Ahmet Rıza ŞAHİN, Selma ATEŞ, Mücahid GÜNAY

Klinik Mikrobiyoloji Laboratuvarlarında Yapay Zekanın Temel İşleyiş Modelleri

Yapay zekanın tıp alanındaki ana ilgi alanı, teşhis ve tedavi önerileri sunabilecek yöntemler geliştirmek gibi görünse de hekim ve hemşire klinik karar destek sistemleri, eczane karar destek sistemleri, hasta bakımı, klinik veri havuzu oluşturulması, birimler ve kurumlar arası veri paylaşımı, depolama, yorumlayabilmeye sürecine katkı ile beraber olarak iş zekası ve makine öğrenmesi gibi sayısız alanı kapsar. Tıbbi laboratuvarlar otomasyon, uzman sistemler ve yapay zekaya doğru güçlü bir yönelimle karşı karşıya olmanın yanısıra uzman sistemlere yönelik artan bir ihtiyaç yaşamaktadır. Klinik mikrobiyoloji laboratuvarları antimikrobiyal dirence karşı mücadelede yer alabilecek veri zincirlerinin tespitinde merkezi bir unsurdur. Yapay zekanın klinik mikrobiyoloji laboratuvar kullanımına entegrasyonun amaçları arasında bireysel epidemiyolojik sürveyans, araştırma uygulamalarına ayrıntılı destek sağlamanın yanı sıra bireysel hasta bakım kalitesini artırmak yer alır. Çalışmamızda klinik mikrobiyoloji ve antibiyotik direncinin işlenmesi konusunda farklı yapay zeka çalışma prensip ve yöntemleri gözden geçirilerek, bu yöntemleri irdeleyen önemli klinik çalışmalar incelenmiştir.

Anahtar Kelimeler:

Yapay zeka, Mikrobiyoloji, Antibiyotik direnci

Basic Processing Models of Artificial Intelligence In Clinical Microbiology Laboratories

Although the main interest of artificial intelligence in medicine seems to be to develop methods that can offer diagnostic and therapeutic recommendations, it includes numerous areas such as physician and nurse clinical decision support systems, pharmacy decision support systems, patient care, clinical data pooling, data sharing between units and institutions, storage, interp- retation, business intelligence and machine learning. In addition to having a strong orientation towards automation, expert systems and artificial intelligence, medical laboratories have an increasing need especially for expert systems. Clinical microbiology laboratories are a central element in the identification of data chains that may be involved in the fight against antimicrobial resistance. By the integration of artificial intelligence to clinical microbiology laboratory use, it is aimed to provide detailed support to individual epidemiological surveillance, research appli- cations and to improve individual patient care quality. In our study, the principles and methods of the study of artificial intelligence in clinical microbiology and antibiotic resistance processing were reviewed and important clinical studies were examined.

Keywords:

