Author
Cavallaro, MassimoMoran, Ed
Collyer, Benjamin
McCarthy, Noel D
Green, Christopher
Keeling, Matt J
Publication date
2023-01-05Subject
Public health. Health statistics. Occupational health. Health educationMicrobiology. Immunology
Metadata
Show full item recordAbstract
he accuracy and flexibility of artificial intelligence (AI) systems often comes at the cost of a decreased ability to offer an intuitive explanation of their predictions. This hinders trust and discourage adoption of AI in healthcare, exacerbated by concerns over liabilities and risks to patients' health in case of misdiagnosis. Providing an explanation for a model's prediction is possible due to recent advances in the field of interpretable machine learning. We considered a data set of hospital admissions linked to records of antibiotic prescriptions and susceptibilities of bacterial isolates. An appropriately trained gradient boosted decision tree algorithm, supplemented by a Shapley explanation model, predicts the likely antimicrobial drug resistance, with the odds of resistance informed by characteristics of the patient, admission data, and historical drug treatments and culture test results. Applying this AI-based system, we found that it substantially reduces the risk of mismatched treatment compared with the observed prescriptions. The Shapley values provide an intuitive association between observations/data and outcomes; the associations identified are broadly consistent with expectations based on prior knowledge from health specialists. The results, and the ability to attribute confidence and explanations, support the wider adoption of AI in healthcare.Citation
Cavallaro M, Moran E, Collyer B, McCarthy ND, Green C, Keeling MJ. Informing antimicrobial stewardship with explainable AI. PLOS Digit Health. 2023 Jan 5;2(1):e0000162. doi: 10.1371/journal.pdig.0000162Type
ArticleAdditional Links
https://journals.plos.org/digitalhealth/PMID
36812617Journal
PLOS Digital HealthPublisher
Public Library of Scienceae974a485f413a2113503eed53cd6c53
10.1371/journal.pdig.0000162