Human and machine learning pipelines for responsible clinical prediction using high-dimensional data
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This protocol outlines the development, validation, and deployment of human and machine learning models for clinical prediction using high-dimensional data, focusing on improving performance, interpretability, and generalizability.
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Abstract
Abstract This protocol aims to develop, validate, and deploy a prediction model using high dimensional data by both human and machine learning. The applicability is intended for clinical prediction in healthcare providers, including but not limited to those using medical histories from electronic health records. This protocol applies diverse approaches to improve both predictive performance and interpretability while maintaining the generalizability of model evaluation. However, some steps require expensive computational capacity; otherwise, these will take longer time. The key stages consist of designs of data collection and analysis, feature discovery and quality control, and model development, validation, and deployment.
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