Microbiology, Artificial intelligence, Antibiotic resistance,

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1. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.
2. Shortliffe, E. H., Axline, S. G., Buchanan, B. G., Merigan, T. C., & Cohen, S. N. (1973). An artificial intelligence program to advise physicians regarding antimicrobial therapy. Computers and Biomedical Research, 6(6), 544-560.
3. Demirhan, A., Kılıç, Y. A., & İnan, G. (2010). Tıpta yapay zeka uygulamaları. Yoğun Bakım Dergisi, 9(1), 31-41
4. Serhatlıoğlu, S., & Hardalaç, F. (2009). Yapay Zeka Teknikleri ve Radyolojiye Uygulanması. Fırat Tıp Dergisi, 14(1), 1-6.
5. Wraith SM, Aikins JS, Buchanan BG, et al. Computerized consultation system for selection of antimicrobial therapy.AmJ Hosp Pharm 1976; 33:1304–1308.
6. Yu VL, Buchanan BG, Shortliffe EH, et al. Evaluating the performance of a computer-based consultant. Comput Programs Biomed 1979; 9:95–102.
7. McCorduck, P. (2004). Machines who think. AK Peters.
8. McCarthy J. What is artificial intelligence? Computer Science Department, Stanford University. Available from: http://www-formal.stanford.edu/jmc/whatisai.pdf accesed time:
9. Nabiyev VV. Yapay Zeka. Ankara: Seçkin Yayınları, 2003.
10. Begley RJ, Riege M, Rosenblum J, Tseng D. Adding intelligence to medical devices. Medical Device & Diagnostic Industry Magazine 2000;3:150.
11. Industrial application of fuzzy logic control. Available from: http://www.fuzzytech.com/ Accwswd time: 20.06.2019
12. Atıcı E.,Hasta-hekim ilişkisi kavramı. Uludağ Üniversitesi Tıp Fakültesi Dergisi,2007 33(1),45-50.
13. Phuong NH, Kreinovich V. Fuzzy logic and its applications in medicine. Int J Med Informatics 2001;62:165-73.
14. JT Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh.George JK, Bo Y, editors. River Edge, NJ, USA: World Scientific Publishing Co., Inc.; 1996.
15. de Bruin, J. S., Adlassnig, K. P., Blacky, A., & Koller, W. (2016). Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic. Artificial intelligence in medicine, 69, 33-41.
16. Dumitrescu O, Dauwalder O, Lina G (2011) Present and future automation in bacteriology. Clin Microbiol Infect 17:649–650.
17. Gansel, X., Mary, M., & Van Belkum, A. (2019). Semantic data interoperability, digital medicine, and e-health in infectious disease management: a review. European Journal of Clinical Microbiology & Infectious Diseases, 38(6), 1023-1034.
18. WHO Antimicrobial resistance factsheet. http://www.who.int/mediacentre/factsheets/fs194/en/. Accessed time: May 19, 2019.
19. de Bruin JS, Seeling W, Schuh C. Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review. J Am Med Inform Assoc 2014;21(5):942–51.
20. Fragidis LL, Chatzoglou PD (2017) Development of nationwide electronic health record (ΝEHR): an international survey. Health Policy Technol 6:124–133.
21. OECD (2017) New health technologies. Organisation for Economic Co-operation and Development, Paris
22. Jensen PB, Jensen LJ, Brunak S (2012) Mining electronic health records: towards better research applications an dclinical care. Nat Rev Gen et 13:395–405.
23. Bietenbeck, A., Boeker, M., & Schulz, S. (2018). NPU, LOINC, and SNOMED CT: a comparison of terminologies for laboratory results reveals individual advantages and a lack of possibilities to encode interpretive comments. LaboratoriumsMedizin, 42(6), 267-275.
24. Petersen UM, Dybkær R, Olesen H. Properties and units in the clinical laboratory sciences. Part XXIII. The NPU terminology, principles, and implementation: a user’s guide (IUPAC Technical Report). Pure Appl Chem 2011;84:137–65.
25. Joint Committee on Nomenclature P, Units of the I, Iupac, Pontet F, Petersen UM, Fuentes-Arderiu X, et al. Clinical laboratory sciences data transmission: the NPU coding system. StudHealth Technol Inform 2009;150:265–9.
26. BIPM I, IFCC I, IUPAC I, ISO O. The international vocabulary of metrology – basic and general concepts and associated terms (VIM), 3rd ed. JCGM 200: 2012. JCGM (Joint Committee for Guides in Metrology). 2012.
27. McDonald CJ, Huff SM, Suico JG, Hill G, Leavelle D, Aller R, et al. LOINC, a universal standard for identifying laboratory observations: a 5-year update. Clin Chem 2003;49:624–33.
28. LOINC (2018) Guide for using LOINC microbiology terms. Regenstrief Institute,
29. IHTSDO (2017) SNOMED CT - starter guide
30. Bodenreider O, Stevens R (2006) Bio-ontologies: current trends and future directions. Brief Bioinform 7:256–274. https://doi.org/ 10.1093/bib/bbl027
31. Schadow G, McDonald CJ, Suico JG, Fohring U, Tolxdorff T, Units of measure in clinical information systems. J AmMed Inform Assoc (1999) 6(2):151–162
32. Schadow G, McDonald CJ (2017) The Unified Code for Units of Measure. http://unitsofmeasure.org/ucum.html. Accessed time: June 6th 2019
33. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: NY: Springer; 2009.
35. Macesic, N., Polubriaginof, F., & Tatonetti, N. P. (2017). Machine learning: novel bioinformatics approaches for combating antimicrobial resistance. Current opinion in infectious diseases, 30(6), 511-517.
36. Pesesky MW, Hussain T, Wallace M, et al. Evaluation of machine learning and rules-based approaches for predicting antimicrobial resistance profiles in Gram-negative bacilli from whole genome sequence data. Front Microbiol 2016; 7:1887.
37. Rishishwar L, Petit RA, Kraft CS, Jordan IK. Genome sequence-based discriminator for vancomycin-intermediate Staphylococcus aureus. J Bacteriol 2014; 196:940–948.
38. Drouin A, Gigue` re S, De´ raspe M, et al. Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons. BMC Genomics 2016; 17:754.
39. Santerre JW, Davis JJ, Xia F, Stevens R. Machine learning for antimicrobial resistance. arXiv.org 2016; arXiv:1607.01224v1.
40. Katuwal GJCR. Machine learning model interpretability for precision medicine. arXivorg 2016.
41. Lipton Z. The mythos of model interpretability. arXivorg 2016.