Machine learning-based clinical prediction modeling -- A practical guide\n for clinicians

Abstract

In the emerging era of big data, larger available clinical datasets and\ncomputational advances have sparked a massive interest in machine\nlearning-based approaches. The number of manuscripts related to machine\nlearning or artificial intelligence has exponentially increased over the past\nyears. As analytical machine learning tools become readily available for\nclinicians to use, the understanding of key concepts and the awareness of\nanalytical pitfalls are increasingly required for clinicians, investigators,\nreviewers and editors, who even as experts in their clinical field, sometimes\nfind themselves insufficiently equipped to evaluate machine learning\nmethodologies. In the first section, we provide explanations on the general\nprinciples of machine learning, as well as analytical steps required for\nsuccessful machine learning-based predictive modelling - which is the focus of\nthis series. In further sections, we review the importance of resampling,\noverfitting and model generalizability as well as feature reduction and\nselection (Part II), strategies for model evaluation, reporting and discussion\nof common caveats and other points of significance (Part III), as well as offer\na practical guide to classification (Part IV) and regression modelling (Part\nV), with a complete coding pipeline. Methodological rigor and clarity as well\nas understanding of the underlying reasoning of the internal workings of a\nmachine learning approach are required, otherwise predictive applications\ndespite being strong analytical tools are not well accepted into the clinical\nroutine. Going forward, machine learning and artificial intelligence shape and\ninfluence modern medicine across disciplines including the field of\nneurosurgery.\n

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