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The 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.

Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission (ICUA). The objective of this study is two-fold, one substantive and one methodological: substantively to evaluate the association of demographic and health records with two related, yet different, outcomes of severe COVID-19 (viz., death and ICUA); methodologically to compare interpretations based on logistic regression and on gradient-boosted decision tree (GBDT) predictions interpreted by means of the Shapley impacts of covariates. Very different association of some factors, e.g., obesity and chronic respiratory diseases, with death and ICUA may guide review of practice. Shapley explanation of GBDTs identified varying effects of some factors among patients, thus emphasising the importance of individual patient assessment. The results of this study are also relevant for the evaluation of complex automated clinical decision systems, which should optimise prediction scores whilst remaining interpretable to clinicians and mitigating potential biases.

The common zoonotic pathogen Campylobacter coli is an important cause of bacterial gastroenteritis worldwide but its evolution is incompletely understood. Using multilocus sequence type (MLST) data of 7 housekeeping genes from a national survey of Campylobacter in Scotland (2005/6), and a combined population genetic-phylogenetics approach, we investigated the evolutionary history of C. coli. Genealogical reconstruction of isolates from clinical infection, farm animals and the environment, revealed a three-clade genetic structure. The majority of farm animal, and all disease causing genotypes belonged to a single clade (clade 1) which had comparatively low synonymous sequence diversity, little deep branching genetic structure, and a higher number of shared alleles providing evidence of recent clonal decent. Calibration of the rate of molecular evolution, based on within-species genetic variation, estimated a more rapid rate of evolution than in traditional estimates. This placed the divergence of the clades at less than 2500 years ago, consistent with the introduction of an agricultural niche having had an effect upon the evolution of the C. coli clades. Attribution of clinical isolate genotypes to source, using an asymmetric island model, confirmed that strains from chicken and ruminants, and not pigs or turkeys, are the principal source of human C. coli infection. Taken together these analyses are consistent with an evolutionary scenario describing the emergence of agriculture-associated C. coli lineage that is an important human pathogen.

Despite being the agent of life-threatening meningitis, Neisseria meningitidis is usually carried asymptomatically in the nasopharynx of humans and only occasionally causes disease. The genetic bases for virulence have not been entirely elucidated and the search for new virulence factors in this species is hampered by the lack of an animal model representative of the human disease. As an alternative strategy we employ a molecular epidemiological approach to establish a statistical association of a candidate virulence gene with disease in the human population. We examine the distribution of a previously-identified genetic element, a temperate bacteriophage, in 1288 meningococci isolated from cases of disease and asymptomatic carriage. The phage was over-represented in disease isolates from young adults indicating that it may contribute to invasive disease in this age group. Further statistical analysis indicated that between 20% and 45% of the pathogenic potential of the five most common disease-causing meningococcal groups was linked to the presence of the phage. In the absence of an animal model of human disease, this molecular epidemiological approach permitted the estimation of the influence of the candidate virulence factor. Such an approach is particularly valuable in the investigation of exclusively human diseases.