Predictive modeling involves the use of mathematical or computational methods to create models that can forecast future outcomes. While equation-based models are used for the former approach, simulation techniques are required for the latter. Predictive modeling has numerous applications in medicine, such as clinical decision-making and clinical trials; however, its potential remains largely untapped in this field due to various challenges. The application of these techniques to the medical domain is particularly challenging because it deals with a dynamic nature of this discipline and complexity of patient populations treated in modern healthcare settings. Furthermore, developing and implementing effective predictive models requires a deep understanding of data being used along with adequate resources to support model development and implementation. A useful glossary table consisting of commonly used terms can be found in Table 1.

The development of various software tools in the medical domain has significantly improved the process of creating predictive models. With both open-source and commercial products available, researchers now have access to more options than ever before for their academic studies. These advancements are expected to continue benefiting medicine in years to come. However, it is essential that the use of these models be carefully scrutinized so as not to negatively impact patient care or violate ethical standards. Additionally, generating and validating a model should be a transparent and systematic process that ensures all relevant information is captured and presented comprehensibly.

There are various approaches to developing and validating predictive models. The chosen approach relies on several factors, including the model type developed, data nature, and resource availability. This entry mainly focuses on the development and validation of more complex statistical models. These models differ from mechanistic models that rely on modeling phenomena studied using mathematical equations. Statistical models use empirical equations to capture statistical relationships between different variables instead of relying solely on a modeling approach as mechanistic ones do. This difference also applies to computer models. Additionally, during the predictive modeling process, there is often a coupling between mechanistic and statistical models in the prediction process. Climate models serve as an example since they are based on physical laws (represented by mathematical equations) with their parameters controlled by data and statistical models. However, all of these models share a similar workflow (see Figure 1); the first step involves gathering data for the model followed by developing either a mathematical model or simulation capable of predicting specific outcomes associated with an event. It is crucial to assess the precision of simulations by subjecting them to examinations with datasets that were not employed during the model’s construction. This ensures that the model can perform accurately when applied to different datasets. Once validated, it can make predictions about future events based on historical data. These predictions are useful in making decisions regarding patient care, managing hospital resources, or evaluating drug effectiveness. To sum up, the act of creating a model should not be seen as a final objective in itself. Instead, it is crucial to continually assess and confirm its effectiveness through iterative procedures aimed at achieving optimal results.

A clear understanding of the advantages and limitations of this approach is essential for its successful implementation. This entry presents an overview of the key principles of predictive modeling, along with some challenges associated with its use in medicine. It also discusses recent developments in this field and potential future applications. The first section provides an overview of predictive modeling’s key principles, followed by a discussion on how it can be applied in medicine. Additionally, the entry highlights major obstacles related to implementing this technique as well as possible areas for further research that could benefit from using it.