Recent math developments, especially statistical modeling, profoundly impact healthcare. Firstly, statistical modeling equips healthcare professionals with advanced tools for analyzing complex healthcare datasets, improving diagnostic accuracy and treatment planning by revealing hidden insights. Secondly, it supports evidence-based decision-making by quantifying treatment effectiveness, assessing risks, and evaluating interventions. By relying on empirical evidence rather than intuition, healthcare providers can make informed decisions that optimize patient outcomes. Additionally, it empowers early identification of high-risk patients, personalizes interventions, optimizes resource allocation, and enhances healthcare efficiency. Lastly, statistical modeling aids in quality improvement by identifying areas for enhancement and monitoring performance indicators. Overall, statistical modeling transforms healthcare, enhancing patient care, resource allocation, and decision-making, paving the way for a data-driven future in medicine.
Top1. Introduction
Statistical modeling is a critical tool in healthcare that plays a pivotal role in analyzing complex data and making informed decisions to enhance patient outcomes. By developing mathematical models and applying statistical techniques to various healthcare data sets, statistical modeling enables the prediction of disease outcomes, evaluation of treatment effectiveness, efficient allocation of healthcare resources, risk assessment, and population health management. This paper explores the diverse applications of statistical modeling in healthcare, highlighting the works of some researchers, who have contributed significant insights to the field. In recent years, researchers have made significant contributions to the field of healthcare through the application of statistical modeling techniques (Khang & Ragimova et al., 2022).
Moshayedi et al. (2023) demonstrates the application of statistical modeling principles in understanding and advancing research on E-nose technology and its diverse applications (Ata et al., 2023). Yalin Vu et al. utilized XGBoost-BLR, a statistical modeling approach, to predict the specific type of Type-II Diabetes Mellitus (T2DM) by analyzing selected features and employing data preprocessing techniques. Their study achieved high identification rates in two databases, highlighting the potential of statistical modeling in improving diabetes diagnosis and management (Yalin et al., 2022).
The impact of COVID-19 on various health aspects has also been investigated using statistical modeling. Chen et al. focused on assessing the effects of COVID-19-related disruptions on dengue cases. They employed a Bayesian regression model, considering factors such as dengue incidence, climate, population, and COVID-19 measures. Their analysis revealed a significant decline in dengue cases during the pandemic, emphasizing the indirect impact of COVID-19 on other infectious diseases (Yuyang et al., 2022).
Statistical modeling has proven valuable in projecting and understanding disease trends. Luo et al. projected cancer incidence and mortality rates in Australia, utilizing statistical modeling methods and considering multiple factors. Their study revealed declining incidence rates for males, stable rates for females, and continuous decreases in mortality rates, providing insights into cancer control strategies (Qingwei et al., 2022).
The mental health outcomes of children and adolescents have also been explored using statistical modeling. Geweniger et al. (2022) investigated the relationship between socioeconomic status, disease complexity, pandemic burden, and mental health outcomes in Germany. By employing statistical modeling techniques, they shed light on the impact of various factors on mental health, contributing to the understanding of pediatric mental health during challenging times (Anne et al., 2022).
The COVID-19 pandemic has prompted researchers to assess its impact on healthcare-associated infections (HAIs). Baker et al. conducted a study to evaluate the association between COVID-19 surges and HAIs in hospitals. They employed statistical modeling, specifically negative binomial mixed models, to analyze the impact of the pandemic on HAI and cluster rates, providing insights for infection prevention and control strategies (Meghan et al., 2022).