Dyar, O. J., Huttner, B., Schouten, J. & Pulcini, C. What is antimicrobial stewardship? Clin. Microbiol. Infect. 23, 793–798 (2017).
Oosterheert, J. J. et al. Effectiveness of early switch from intravenous to oral antibiotics in severe community acquired pneumonia: multicentre randomised trial. BMJ 333, 1193 (2006).
Agency, U. H. S. Antimicrobial stewardship: Start smart – then focus https://www.gov.uk/government/publications/antimicrobial-stewardship-start-smart-then-focus. (2015).
Davar, K. et al. Can the Future of ID Escape the Inertial Dogma of Its Past? : The Exemplars of Shorter Is Better and Oral Is the New IV. Open Forum Infectious Diseases ofac706, https://doi.org/10.1093/ofid/ofac706 (2022).
Gilchrist, M. et al. Outpatient parenteral antimicrobial therapy (OPAT) in the UK: findings from the BSAC National Outcomes Registry (2015–19). J. Antimicrob. Chemother. 77, 1481–1490 (2022).
Platts, S., Payne, B. A. I., Price, D. A., Pareja-Cebrian, L. & Schwab, U. Oral step-down for Staphylococcus aureus bacteraemia: An opportunity for antimicrobial stewardship? Clin. Infect. Pract. 16, 100202 (2022).
Kaasch, A. J. et al. Early oral switch in low-risk Staphylococcus aureus bloodstream infection https://www.medrxiv.org/content/10.1101/2023.07.03.23291932v1. (2023).
Spellberg, B., Chambers, H. F., Musher, D. M., Walsh, T. L. & Bayer, A. S. Evaluation of a paradigm shift from intravenous antibiotics to oral step-down therapy for the treatment of infective endocarditis: a narrative review. JAMA Int. Med. 180, 769–777 (2020).
Iversen, K. et al. Partial oral versus intravenous antibiotic treatment of endocarditis. N. Engl. J. Med. 380, 415–424 (2019).
Li, H.-K. et al. Oral versus Intravenous Antibiotics for Bone and Joint Infection. N. Engl. J. Med. 380, 425–436 (2019).
Wald-Dickler, N. et al. Oral Is the New IV. Challenging decades of blood and bone infection dogma: a systematic review. Am. J. Med. 135, 369–379.e1 (2022).
Ray-Barruel, G., Xu, H., Marsh, N., Cooke, M. & Rickard, C. M. Effectiveness of insertion and maintenance bundles in preventing peripheral intravenous catheter-related complications and bloodstream infection in hospital patients: a systematic review. Infect. Dis. Health 24, 152–168 (2019).
McMeekin, N. et al. Cost-effectiveness of oral versus intravenous antibiotics (OVIVA) in patients with bone and joint infection: evidence from a non-inferiority trial. Wellcome Open Res. 4, 108 (2020).
Agency, U. H. S. National antimicrobial intravenous-to-oral switch (IVOS) criteria for early switch https://www.gov.uk/government/publications/antimicrobial-intravenous-to-oral-switch-criteria-for-early-switch/national-antimicrobial-intravenous-to-oral-switch-ivos-criteria-for-early-switch. (2022).
Hospenthal, D. R., Waters, C. D., Beekmann, S. E. & Polgreen, P. M. Practice patterns of infectious diseases physicians in transitioning from intravenous to oral therapy in patients with bacteremia. Open Forum Infect. Dis. 7, ofz386 (2020).
Buis, D. T. P. et al. Current clinical practice in antibiotic treatment of Staphylococcus aureus bacteraemia: results from a survey in five European countries. J. Antimicrob. Chemother. 77, 2827–2834 (2022).
Rawson, T. M. et al. A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately? Clin. Microbiol. Infect. 23, 524–532 (2017).
Peiffer-Smadja, N. et al. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin. Microbiol. Infect. 26, 584–595 (2020).
Bolton, W. J. et al. Machine learning and synthetic outcome estimation for individualised antimicrobial cessation. Frontiers in Digital Health 4 https://www.frontiersin.org/articles/10.3389/fdgth.2022.997219. (2022).
D’Hondt, E., Ashby, T. J., Chakroun, I., Koninckx, T. & Wuyts, R. Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit. Commun. Med. 2, 1–12 (2022).
Johnson, A. et al. Mimic-iv https://physionet.org/content/mimiciv/1.0/. (2021).
Goldberger, A. L. et al. PhysioBank, PhysioToolkit, and PhysioNet. Circulation 101, e215–e220 (2000).
Pollard, T. J. et al. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci. Data 5, 180178 (2018).
Pollard, T. J. et al. The eICU Collaborative Research Database (version 2.0). PhysioNet, https://doi.org/10.13026/C2WM1R (2019).
Gerber, J. S. et al. Development and application of an antibiotic spectrum index for benchmarking antibiotic selection patterns across hospitals. Infect. Control Hosp. Epidemiol. 38, 993–997 (2017).
