Corpus ID: 220128116

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

  title={Machine learning-based clinical prediction modeling - A practical guide for clinicians},
  author={Julius M. Kernbach and V. Staartjes},
In the emerging era of big data, larger available clinical datasets and computational advances have sparked a massive interest in machine learning-based approaches. The number of manuscripts related to machine learning or artificial intelligence has exponentially increased over the past years. As analytical machine learning tools become readily available for clinicians to use, the understanding of key concepts and the awareness of analytical pitfalls are increasingly required for clinicians… Expand


An introduction and overview of machine learning in neurosurgical care
Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction. Expand
A Systematic Review on Machine Learning in Neurosurgery: The Future of Decision-Making in Patient Care.
This systematical review aimed to assemble the current neurosurgical literature that machine learning has been utilized, and to inform neurosurgeons on this novel method of data analysis. Expand
Scalable and accurate deep learning with electronic health records
A representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format is proposed, and it is demonstrated that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. Expand
Updating methods improved the performance of a clinical prediction model in new patients.
When the performance is poor in new patients, updating methods can be applied to adjust the model, rather than to develop a new model. Expand
Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.
Based on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Expand
Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches
Machine-learning significantly improved accuracy of prediction of premature all-cause mortality in this middle-aged population, compared to standard methods, and illustrates the value of machine-learning for risk prediction within a traditional epidemiological study design. Expand
Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury.
ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe TBI and should be rigorously validated to ensure applicability to new populations. Expand
Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures
It is suggested that reporting discrimination and calibration will always be important for a prediction model and decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Expand
Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports.
Among various NLP techniques, the bag-of-words approach combined with a LASSO regression model demonstrated the best overall performance in extracting binary outcomes from free-text clinical reports. Expand
Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes.
It is proposed that the minimum value of n should meet the following four key criteria: small optimism in predictor effect estimates as defined by a global shrinkage factor, and precise estimation of the mean predicted outcome value (model intercept). Expand