diagnosing diseases and treating patients. In this context, AI models facilitate the rapid learning and analysis of large datasets. Consequently, with the growing volume of data collection and the refinement of AI models, AI technologies can assist physicians and health policy-makers make more precise evidence-based clinical decisions. In cancer research, AI methodologies are extensively utilized for prognostic predictions and risk assessments. Specifically, accurately categorizing cancer patients into risk groups and forecasting individual prognoses are vital for therapeutic decision-making. Like other AI techniques, genetic programming (GP) has been employed for prognostic predictions and the classification of cancer patients.Additionally, AI-assisted classification of cancer types may provide more precise criteria for distinguishing malignant and benign lesions. Preliminary studies in breast cancer utilizing GP have yielded significant diagnostic criteria for the classification of malignant lesions in screening mammography. Early cancer diagnosis and identifying individuals at risk for specialized screening programs are undoubtedly life-saving advancements in cancer research. In light of this, further investigations utilizing GP are recommended

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables

Abstract Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine the importance of laboratory predictors in the prognosis of COVID-19 patients. The present retrospective study was conducted on 1455 patients with COVID-19 (727 males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, Gonabad, Iran in 2020–2021. For each patient, the demographic characteristics, common laboratory tests at the time of admission, duration of hospitalization, admission to the intensive care unit (ICU), and mortality were collected through the electronic information system of the hospital. Then, the data were normalized and randomly divided into training and test data. Furthermore, mathematical prediction models were developed by MGGP for each gender. Finally, a sensitivity analysis was performed to determine the significance of input parameters on the COVID-19 prognosis. Based on the achieved results, MGGP is able to predict the mortality of COVID-19 patients with an accuracy of 60–92%, the duration of hospital stay with an accuracy of 53–65%, and admission to the ICU with an accuracy of 76–91%, using common hematological tests at the time of admission. Also, sensitivity analysis indicated that blood urea nitrogen (BUN) and aspartate aminotransferase (AST) play key roles in the prognosis of COVID-19 patients. AI techniques, such as MGGP, can be used in the triage and prognosis prediction of COVID-19 patients. In addition, due to the sensitivity of BUN and AST in the estimation models, further studies on the role of the mentioned parameters in the pathophysiology of COVID-19 are recommended