Predictive modeling terminology included in the systematic review

Term | Definition |
---|---|

Predictive modeling | The process of creating a model that can predict future outcomes or events using historical data and statistical techniques |

Statistical learning | A field of study that focuses on the development and application of statistical methods and algorithms for data analysis and prediction |

Machine learning | A subfield of AI that focuses on the development of algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed |

Deep learning | A subfield of machine learning that focuses on the development and use of AI neural networks with multiple layers to learn and extract intricate patterns and representations from data |

Logistic regression | A statistical modeling technique that predicts the probability of a binary outcome based on one or more independent variables |

Least absolute shrinkage and selection operator | A regularization technique used in regression analysis to perform variable selection and shrinkage of coefficients |

Survival analysis | A statistical method used to analyze the time until an event of interest occurs |

Cox regression | A statistical technique used for survival analysis to determine the relationship between predictor variables and the time-to-event outcome |

Nomogram | A graphical tool used in statistic and data analysis to estimate the probability of an outcome or to calculate the value of a variable based on the values of other variables |

Support vector machine | An algorithm for machine learning that is used for classification and regression analysis to solve complex nonlinear problems |

Decision tree | A hierarchical, tree-like model is used for classification and decision-making. It arranges a series of decisions and their various outcomes into a tree-like structure, where each internal node represents a feature or attribute-based decision, and each leaf node represents a class label or an outcome |

Random forest | A machine learning algorithm that incorporates ensemble learning and decision trees |

Convolutional neural network | A deep learning algorithm designed to analyze visual data. It automatically learns and extracts relevant features from the input data through the use of convolutional layers |

Radiomics | It involves quantifying medical images, including computed tomography, magnetic resonance imaging, and positron emission tomography and computed tomography. It incorporates a high-throughput extract of many quantitative image features that capture tumor phenotype, texture, shape, and spatial correlations. Applying machine learning or statistical learning to radiomic features can predict clinical outcomes or make prognostic assessments |

AI, artificial intelligence.

Ann Hepatobiliary Pancreat Surg 2024;28:14-24 https://doi.org/10.14701/ahbps.23-078

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