Healthc Inform Res.  2021 Apr;27(2):168-171. 10.4258/hir.2021.27.2.168.

Artificial Intelligence: The New Alexander Fleming

• Almallah Z
• El-Lababidi R
• Shamout F
• Doyle DJ

• KMID: 2515920
• DOI: http://doi.org/10.4258/hir.2021.27.2.168

Figure

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  Figure 1 An example of an expert system rule pertaining to microbiology. This rule considers three organism-related factors (Gram stain, morphology, and conformation) to produce a conclusion that is probabilistic rather than certain. Example derived from Johnson L, Keravnou ET. Expert systems technology: a guide. Cambridge (MA): Abacus Press; 1985.

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  Figure 2 Illustration of some of the known mechanisms of antibiotic action. These include the inhibition of cell wall biosynthesis, inhibition of nucleic acid metabolism and repair, and protein synthesis inhibition, in addition to cell membrane disruption. Adapted from https://commons.wikimedia.org/wiki/File:Antibiotics_Mechanisms_of_action.png.

Reference

References

1. Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A deep learning approach to antibiotic discovery. Cell. 2020; 181(2):475–83.

2. Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, et al. The Drug Repurposing Hub: a next-generation drug library and information resource. Nat Med. 2017; 23(4):405–8.

3. Colman AM, Krockow EM, Chattoe-Brown E, Tarrant C. Medical prescribing and antibiotic resistance: a game-theoretic analysis of a potentially catastrophic social dilemma. PLoS One. 2019; 14(4):e0215480.

4. Zilahi G, Artigas A, Martin-Loeches I. What’s new in multidrug-resistant pathogens in the ICU? Ann Intensive Care. 2016; 6(1):96.

5. Su LH, Chen IL, Tang YF, Lee JS, Liu JW. Increased financial burdens and lengths of stay in patients with healthcare-associated infections due to multidrug-resistant bacteria in intensive care units: a propensity-matched case-control study. PLoS One. 2020; 15(5):e0233265.

6. Ho VP, Kaafarani H, Rattan R, Namias N, Evans H, Zakrison TL. Sepsis 2019: what surgeons need to know. Surg Infect (Larchmt). 2020; 21(3):195–204.

7. Arefian H, Heublein S, Scherag A, Brunkhorst FM, Younis MZ, Moerer O, et al. Hospital-related cost of sepsis: a systematic review. J Infect. 2017; 74(2):107–17.

8. Khan A, Miller WR, Arias CA. Mechanisms of antimicrobial resistance among hospital-associated pathogens. Expert Rev Anti Infect Ther. 2018; 16(4):269–87.

9. Garcia-Vidal C, Sanjuan G, Puerta-Alcalde P, Moreno-Garcia E, Soriano A. Artificial intelligence to support clinical decision-making processes. EBioMedicine. 2019; 46:27–9.

10. Downing NL, Rolnick J, Poole SF, Hall E, Wessels AJ, Heidenreich P, et al. Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation. BMJ Qual Saf. 2019; 28(9):762–8.

11. Alam N, Hobbelink EL, van Tienhoven AJ, van de Ven PM, Jansma EP, Nanayakkara PW. The impact of the use of the Early Warning Score (EWS) on patient outcomes: a systematic review. Resuscitation. 2014; 85(5):587–94.

12. Schinkel M, Paranjape K, Nannan Panday RS, Skyttberg N, Nanayakkara PW. Clinical applications of artificial intelligence in sepsis: a narrative review. Comput Biol Med. 2019; 115:103488.

13. Giacobbe DR, Mora S, Giacomini M, Bassetti M. Machine learning and multidrug-resistant gram-negative bacteria: an interesting combination for current and future research. Antibiotics (Basel). 2020; 9(2):54.

14. Beaudoin M, Kabanza F, Nault V, Valiquette L. Evaluation of a machine learning capability for a clinical decision support system to enhance antimicrobial stewardship programs. Artif Intell Med. 2016; 68:29–36.

15. Roshanov PS, Fernandes N, Wilczynski JM, Hemens BJ, You JJ, Handler SM, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 2013; 346:f657.