Computer Physician Order Entry Benefits, Costs, and Issues | Annals of Internal Medicine. Accessed: Nov. 06, 2023. [Online]. Available: https://www.acpjournals.org/doi/full/https://doi.org/10.7326/0003-4819-139-1-200307010-00010

Lewis PJ, Dornan T, Taylor D, Tully MP, Wass V, Ashcroft DM (2009) Prevalence, Incidence and Nature of Prescribing Errors in Hospital Inpatients, Drug Saf, vol. 32, no. 5, Art. no. 5, May https://doi.org/10.2165/00002018-200932050-00002

Bates DW, Leape LL, Petrycki S (1993) Incidence and preventability of adverse drug events in hospitalized adults, J. Gen. Intern. Med, vol. 8, no. 6, pp. 289–294, Jun. https://doi.org/10.1007/BF02600138

Abraham J et al (Apr. 2018) Reasons for computerised provider order entry (CPOE)-based inpatient medication ordering errors: an observational study of voided orders. BMJ Qual Saf 27(4):299–307. https://doi.org/10.1136/bmjqs-2017-006606

Kannampallil TG et al (2017) Jul., Learning from errors: analysis of medication order voiding in CPOE systems, J. Am. Med. Inform. Assoc, vol. 24, no. 4, pp. 762–768, https://doi.org/10.1093/jamia/ocw187

Applied Sciences | Free Full-Text | Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit A Systematic Review. Accessed: Nov. 06, 2023. [Online]. Available: https://www.mdpi.com/2076-3417/11/6/2880

Ancker JS et al (Apr. 2017) Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inf Decis Mak 17(1):36. https://doi.org/10.1186/s12911-017-0430-8

Nuckols TK et al (Jun. 2014) The effectiveness of computerized order entry at reducing preventable adverse drug events and medication errors in hospital settings: a systematic review and meta-analysis. Syst Rev 3(1):56. https://doi.org/10.1186/2046-4053-3-56

van der Sijs H, Aarts J, Vulto A, Berg M (2006) Overriding of Drug Safety Alerts in Computerized Physician Order Entry, J. Am. Med. Inform. Assoc, vol. 13, no. 2, pp. 138–147, Mar. https://doi.org/10.1197/jamia.M1809

Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting | Journal of the American Medical Informatics Association | Oxford Academic. Accessed: Nov. 06, 2023. [Online]. Available: https://academic.oup.com/jamia/article/26/12/1560/5544737

Predicting self-intercepted medication ordering errors using machine learning | PLOS ONE Accessed: Nov. 06, 2023. [Online]. Available: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254358

Krishnan DG (Nov. 2022) Artificial Intelligence in oral and maxillofacial surgery education. Oral Maxillofac Surg Clin 34(4):585–591. https://doi.org/10.1016/j.coms.2022.03.006

Bouletreau P, Makaremi M, Ibrahim B, Louvrier A, Sigaux N (2019) Artificial Intelligence: Applications in orthognathic surgery, J. Stomatol. Oral Maxillofac. Surg, vol. 120, no. 4, pp. 347–354, Sep. https://doi.org/10.1016/j.jormas.2019.06.001

Artificial intelligence for oral and maxillo-facial surgery A narrative review - ScienceDirect. Accessed: Nov. 06, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2468785522000271

Watson J et al (2020) Jul., Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers? JAMIA Open, vol. 3, no. 2, pp. 167–172, https://doi.org/10.1093/jamiaopen/ooz046

Shirgave S, Awati C, More R, Patil S (2019) A Review On Credit Card Fraud Detection Using Machine Learning, Int. J. Sci. Technol. Res, vol. 8, pp. 1217–1220, Oct

Wuest T, Weimer D, Irgens C, Thoben K-D (Jan. 2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4(1):23–45. https://doi.org/10.1080/21693277.2016.1192517

Handa A, Sharma A, Shukla SK (2019) Machine learning in cybersecurity: a review. WIREs Data Min Knowl Discov 9(4):e1306. https://doi.org/10.1002/widm.1306

Article  Google Scholar 

Nanduri J, Jia Y, Oka A, Beaver J, Liu Y-W (2020) Microsoft Uses Machine Learning and Optimization to Reduce E-Commerce Fraud, Inf. J. Appl. Anal, vol. 50, no. 1, pp. 64–79, Jan. https://doi.org/10.1287/inte.2019.1017

Machine Learning for Anomaly Detection A Systematic Review | IEEE Journals & Magazine | IEEE Xplore. Accessed: Jan. 03, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9439459

Briceland L, Stein DS Factors Related to Errors in Medication Prescribing

Budach L et al The Effects of Data Quality on Machine Learning Performance, arXiv.org. Accessed: Jan. 03, 2024. [Online]. Available: https://arxiv.org/abs/2207.14529v4

Ahsan M, Gomes R, Chowdhury MM, Nygard KE (2021) Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector, J. Cybersecurity Priv, vol. 1, no. 1, Art. no. 1, Mar. https://doi.org/10.3390/jcp1010011

Khandani AE, Kim AJ, Lo AW (2010) Consumer credit-risk models via machine-learning algorithms, J. Bank. Finance, vol. 34, no. 11, pp. 2767–2787, Nov. https://doi.org/10.1016/j.jbankfin.2010.06.001

Alshaibi A, Al-Ani M, Al-Azzawi A, Konev A, Shelupanov A (2022) The Comparison of Cybersecurity Datasets, Data, vol. 7, no. 2, Art. no. 2, Feb. https://doi.org/10.3390/data7020022

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: Synthetic Minority Over-sampling Technique, J. Artif. Intell. Res, vol. 16, pp. 321–357, Jun. https://doi.org/10.1613/jair.953

Thennakoon A, Bhagyani C, Premadasa S, Mihiranga S, Kuruwitaarachchi N (2019) Real-time Credit Card Fraud Detect Using Mach Learn. https://doi.org/10.1109/CONFLUENCE.2019.8776942

Article  Google Scholar 

Varmedja D, Karanovic M, Sladojevic S, Arsenovic M, Anderla A (2019) Credit Card Fraud Detection - Machine Learning methods, in 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), East Sarajevo, Bosnia and Herzegovina: IEEE, Mar. pp. 1–5. https://doi.org/10.1109/INFOTEH.2019.8717766