Murray CJ, Ikuta KS, Sharara F, Swetschinski L, Robles Aguilar G, Gray A et al (2022) Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 399:629–655
Zhang C, Fu X, Liu Y, Zhao H, Wang G (2024) Burden of infectious diseases and bacterial antimicrobial resistance in China: a systematic analysis for the global burden of disease study 2019. Lancet Reg Health West Pac 43:1–12
Majumder MAA, Rahman S, Cohall D, Bharatha A, Singh K, Haque M et al (2020) Antimicrobial stewardship: Fighting antimicrobial resistance and protecting global public health. Infect Drug Resist 13:4713–4738
Article PubMed PubMed Central CAS Google Scholar
Aiesh BM, Nazzal MA, Abdelhaq AI, Abutaha SA, Zyoud SH, Sabateen A (2023) Impact of an antibiotic stewardship program on antibiotic utilization, bacterial susceptibilities, and cost of antibiotics. Sci Rep. 13:1–9. https://doi.org/10.1038/s41598-023-32329-6
Fanelli U, Chiné V, Pappalardo M, Gismondi P, Esposito S (2020) Improving the quality of hospital antibiotic use: impact on multidrug-resistant bacterial infections in children. Front Pharmacol 11:2019–2021
Ali T, Ahmed S, Aslam M (2023) Artificial intelligence for antimicrobial resistance prediction: challenges and opportunities towards practical implementation. Antibiotics 12:523
Article PubMed PubMed Central Google Scholar
Pinto-de-Sá R, Sousa-Pinto B, Costa-de-Oliveira S (2024) Brave new world of artificial intelligence: its use in antimicrobial stewardship—a systematic review. Antibiotics 13:307
Article PubMed PubMed Central Google Scholar
Guni A, Sounderajah V, Whiting P, Bossuyt P, Darzi A, Ashrafian H (2024) A revised tool for the quality assessment of diagnostic accuracy studies utilising AI: protocol for QUADAS-AI. JMIR Res Protoc 13:e58202
Article PubMed PubMed Central Google Scholar
Yarahuan JKW, Kisvarday S, Kim E, Yan AP, Nakamura MM, Jones SB et al (2024) An Algorithm to Assess Guideline Concordance of Antibiotic Choice in Community-Acquired Pneumonia. Hosp Pediatr 14:137–145
Agbaria AH, Salman A, Beck G, Lapidot I, Rich DH, Kapelushnik J et al (2019) Potential of bacterial infection diagnosis using infrared spectroscopy of WBC and machine learning algorithms. Optics InfoBase Conference Papers. Part F142-:2024
Imamović E, Deumić A, Khouly A, Pisil KT, Avdić E, Hukić M et al (2021) Prediction of multi-drug resistance in escherichia coli using machine learning algorithms. IFMBE Proc 84:155–163
de Vries S, ten Doesschate T, Totté JEE, Heutz JW, Loeffen YGT, Oosterheert JJ et al (2022) A semi-supervised decision support system to facilitate antibiotic stewardship for urinary tract infections. Comput Biol Med 146:105621
Shi ZY, Hon JS, Cheng CY, Chiang HT, Huang HM (2022) Applying machine learning techniques to the audit of antimicrobial prophylaxis. Appl Sci (Switzerland) 12:2586
Moehring RW, Phelan M, Lofgren E, Nelson A, Dodds Ashley E, Anderson DJ et al (2021) Development of a machine learning model using electronic health record data to identify antibiotic use among hospitalized patients. JAMA Netw Open 4:1–12
Beaudoin M, Kabanza F, Nault V, Valiquette L (2016) Evaluation of a machine learning capability for a clinical decision support system to enhance antimicrobial stewardship programs. Artif Intell Med 68:29–36. https://doi.org/10.1016/j.artmed.2016.02.001
Feretzakis G, Sakagianni A, Loupelis E, Kalles D, Skarmoutsou N, Martsoukou M et al (2021) Machine learning for antibiotic resistance prediction: A prototype using off-the-shelf techniques and entry-level data to guide empiric antimicrobial therapy. Healthc Inform Res 27:214–221
Article PubMed PubMed Central Google Scholar
Bystritsky RJ, Beltran A, Young AT, Wong A, Hu X, Doernberg SB (2020) Machine learning for the prediction of antimicrobial stewardship intervention in hospitalized patients receiving broad-spectrum agents. Infect Control Hosp Epidemiol 41:1022–1027
Mancini A, Vito L, Marcelli E, Piangerelli M, De Leone R, Pucciarelli S et al (2020) Machine learning models predicting multidrug resistant urinary tract infections using “dsaaS.” BMC Bioinforma 21:1–12. https://doi.org/10.1186/s12859-020-03566-7
