Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges

References

• Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1–4. doi: 10.1056/NEJMra1814259.
   PubMed Web of Science ®Google Scholar
• Giacobbe DR, Mora S, Giacomini M, et al. Machine learning and multidrug-resistant gram-negative bacteria: an interesting combination for current and future research. Antibiotics. 2020;9(2):54. doi: 10.3390/antibiotics9020054.
   Google Scholar
• Tobore I, Li J, Yuhang L, et al. Deep learning intervention for health care challenges: some biomedical domain considerations. JMIR Mhealth Uhealth. 2019;7(8):e11966. doi: 10.2196/11966.
   PubMed Web of Science ®Google Scholar
• Luz CF, Vollmer M, Decruyenaere J, et al. Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies. Clin Microbiol Infect. 2020;26(10):1291–1299. doi: 10.1016/j.cmi.2020.02.003.
   PubMed Web of Science ®Google Scholar
• Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66(1):149–153. doi: 10.1093/cid/cix731.
   PubMed Web of Science ®Google Scholar
• Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395(10236):1579–1586. doi: 10.1016/S0140-6736(20)30226-9.
   PubMed Web of Science ®Google Scholar
• Leo S, Cherkaoui A, Renzi G, et al. Mini review: clinical routine microbiology in the era of automation and digital health. Front Cell Infect Microbiol. 2020;10:582028. doi: 10.3389/fcimb.2020.582028.
   PubMed Web of Science ®Google Scholar
• Anahtar MN, Yang JH, Kanjilal S. Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research. J Clin Microbiol. 2021;59(7):e0126020. doi: 10.1128/JCM.01260-20.
   PubMed Web of Science ®Google Scholar
• Moreno-Indias I, Lahti L, Nedyalkova M, et al. Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions. Front Microbiol. 2021;12:635781. doi: 10.3389/fmicb.2021.635781.
   PubMed Web of Science ®Google Scholar
• Goodswen SJ, Barratt JLN, Kennedy PJ, et al. Machine learning and applications in microbiology. FEMS Microbiol Rev. 2021;45(5):fuab015.
   PubMed Web of Science ®Google Scholar
• Lin B, Wu S. Digital transformation in personalized medicine with artificial intelligence and the internet of medical things. OMICS. 2022;26(2):77–81. doi: 10.1089/omi.2021.0037.
   PubMed Web of Science ®Google Scholar
• Hu RS, Hesham AE, Zou Q. Machine learning and its applications for protozoal pathogens and protozoal infectious diseases. Front Cell Infect Microbiol. 2022;12:882995. doi: 10.3389/fcimb.2022.882995.
   PubMed Web of Science ®Google Scholar
• Chen Y, Xi M, Johnson A, et al. Machine learning approaches to investigate Clostridioides difficile infection and outcomes: a systematic review. Int J Med Inform. 2022;160:104706. doi: 10.1016/j.ijmedinf.2022.104706.
   PubMed Web of Science ®Google Scholar
• Golumbeanu M, Yang GJ, Camponovo F, et al. Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions. Infect Dis Poverty. 2022;11(1):61. doi: 10.1186/s40249-022-00981-1.
   PubMed Web of Science ®Google Scholar
• Dănăilă V-R, Avram S, Buiu C. The applications of machine learning in HIV neutralizing antibodies research-A systematic review. Artif Intell Med. 2022;134:102429. doi: 10.1016/j.artmed.2022.102429.
   PubMed Web of Science ®Google Scholar
• Peiffer-Smadja N, Rawson TM, Ahmad R, et al. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin Microbiol Infect. 2020;26(5):584–595. doi: 10.1016/j.cmi.2019.09.009.
   PubMed Web of Science ®Google Scholar
• Wong F, de la Fuente-Nunez C, Collins JJ. Leveraging artificial intelligence in the fight against infectious diseases. Science. 2023;381(6654):164–170. doi: 10.1126/science.adh1114.
   PubMed Web of Science ®Google Scholar
• Giacobbe DR, Signori A, Del Puente F, et al. Early detection of sepsis with machine learning techniques: a brief clinical perspective. Front Med. 2021;8:617486. doi: 10.3389/fmed.2021.617486.
