# Conclusion

This concludes our step-by-step overview of MILO-ML.

Hopefully, the above documentation has been helpful in your model building journey within MILO-ML. For further questions please visit our website https://milo-ml.com (opens new window).



# References

  1. Hooman H Rashidi, MD, Nam K. Tran, PhD, Elham Vali Betts, MD, Lydia P. Howell, MD, Ralph Green, MD, PhD. Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Academic Pathology. Academic Pathology. Sept 2019.

  2. Nam K. Tran, PhD, Samer Albahra, MD, Tam N. Pham, MD, James Holmes IV, MD, David Greenhalgh, MD, Tina L. Palmieri, MD, Jeffrey Wajda, and Hooman H. Rashidi, MD. Novel Application of An Automated-Machine Learning Development Tool For Predicting Burn Sepsis. Nature’s Scientific Reports. July 2020

  3. Hooman H. Rashidi, MD, FASCP1*; Soman Sen, MD, FACS2; Tina L. Palmieri, MD, FACS, FCCM2; Thomas Blackmon, BS1; Jeffrey Wajda, DO3; and Nam K. Tran, PhD, HCLD (ABB), FACB1*. Early Recognition of Acute Kidney Injury In Trauma Surgery And Severely Burned Patients By Artificial Intelligence: Generalization Of Machine Learning Techniques. Nature’s Scientific Reports. Jan 2020

  4. Kuang-Yu Jen, Samer Albahra, Felicia Yen, Junichiro Sageshima, Ling-Xin Chen, Hooman H. Rashidi. Automated En Masse Machine Learning Model Generation and Optimization for Predicting Delayed Graft Function in Renal Allografts. Transplantation (Accepted. Dec. 2020)

  5. Hooman H. Rashidi, MD, FASCP1*, Amy Makley, MD2; Tina L. Palmieri, MD, FACS, FCCM3; Samer Albahra, MD1; Julia Loegering, BS1; Lei Fang, PhD4; Kensuke Yamaguchi, PhD4; Travis Gerlach, MD5; Dario Rodriquez Jr, MSc, RRT, FAARC6; and Nam K. Tran, PhD, HCLD (ABB), FAACC. Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction through an Automated Machine Learning Platform and Point-of-Care Testing. (Archives of Pathology & Laboratory Medicine, 2021, In press)