Stracy M, Snitser O, Yelin I, Amer Y, Parizade M, Katz R et al (1979) Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections. Science 2022(375):889–894
Corbin CK, Sung L, Chattopadhyay A, Noshad M, Chang A, Deresinksi S et al (2022) Personalized antibiograms for machine learning driven antibiotic selection. Commun Med 2:38
Article PubMed PubMed Central Google Scholar
Eickelberg G, Sanchez-Pinto LN, Luo Y (2020) Predictive modeling of bacterial infections and antibiotic therapy needs in critically ill adults. J Biomed Inform 109:103540. https://doi.org/10.1016/j.jbi.2020.103540
Article PubMed PubMed Central Google Scholar
Goodman KE, Heil EL, Claeys KC, Banoub M, Bork JT (2022) Real-world antimicrobial stewardship experience in a large academic medical center: using statistical and machine learning approaches to identify intervention “Hotspots” in an antibiotic audit and feedback program. Open Forum Infect Dis 9:ofac289
Article PubMed PubMed Central Google Scholar
Tzelves L, Lazarou L, Feretzakis G, Kalles D, Mourmouris P, Loupelis E et al (2022) Using machine learning techniques to predict antimicrobial resistance in stone disease patients. World J Urol. 40:1731–6. https://doi.org/10.1007/s00345-022-04043-x
Article PubMed CAS Google Scholar
Oonsivilai M, Mo Y, Luangasanatip N, Lubell Y, Miliya T, Tan P et al (2018) Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children’s hospital in Cambodia. Wellcome Open Res 3:1–18
Bolton WJ, Rawson TM, Hernandez B, Wilson R, Antcliffe D, Georgiou P et al (2022) Machine learning and synthetic outcome estimation for individualised antimicrobial cessation. Front Digit Health 4:1–12
Wong JG, Aung AH, Lian W, Lye DC, Ooi CK, Chow A (2020) Risk prediction models to guide antibiotic prescribing: a study on adult patients with uncomplicated upper respiratory tract infections in an emergency department. Antimicrob Resist Infect Control 9:1–11
Viswanathan V, Govindan S, Selvaraj B, Rupert S, Kumar R (2024) A clinical study to evaluate autofluorescence imaging of diabetic foot ulcers using a novel artificial intelligence enabled noninvasive device. Int J Low Extrem Wounds 23:169–176
Article PubMed CAS Google Scholar
Caǧlayan Ç, Barnes SL, Pineles LL, Harris AD, Klein EY (2022) A data-driven framework for identifying intensive care unit admissions colonized with multidrug-resistant organisms. Front Public Health 10:1–17
Wang Y, Wang G, Zhao Y, Wang C, Chen C, Ding Y et al (2023) A deep learning model for predicting multidrug-resistant organism infection in critically ill patients. J Intensive Care. 11:1–11. https://doi.org/10.1186/s40560-023-00695-y
Article PubMed PubMed Central Google Scholar
McGuire RJ, Yu SC, Payne PRO, Lai AM, Vazquez-Guillamet MC, Kollef MH et al (2021) A pragmatic machine learning model to predict carbapenem resistance. Antimicrob Agents Chemother 65:1–10
Tsurumi A, Flaherty PJ, Que YA, Ryan CM, Banerjee A, Chakraborty A et al (2023) A preventive tool for predicting bloodstream infections in children with burns. Shock 59:393–399
Article PubMed PubMed Central CAS Google Scholar
Chan ALF, Chen JX, Wang HY (2006) Application of data mining to predict the dosage of vancomycin as an outcome variable in a teaching hospital population. Int J Clin Pharmacol Ther. 44:533–8
Article PubMed CAS Google Scholar
Cai T, Anceschi U, Prata F, Collini L, Brugnolli A, Migno S et al (2023) Artificial intelligence can guide antibiotic choice in recurrent UTIs and become an important aid to improve antimicrobial stewardship. Antibiotics. 12:375
Article PubMed PubMed Central CAS Google Scholar
Guerrero-López A, Sevilla-Salcedo C, Candela A, Hernández-García M, Cercenado E, Olmos PM et al (2023) Automatic antibiotic resistance prediction in Klebsiella pneumoniae based on MALDI-TOF mass spectra. Eng Appl Artif Intell 118:105644. https://doi.org/10.1016/j.engappai.2022.105644
Abu-Aqil G, Sharaha U, Suleiman M, Riesenberg K, Lapidot I, Salman A et al (2022) Culture-independent susceptibility determination of E. c
留言 (0)