Academic Journal
Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.
| Title: | Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome. |
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| Authors: | Austin Chou, Abel Torres-Espin, Nikos Kyritsis, J Russell Huie, Sarah Khatry, Jeremy Funk, Jennifer Hay, Andrew Lofgreen, Rajiv Shah, Chandler McCann, Lisa U Pascual, Edilberto Amorim, Philip R Weinstein, Geoffrey T Manley, Sanjay S Dhall, Jonathan Z Pan, Jacqueline C Bresnahan, Michael S Beattie, William D Whetstone, Adam R Ferguson, TRACK-SCI Investigators |
| Source: | PLoS ONE, Vol 17, Iss 4, p e0265254 (2022) |
| Publisher Information: | Public Library of Science (PLoS), 2022. |
| Publication Year: | 2022 |
| Collection: | LCC:Medicine LCC:Science |
| Subject Terms: | Medicine, Science |
| More Details: | Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for reproducible modeling results. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics to assess model reproducibility. 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 to optimize the intraoperative hemodynamic range during injury-related surgery and additionally identified a strong detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured how evolving clinical practices such as faster time-to-surgery and blood pressure management affect clinical model development. Altogether, we illustrate how expert-augmented AutoML improves inferential reproducibility for biomedical discovery and can ultimately build trust in AI processes towards effective clinical integration. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1932-6203 |
| Relation: | https://doaj.org/toc/1932-6203 |
| DOI: | 10.1371/journal.pone.0265254 |
| Access URL: | https://doaj.org/article/bec17b8aaf234782a7d80a492d9dbb4e |
| Accession Number: | edsdoj.bec17b8aaf234782a7d80a492d9dbb4e |
| Database: | Directory of Open Access Journals |
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| ISSN: | 19326203 |
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| DOI: | 10.1371/journal.pone.0265254 |
| Published in: | PLoS ONE |
| Language: | English |