Understanding basic principles of Artificial Intelligence: a practical guide for intensivists: Basic Principles of Artificial Intelligence

Valentina Bellini; Marco Cascella; Franco Cutugno; Michele Russo; Roberto Lanza; Christian Compagnone; Elena Giovanna Bignami

doi:10.23750/abm.v93i5.13626

Understanding basic principles of Artificial Intelligence: a practical guide for intensivists

Basic Principles of Artificial Intelligence

Authors

Valentina Bellini
Marco Cascella
Franco Cutugno
Michele Russo
Roberto Lanza
Christian Compagnone
Elena Giovanna Bignami

DOI: https://doi.org/10.23750/abm.v93i5.13626

Keywords:

artificial intelligence, machine learning, intensive care, anesthesia, software, data processing

Abstract

Background and aim: Artificial intelligence was born to allow computers to learn and control their environment, trying to imitate the human brain structure by simulating its biological evolution. Artificial intelligence makes it possible to analyze large amounts of data (big data) in real-time, providing forecasts that can support the clinician’s decisions. This scenario can include diagnosis, prognosis, and treatment in anesthesiology, intensive care medicine, and pain medicine. Machine Learning is a subcategory of AI. It is based on algorithms trained for decisions making that automatically learn and recognize patterns from data. This article aims to offer an overview of the potential application of AI in anesthesiology and analyzes the operating principles of machine learning Every Machine Learning pathway starts from task definition and ends in model application.

Conclusions: High-performance characteristics and strict quality controls are needed during its progress. During this process, different measures can be identified (pre-processing, exploratory data analysis, model selection, model processing and evaluation). For inexperienced operators, the process can be facilitated by ad hoc tools for data engineering, machine learning, and analytics

References

Melanie, M. "An Introduction to Genetic Algorithms (Fifth printing ed. Vol3)." A Bradford Book The MIT Press, Cambridge, 221s (1999).

McCarthy, J. What is artificial intelligence. 2004. accessable on:: http://www-formal. stanford. edu/jmc/whatisai. html.

Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019;8(7):2328-2331. doi:10.4103/jfmpc.jfmpc_440_19

Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020 Oct;92(4):807-812. doi: 10.1016/j.gie.2020.06.040.

Borges do Nascimento IJ, Marcolino MS, Abdulazeem HM, Weerasekara I, Azzopardi-Muscat N, Gonçalves MA, et al. Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies. J Med Internet Res. 2021 Apr 13;23(4):e27275. doi: 10.2196/27275.

González García C, Núñez Valdéz ER, García Díaz V, Pelayo García-Bustelo BC, Cueva Lovelle JM. A review of artificial intelligence in the internet of things. International Journal Of Interactive Multimedia And Artificial Intelligence. 2019;5:9-20 doi: 10.9781/ijimai.2018.03.004

Bellini V, Rafano Carnà E, Russo M, Di Vincenzo F, Berghenti M, Baciarello M, et al. Artificial intelligence and anesthesia: a narrative review. Ann Transl Med. 2022;10(9):528. doi: 10.21037/atm-21-7031

Basu K, Sinha R, Ong A, Basu T. Artificial Intelligence: How is It Changing Medical Sciences and Its Future? Indian J Dermatol. 2020 Sep-Oct;65(5):365-370. doi: 10.4103/ijd.IJD_421_20.

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks? Nature. 2017;542:115–8. doi: 10.1038/nature21056.

Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018 Aug;18(8):500-510. doi: 10.1038/s41568-018-0016-5.

Ren Y, Loftus TJ, Datta S, Ruppert MM, Guan Z, Miao S, et al. Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform. JAMA Netw Open. 2022 May 2;5(5):e2211973. doi: 10.1001/jamanetworkopen.2022.11973.

Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021 Jan;26(1):80-93. doi: 10.1016/j.drudis.2020.10.010.

Hossain MA, Hossain ME, Qureshi MJU, Sayeed MA, Uddin MA, Jinan UA, et al. Design and Implementation of an IoT Based Medical Assistant Robot (Aido-Bot). 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). 2020;17-20, doi: 10.1109/WIECON-ECE52138.2020.9397958.