   PubMed Web of Science ®Google Scholar
• Chadaga K, Prabhu S, Bhat V, et al. Artificial intelligence for diagnosis of mild-moderate COVID-19 using haematological markers. Ann Med. 2023;55(1):2233541.
   PubMed Web of Science ®Google Scholar
• Wang S-H, Zhang Y-D. Advances and challenges of deep learning. Recent Pat Eng. 2023;17(4):1–2.
   Google Scholar
• Ribeiro MT, Singh S, Guestrin C. “Why should I trust you?”: explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; San Francisco, California, USA: Association for Computing Machinery; 2016. p. 1135–1144.
   Google Scholar
• Lundberg SM, Lee SI. A unified approach to interpreting model predictions. NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems. ACM; 2017. p. 30.
   Google Scholar
• Ali S, Akhlaq F, Imran AS, et al. The enlightening role of explainable artificial intelligence in medical & healthcare domains: a systematic literature review. Comput Biol Med. 2023;166:107555. doi: 10.1016/j.compbiomed.2023.107555.
   PubMed Web of Science ®Google Scholar
• Amann J, Vetter D, Blomberg SN, et al. To explain or not to explain?-artificial intelligence explainability in clinical decision support systems. PLOS Digit Health. 2022;1(2):e0000016. doi: 10.1371/journal.pdig.0000016.
   PubMedGoogle Scholar
• Giacobbe DR, Mora S, Signori A, et al. Validation of an automated system for the extraction of a wide dataset for clinical studies aimed at improving the early diagnosis of candidemia. Diagnostics. 2023;13(5):961. doi: 10.3390/diagnostics13050961.
   Web of Science ®Google Scholar
• Drouin A, Letarte G, Raymond F, et al. Interpretable genotype-to-phenotype classifiers with performance guarantees. Sci Rep. 2019;9(1):4071. doi: 10.1038/s41598-019-40561-2.
   PubMedGoogle Scholar
• Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206–215. doi: 10.1038/s42256-019-0048-x.
   PubMedGoogle Scholar
• Balas EA, Vernon M, Magrabi F, et al. Big data clinical research: validity, ethics, and regulation. Stud Health Technol Inform. 2015;216:448–452.
   PubMedGoogle Scholar
• Wang X, Williams C, Liu ZH, et al. Big data management challenges in health research-a literature review. Brief Bioinform. 2019;20(1):156–167. doi: 10.1093/bib/bbx086.
   PubMed Web of Science ®Google Scholar
• Meszaros J, Minari J, Huys I. The future regulation of artificial intelligence systems in healthcare services and medical research in the european union. Front Genet. 2022;13:927721. doi: 10.3389/fgene.2022.927721.
   PubMed Web of Science ®Google Scholar
• Jiménez-Luna J, Grisoni F, Schneider G. Drug discovery with explainable artificial intelligence. Nat Mach Intell. 2020;2(10):573–584. 2020/10/01doi: 10.1038/s42256-020-00236-4.
   Google Scholar
• Fernandes FC, Cardoso MH, Gil-Ley A, et al. Geometric deep learning as a potential tool for antimicrobial peptide prediction. Front Bioinform. 2023;3:1216362. doi: 10.3389/fbinf.2023.1216362.
   PubMedGoogle Scholar
• Jukic M, Bren U. Machine learning in antibacterial drug design. Front Pharmacol. 2022;13:864412. doi: 10.3389/fphar.2022.864412.
   PubMed Web of Science ®Google Scholar
• Zhang Y. Fighting against COVID-19: innovations and applications. Int J Imaging Syst Tech. 2023;33(4):1111–1115. doi: 10.1002/ima.22925.
   Web of Science ®Google Scholar
• Cavallaro M, Moran E, Collyer B, et al. Informing antimicrobial stewardship with explainable AI. PLOS Digit Health. 2023;2(1):e0000162. doi: 10.1371/journal.pdig.0000162.
   PubMedGoogle Scholar