Image Place holder

Yi Luo, PhD

Program: Machine Learning

View Lab Page

Overview

Dr. Luo’s research interests focus on machine learning and systems informatics and their application to health outcomes, decision support, interpretable and credible models at both the individual and community levels for precision medicine, health equity, and healthcare quality. He will be joining the Health Outcomes & Behavior Program to advance applications of AI/ML in areas related to improving cancer outcomes through social determinants and reducing care disparity through implementation of Fair AI.


Associations

    • Machine Learning
    • Health Outcomes & Behavior Program

Education & Training

Graduate:

  • Mississippi State University - MS - Industrial and Systems Engineering
  • University of Arizona - PhD - Systems and Industrial Engineering

Fellowship:

  • University of Michigan, - Environment and Sustainability
  • University of Michigan, - Radiation Oncology
Research

Dr. Luo’s research interests focus on machine learning and systems informatics and their application to health outcomes, decision support, interpretable and credible models at both the individual and community levels for precision medicine, health equity, and healthcare quality. He will be joining the Health Outcomes & Behavior Program to advance applications of AI/ML in areas related to improving cancer outcomes through social determinants and reducing care disparity through implementation of Fair AI.

Publications

  • Turner K, Brownstein NC, Thompson Z, El Naqa I, Luo Y, Jim HSL, Rollison DE, Howard R, Zeng D, Rosenberg SA, Perez B, Saltos A, Oswald LB, Gonzalez BD, Islam JY, Alishahi Tabriz A, Zhang W, Dilling TJ. Longitudinal patient-reported outcomes and survival among early-stage non-small cell lung cancer patients receiving stereotactic body radiotherapy. Radiother Oncol. 2022 Feb.167:116-121. Pubmedid: 34953934. Pmcid: PMC8934278.
  • Cui S, Luo Y, Tseng HH, Ten Haken RK, El Naqa I. Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage. Med Phys. 2019 May.46(5):2497-2511. Pubmedid: 30891794. Pmcid: PMC6510637.
  • Cui S, Luo Y, Hsin Tseng H, Ten Haken RK, El Naqa I. Artificial Neural Network with Composite Architectures for Prediction of Local Control in Radiotherapy. IEEE Trans Radiat Plasma Med Sci. 2019 Mar.3(2):242-249. Pubmedid: 30854501. Pmcid: PMC6404537.
  • Luo Y, McShan D, Ray D, Matuszak M, Jolly S, Lawrence T, Ming Kong F, Ten Haken R, El Naqa I. Development of a Fully Cross-Validated Bayesian Network Approach for Local Control Prediction in Lung Cancer. IEEE Trans Radiat Plasma Med Sci. 2019 Mar.3(2):232-241. Pubmedid: 30854500. Pmcid: PMC6404542.
  • Luo Y, Tseng HH, Cui S, Wei L, Ten Haken RK, El Naqa I. Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR Open. 2019 Jul.1(1):20190021. Pubmedid: 33178948. Pmcid: PMC7592485.
  • Tseng HH, Wei L, Cui S, Luo Y, Ten Haken RK, El Naqa I. Machine Learning and Imaging Informatics in Oncology. Oncology-Basel. 2018 Nov.98(6):344-362. Pubmedid: 30472716. Pmcid: PMC6533165.
  • Zhang J, Heng X, Luo Y, Li L, Zhang H, Che F, Li B. Negative lymph node at station 108 is a strong predictor of overall survival in esophageal cancer. Oncol Lett. 2018 Nov.16(5):6705-6712. Pubmedid: 30405812. Pmcid: PMC6202523.
  • Luo Y, McShan DL, Matuszak MM, Ray D, Lawrence TS, Jolly S, Kong FM, Ten Haken RK, El Naqa I. A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy. Med Phys. 2018 Jun. Pubmedid: 29862533. Pmcid: PMC6279602.
  • Zhang J, Liu Y, Che F, Luo Y, Huang W, Heng X, Li B. Pattern of lymph node metastasis in thoracic esophageal squamous cell carcinoma with poor differentiation. Mol Clin Oncol. 2018 Jun.8(6):760-766. Pubmedid: 29844907. Pmcid: PMC5958789.
  • Tseng HH, Luo Y, Ten Haken RK, El Naqa I. The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy. Front Oncol. 2018 Jul.8:266. Pubmedid: 30101124. Pmcid: PMC6072876.
  • Tseng HH, Luo Y, Cui S, Chien JT, Ten Haken RK, Naqa IE. Deep reinforcement learning for automated radiation adaptation in lung cancer. Med Phys. 2017 Dec.44(12):6690-6705. Pubmedid: 29034482. Pmcid: PMC5734677.
  • El Naqa I, Kerns SL, Coates J, Luo Y, Speers C, West CML, Rosenstein BS, Ten Haken RK. Radiogenomics and radiotherapy response modeling. Phys Med Biol. 2017 Aug.62(16):R179-R206. Pubmedid: 28657906. Pmcid: PMC5557376.
  • Luo Y, El Naqa I, McShan DL, Ray D, Lohse I, Matuszak MM, Owen D, Jolly S, Lawrence TS, Kong FS, Ten Haken RK. Unraveling biophysical interactions of radiation pneumonitis in non-small-cell lung cancer via Bayesian network analysis. Radiother Oncol. 2017 Apr.123(1):85-92. Pubmedid: 28237401. Pmcid: PMC5386796.
  • Zhang J, Heng X, Luo Y, Fu Q, Li Z, Che F, Li B. Influence of negative lymph node in No 7 on survival of patients with middle thoracic esophageal squamous cell carcinoma. Onco Targets Ther. 2016 Mar.9:1831-1837. Pubmedid: 27099516. Pmcid: PMC4821374.