Explanatory Predictive Model
An Explanatory Predictive Model is a interpretable predictive model that is an explanatory model.
- Context:
- It can be produced by an Explanatory Predictive Model Creation Task.
- See: Predictive Model, Retrospective Explanatory Model, Predictive Modeling, Causal Model.
References
2020
- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology Retrieved:2020-4-6.
- Compartmental models may be used to predict properties of how a disease spreads, for example the prevalence (total number of infected) or the duration of an epidemic. Also, the model allows for understanding how different situations may affect the outcome of the epidemic, e.g., what the most efficient technique is for issuing a limited number of vaccines in a given population.
2016
- (Felipe et al., 2016) ⇒ Carmen M. Felipe, José L. Roldán, and Antonio L. Leal-Rodríguez. (2016). “An Explanatory and Predictive Model for Organizational Agility.” Journal of Business Research 69, no. 10
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2014
- (Waljee et al., 2014) ⇒ Akbar K. Waljee, Peter DR Higgins, and Amit G. Singal. (2014). “A Primer on Predictive Models.” Clinical and translational gastroenterology, 5(1). doi:10.1038%2Fctg.2013.19
- QUOTE: ... Prediction research, which aims to predict future events or outcomes based on patterns within a set of variables, has become increasingly popular in medical research.[1] Accurate predictive models can inform patients and physicians about the future course of an illness or the risk of developing an illness and thereby help guide decisions on screening and/or treatment. For example, predictive models have been developed in gastroenterology to predict the risk of disease flares for inflammatory bowel disease and risk of hepatocellular carcinoma among patients with cirrhosis. [2, 3]
There are several important differences between traditional explanatory research and prediction research. Explanatory research typically applies statistical methods to test causal hypotheses using a priori theoretical constructs (e.g., hepatocellular carcinoma surveillance underutilization is related to provider-level factors4). In contrast, predictive research applies statistical methods and/or data mining techniques, without preconceived theoretical constructs, to predict future outcomes (e.g., predicting the risk of hospital readmission5).[6] Although predictive models may be used to provide insight into causality of pathophysiology of the outcome, causality is neither a primary aim nor a requirement for variable inclusion.[6] Noncausal predictive factors may be surrogates for other drivers of disease, with tumor markers as predictors of cancer progression or recurrence being the most common example. Unfortunately, a poor understanding of the differences in methodology between explanatory and predictive research has led to a wide variation in the methodologic quality of prediction research.[7] The aim of this primer is to describe basic methods for conducting prediction research, which can be divided into three main steps: developing a predictive model, independently validating its performance, and prospectively studying its clinical impact. …
- QUOTE: ... Prediction research, which aims to predict future events or outcomes based on patterns within a set of variables, has become increasingly popular in medical research.[1] Accurate predictive models can inform patients and physicians about the future course of an illness or the risk of developing an illness and thereby help guide decisions on screening and/or treatment. For example, predictive models have been developed in gastroenterology to predict the risk of disease flares for inflammatory bowel disease and risk of hepatocellular carcinoma among patients with cirrhosis. [2, 3]