Expert-integrated automated machine learning uncovers hemodynamic predictors in spinal cord injury

Abstract

Automated machine learning (AutoML) is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed to create prediction models. However, successful translation of ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards discovering reproducible clinical and biological inferences. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics for performance precision and feature instability. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication and identified a detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured evolving clinical practices such as faster time-to-surgery and blood pressure management that affected clinical model validation. Altogether, we illustrate how augmenting AutoML for inferential reproducibility empowers biomedical discovery and builds trust in AI processes towards effective clinical integration.

Quantifying the Risk of General Health and Early COVID-19 Spread in Residential Buildings with Deep Learning and Expert-augmented Machine Learning

“…Most importantly, it has been noted that, due to the lack of general knowledge in a specific domain, current machine learning algorithms often make oversights that appear trivial to experts but, at the same time, are of grave consequences if decisions were made based on such oversights, especially in healthcare [39]. Iteratively combining computational tools with human expertise holds promise for the identification of treatments for rare and neglected diseases, e.g., drugs for the COVID-19 [50] and optimizing hemodynamic predictors of spinal cord injury outcome [51], in areas of biological discovery where relevant data may be lacking or hidden in the mass of available biomedical literature [50]. Recently, expertaugmented machine learning has been advanced to gain the trust of artificial intelligence in the area of healthcare, e.g., [39] and environmental science, e.g., [52] by rectifying machine learning algorithms' ungeneralizable findings, inefficient data usage, and the lack of domainspecific general knowledge.…”