Hurt B, Kligerman S, Hsiao A. Deep Learning Localization of Pneumonia: 2019 Coronavirus (COVID-19) Outbreak. J Thorac Imaging. 2020 May;35(3):W87-W89

Li D, Wang D, Dong J, Wang N, Huang H, Xu H, et al. False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases. Korean J Radiol. 2020 Apr;21(4):505-508

Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology. 2020 Aug 19;296(2):E65-E71

Yang Z, Zeng Z, Wang K, Wong S, Liang W, Zanin M, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thorac Dis. 2020 Mar;12(3):165-174

Al-Najjar HN, Al-Rousan N. A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea. Eur Rev Med Pharmacol Sci. 2020 Mar;24(6):3400-3403

Jiang X, Coffee M, Bari A. Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity. 2020 Mar 30;63(1):537-551.

Beck BR, Shin B, Choi Y, Park S, Kang K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J. 2020;18:784-790.

Kadioglu O, Saeed M, Greten HJ, Efferth T. Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning. Bull World Health Organ. 2020 Mar 21:1-29.

Richardson P, Griffin I, Tucker C, Smith D, Oechsle O, Phelan A, et al. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. The Lancet. 2020 Feb;395(10223):e30-e31.

Ton A, Gentile F, Hsing M, Ban F, Cherkasov A. Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds. Mol Inform. 2020 Aug 11;39(8):e2000028.

Zhang D, Wu K, Zhang X, Deng S, Peng B. In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus. J Integr Med. 2020 Mar;18(2):152-158.

Houthooft R, Ruyssinck J, van der Herten J, Stijven S, Couckuyt I, Gadeyne B, et al. Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. Artif Intell Med. 2015 Mar;63(3):191-207. doi: 10.1016/j.artmed.2014.12.009.

Sotoodeh M, Ho JC. Improving length of stay prediction using a hidden Markov model. AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:425-434.

Lin YW, Zhou Y, Faghri F, Shaw MJ, Campbell RH. Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with lon short term memory. PLoS One. 2019;14:e0218942. doi: 10.1371/journal.pone.0218942.

Awad A, Bader-El-Den M, McNicholas J, Briggs J. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. Int J Med Inform. 2017;108:185–195. doi: 10.1016/j.ijmedinf.2017.10.002.

Holmgren G, Andersson P, Jakobsson A, Frigyesi A. Artificial neural networks improve and simplify intensive care mortality prognostication: a national cohort study of 217,289 first-time intensive care unit admissions. J Intensive Care. 2019;7:44. doi: 10.1186/s40560-019-0393-1.

Yoon JH, Mu L, Chen L, Dubrawski A, Hravnak M, Pinsky MR, et al. Predicting tachycardia as a surrogate for instability in the intensive care unit. J Clin Monit Comput. 2019 Dec;33(6):973-985. doi: 10.1007/s10877-019-00277-0.

Blanch L, Sales B, Montanya J, Lucangelo U, Garcia-Esquirol O, Villagra A, et al. Validation of the Better Care® system to detect ineffective efforts during expiration in mechanically ventilated patients: a pilot study. Intensive Care Med. 2012 May;38(5):772-80. doi: 10.1007/s00134-012-2493-4.

Rehm GB, Han J, Kuhn BT, Delplanque JP, Anderson NR, Adams JY, et al. Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony. Methods Inf Med. 2018 Sep;57(4):208-219. doi: 10.3414/ME17-02-0012.

Adams JY, Lieng MK, Kuhn BT, Rehm GB, Guo EC, Taylor SL, Delplanque JP, Anderson NR. Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation. Sci Rep. 2017 Nov 3;7(1):14980. doi: 10.1038/s41598-017-15052-x.

Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716-1720. doi: 10.1038/s41591-018-0213-5.

Davoudi A, Malhotra KR, Shickel B, Siegel S, Williams S, Ruppert M, et al. Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning. Sci Rep. 2019 May 29;9(1):8020. doi: 10.1038/s41598-019-44004-w.

Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019;380(14):1347-1358. doi: 10.1056/NEJMra1814259.

Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018;378(11):981-983. doi: 10.1056/NEJMp1714229.

Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W. Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics Proteomics.;15(1):41-51. doi: 10.21873/cgp.20063.

Zhang F, Petersen M, Johnson L, Hall J, O'Bryant SE. Recursive Support Vector Machine Biomarker Selection for Alzheimer's Disease. J Alzheimers Dis. 2021;79(4):1691-1700. doi: 10.3233/JAD-201254.

Kumar B, Gupta D. Universum based Lagrangian twin bounded support vector machine to classify EEG signals. Comput Methods Programs Biomed. 2021;208:106244. doi: 10.1016/j.cmpb.2021.106244.

Tacke M, Kochs EF, Mueller M, Kramer S, Jordan D, Schneider G. Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia. PLoS One. 2020;15(8):e0238249. doi: 10.1371/journal.pone.0238249.

Yu S, Tan KK, Sng BL, Li S, Sia AT. Lumbar Ultrasound Image Feature Extraction and Classification with Support Vector Machine. Ultrasound Med Biol. 2015;41(10):2677-89. doi: 10.1016/j.ultrasmedbio.2015.05.015.

Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020;132(2):379-394. doi:10.1097/ALN.0000000000002960

Fakherpour A, Ghaem H, Fattahi Z, Zaree S. Maternal and anaesthesia-related risk factors and incidence of spinal anaesthesia-induced hypotension in elective caesarean section: A multinomial logistic regression. Indian J Anaesth. 2018;62(1):36-46. doi: 10.4103/ija.IJA_416_17.

Padmanabhan R, Meskin N, Haddad WM. Closed-loop control of anesthesia and mean arterial pressure using reinforcement learning. Biomed Signal Process Control 2015; 22: 54–64.

Hu YJ, Ku TH, Jan RH, Wang K, Tseng YC, Yang SF. Decision tree-based learning to predict patient controlled analgesia consumption and readjustment. BMC Med Inform Decis Mak 2012; 12:131.

Cascella M. Mechanisms underlying brain monitoring during anesthesia: limitations, possible improvements, and perspectives. Korean J Anesthesiol. 2016;69(2):113-20. doi: 10.4097/kjae.2016.69.2.113.

Jonsson A. Deep Reinforcement Learning in Medicine. Kidney Dis (Basel). 2019;5(1):18-22. doi: 10.1159/000492670.

Brasoveanu A, Dotlačil J. Reinforcement Learning for Production-Based Cognitive Models. Top Cogn Sci. 2021;13(3):467-487. doi: 10.1111/tops.12546.

Roscow EL, Chua R, Costa RP, Jones MW, Lepora N. Learning offline: memory replay in biological and artificial reinforcement learning. Trends Neurosci. 2021;44(10):808-821. doi: 10.1016/j.tins.2021.07.007.

Chen D, Zhang H, Kavitha PT, Loy FL, Ng SH, Wang C, et al. Scalp EEG-Based Pain Detection Using Convolutional Neural Network. IEEE Trans Neural Syst Rehabil Eng. 2022;30:274-285. doi: 10.1109/TNSRE.2022.3147673.

Hasan N, Bao Y, Shawon A, Huang Y. DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image. SN Comput Sci. 2021;2(5):389. doi: 10.1007/s42979-021-00782-7.

Madanu R, Rahman F, Abbod MF, Fan SZ, Shieh JS. Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition. Math Biosci Eng. 2021;18(5):5047-5068. doi: 10.3934/mbe.2021257.

Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform. 2017 Sep 1;18(5):851-869. doi: 10.1093/bib/bbw068.

Liu X, Liu C, Huang R, Zhu H, Liu Q, Mitra S, et al. Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling. Int J Clin Pharmacol Ther. 2021;59(2):138-146. doi: 10.5414/CP203800.

Python Scikit-Learn Cheat Sheet. accessable on:: https://intellipaat.com/blog/tutorial/python-tutorial/scikit-learn-cheat-sheet/