Lundberg, S. M. & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. In Guyon, I.et al. (eds.) Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, Inc., 2017). https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf.
Youden, W. J. Index for rating diagnostic tests. Cancer 3, 32–35 (1950).
van den Broek, A. K., Prins, J. M., Visser, C. E. & van Hest, R. M. Systematic review: the bioavailability of orally administered antibiotics during the initial phase of a systemic infection in non-ICU patients. BMC Infect. Dis. 21, 285 (2021).
Crabbe, J., Qian, Z., Imrie, F. & van der Schaar, M. Explaining Latent Representations with a Corpus of Examples. In Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Vaughan, J. W. (eds.) Advances in Neural Information Processing Systems, vol. 34, 12154–12166 (Curran Associates, Inc., 2021). https://proceedings.neurips.cc/paper/2021/file/65658fde58ab3c2b6e5132a39fae7cb9-Paper.pdf.
Hardt, M., Price, E. & Srebro, N. Equality of Opportunity in Supervised Learning. arXiv:1610.02413 [cs] http://arxiv.org/abs/1610.02413. (2016).
Afrose, S., Song, W., Nemeroff, C. B., Lu, C. & Yao, D. D. Subpopulation-specific machine learning prognosis for underrepresented patients with double prioritized bias correction. Commun. Med. 2, 1–14 (2022).
Schouten, B. et al. Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators. J. Med. Artif. Intell. 5 (2022).
Bolton, W. J., Badea, C., Georgiou, P., Holmes, A. & Rawson, T. M. Developing moral AI to support decision-making about antimicrobial use. Nat. Mach. Intellig. 4, 912–915 (2022).
Herm, L.-V., Heinrich, K., Wanner, J. & Janiesch, C. Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability. Int. J. Inf. Manag. 102538 https://www.sciencedirect.com/science/article/pii/S026840122200072X. (2022).
Barredo Arrieta, A. et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion 58, 82–115 (2020).
Ciná, G., Röber, T., Goedhart, R. & Birbil, I. Why we do need Explainable AI for Healthcare http://arxiv.org/abs/2206.15363. (2022).
Smith, M., Higgs, J. & Ellis, E. Factors influencing clinical decision making. Clin. Reas. Health Professions 3, 89–100 (2008).
McCarthy, K. & Avent, M. Oral or intravenous antibiotics? Australian Prescriber 43, 45–48 (2020).
Holmes, A. H. et al. Understanding the mechanisms and drivers of antimicrobial resistance. Lancet 387, 176–187 (2016).
Tamma, P. D., Miller, M. A. & Cosgrove, S. E. Rethinking how antibiotics are prescribed: incorporating the 4 moments of antibiotic decision making into clinical practice. JAMA 321, 139–140 (2019).
Charani, E. et al. Optimising antimicrobial use in humans – review of current evidence and an interdisciplinary consensus on key priorities for research. Lancet Reg. Health – Europe 7, 100161 (2021).
Cantón, R., Akova, M., Langfeld, K. & Torumkuney, D. Relevance of the consensus principles for appropriate antibiotic prescribing in 2022. J. Antimicrob. Chemother. 77, i2–i9 (2022).
Korzilius, J. W. et al. Oral antimicrobial agents in patients with short bowel syndrome: worth a try! J. Antimicrob. Chemotherapy dkad198, https://doi.org/10.1093/jac/dkad198 (2023).
Lubba, C. H. et al. catch22: CAnonical Time-series CHaracteristics. Data Min. Knowl. Discov. 33, 1821–1852 (2019).
Löning, M. et al. sktime: A Unified Interface for Machine Learning with Time Series http://arxiv.org/abs/1909.07872. (2019).
Löning, M. et al. sktime/sktime: v0.13.4 https://zenodo.org/record/7117735. (2022).
Vafaie, H. & De Jong, K. Genetic algorithms as a tool for feature selection in machine learning. In Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI ’92, 200–203 (1992).
Paszke, A. et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, 8024–8035 (Curran Associates, Inc., 2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.
Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019).
Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002).
Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization http://arxiv.org/abs/1412.6980. (2014).
Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learning Res. 12, 2825–2830 (2011).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in python. Nat. Methods 17, 261–272 (2020).
Bird, S. et al. Fairlearn: A toolkit for assessing and improving fairness in AI. Tech. Rep. MSR-TR-2020-32, Microsoft (2020). https://www.microsoft.com/en-us/research/publication/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai/.
Bolton, W. Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning. https://github.com/WilliamBolton/iv_to_oral. (2023).
![Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code. (arXiv:2312.14856v1 [cs.SE])](https://aigumbo.com/wp-content/uploads/2023/12/arxiv-logo-fb-235x190.png "Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code. (arXiv:2312.14856v1 [cs.SE])"){kind=logo}
