References

American Medical Association. (2021). Trends in health care spending. https://www.ama-assn.org/about/research/trends-health-care-spending
Berger, D. (1999). A brief history of medical diagnosis and the birth of the clinical laboratory: Part 2–laboratory science and professional certification in the 20th century. Medical Laboratory Observer: MLO, 31(8), 32–34, 36, 38. https://www.proquest.com/docview/223382876/citation/57E18BE0383F41C4PQ/1
Boehmke, B., & Greenwell, B. (2020). Hands-on machine learning with r. https://bradleyboehmke.github.io/HOML/
Breiman, L. (2001a). Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
Breiman, L. (2001b). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16(3). https://doi.org/10.1214/ss/1009213726
Cabitza, F., & Banfi, G. (2018). Machine learning in laboratory medicine: waiting for the flood? Clinical Chemistry and Laboratory Medicine (CCLM), 56(4), 516–524. https://doi.org/10.1515/cclm-2017-0287
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. 785794. https://doi.org/10.1145/2939672.2939785
De Bruyne, S., Speeckaert, M. M., Van Biesen, W., & Delanghe, J. R. (2021). Recent evolutions of machine learning applications in clinical laboratory medicine. Critical Reviews in Clinical Laboratory Sciences, 58(2), 131–152. https://doi.org/10.1080/10408363.2020.1828811
Géron, A. (2019). Hands-on machine learning with scikit-learn, keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems (Second edition). O’Reilly Media, Inc.
Greenwell, Brandon,M., & Boehmke, Bradley,C. (2020). Variable Importance PlotsAn Introduction to the vip Package. The R Journal, 12(1), 343. https://doi.org/10.32614/RJ-2020-013
Henricks, W. H. (2015). Laboratory Information Systems. Surgical Pathology Clinics, 8(2), 101–108. https://doi.org/10.1016/j.path.2015.02.016
Hernandez, J. S. (2003). Cost-effectiveness of laboratory testing. Archives of Pathology & Laboratory Medicine, 127(4), 440–445. https://doi.org/10.5858/2003-127-0440-COLT
Johnson, Alistair, Bulgarelli, Lucas, Pollard, Tom, Horng, Steven, Celi, Leo Anthony, & Mark, Roger. (n.d.). MIMIC-IV. https://doi.org/10.13026/S6N6-XD98
Laan, M. J. van der. (2006). Statistical Inference for Variable Importance. The International Journal of Biostatistics, 2(1). https://doi.org/10.2202/1557-4679.1008
Li, L. T., Huang, T., Bernstam, E. V., & Jiang, X. (2022). External Validation of a Laboratory Prediction Algorithm for the Reduction of Unnecessary Labs in the Critical Care Setting. The American Journal of Medicine. https://doi.org/10.1016/j.amjmed.2021.12.020
Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. 2.
Luo, Y., Szolovits, P., Dighe, A. S., & Baron, J. M. (2016). Using Machine Learning to Predict Laboratory Test Results. American Journal of Clinical Pathology, 145(6), 778–788. https://doi.org/10.1093/ajcp/aqw064
Ma, I., Lau, C. K., Ramdas, Z., Jackson, R., & Naugler, C. (2019). Estimated costs of 51 commonly ordered laboratory tests in Canada. Clinical Biochemistry, 65, 58–60. https://doi.org/10.1016/j.clinbiochem.2018.12.013
Murphy, M. J. (2021). Reflex and reflective testing: Progress, but much still to be done. Annals of Clinical Biochemistry, 58(2), 75–77. https://doi.org/10.1177/0004563221993153
New York State Department of Health. (n.d.). Disease screening - statistics teaching tools - new york state department of health. https://www.health.ny.gov/diseases/chronic/discreen.htm
Park, J. Y., & Kricka, L. J. (2017). One hundred years of clinical laboratory automation: 19672067. Clinical Biochemistry, 50(12), 639–644. https://doi.org/10.1016/j.clinbiochem.2017.03.004
Plebani, M., & Giovanella, L. (2020). Reflex TSH strategy: the good, the bad and the ugly. Clinical Chemistry and Laboratory Medicine (CCLM), 58(1), 1–2. https://doi.org/10.1515/cclm-2019-0625
Rabbani, N., Kim, G. Y. E., Suarez, C. J., & Chen, J. H. (2022). Applications of machine learning in routine laboratory medicine: Current state and future directions. Clinical Biochemistry, 103, 1–7. https://doi.org/10.1016/j.clinbiochem.2022.02.011
Schectman, J. M., Kallenberg, G. A., Hirsch, R. P., & Shumacher, R. J. (1991). Report of an association between race and thyroid stimulating hormone level. American Journal of Public Health, 81(4), 505–506. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1405055/
Srivastava, R., Bartlett, W. A., Kennedy, I. M., Hiney, A., Fletcher, C., & Murphy, M. J. (2010). Reflex and reflective testing: efficiency and effectiveness of adding on laboratory tests. Annals of Clinical Biochemistry, 47(3), 223–227. https://doi.org/10.1258/acb.2010.009282
Tyler, P. D., Du, H., Feng, M., Bai, R., Xu, Z., Horowitz, G. L., Stone, D. J., & Celi, L. A. (2018). Assessment of intensive care unit laboratory values that differ from reference ranges and association with patient mortality and length of stay. JAMA Network Open, 1(7), e184521. https://doi.org/10.1001/jamanetworkopen.2018.4521
Verboeket-van de Venne, W. P. H. G., Aakre, K. M., Watine, J., & Oosterhuis, W. P. (2012). Reflective testing: adding value to laboratory testing. Clinical Chemistry and Laboratory Medicine (CCLM), 50(7), 1249–1252. https://doi.org/10.1515/cclm-2011-0611
Woodmansee, W. W. (2018). Determination of optimal TSH ranges for reflex free T4 testing. Clinical Thyroidology for the Public, 11(2), 3–4. https://www.thyroid.org/patient-thyroid-information/ct-for-patients/february-2018/vol-11-issue-2-p-3-4/
Xu, S., Hom, J., Balasubramanian, S., Schroeder, L. F., Najafi, N., Roy, S., & Chen, J. H. (2019). Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests. JAMA Network Open, 2(9), e1910967. https://doi.org/10.1001/jamanetworkopen.2019.10967