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Artificial intelligence can improve decision-making in infection management
| File | Description | Size | Format | |
|---|---|---|---|---|
 NatureHB_Submission_Final.docx | Accepted version | 47.95 kB | Microsoft Word | View/Open |
| Title: | Artificial intelligence can improve decision-making in infection management |
| Authors: | Rawson, TM Ahmad, R Toumazou, C Georgiou, P Holmes, A |
| Item Type: | Journal Article |
| Abstract: | Antibiotic resistance is an emerging global danger. Reaching responsible prescribing decisions requires the integration of broad and complex information. Artificial intelligence tools could support decision-making at multiple levels, but building them needs a transparent co-development approach to ensure their adoption upon implementation. |
| Issue Date: | 25-Mar-2019 |
| Date of Acceptance: | 17-Mar-2019 |
| URI: | http://hdl.handle.net/10044/1/69553 |
| DOI: | 10.1038/s41562-019-0583-9 |
| ISSN: | 2397-3374 |
| Publisher: | Nature Research |
| Start Page: | 543 |
| End Page: | 545 |
| Journal / Book Title: | Nature Human Behaviour |
| Volume: | 3 |
| Copyright Statement: | © 2019 Springer Nature Publishing AG |
| Sponsor/Funder: | National Institute for Health Research National Institute for Health Research ESRC NIHR knowledge mobilisation fellowship |
| Funder's Grant Number: | II-LA-0214-20008 II-LA-0214-20008 KMRF-2015 04 007 |
| Keywords: | Social Sciences Science & Technology Life Sciences & Biomedicine Psychology, Biological Multidisciplinary Sciences Neurosciences Psychology, Experimental Psychology Science & Technology - Other Topics Neurosciences & Neurology PREDICTION SUPPORT |
| Publication Status: | Published |
| Online Publication Date: | 2019-03-25 |
| Appears in Collections: | Electrical and Electronic Engineering Department of Infectious Diseases Faculty of Engineering |