The other Dr. Sidey-Gibbons and I have written an introduction to machine learning specifically for medics and medical researchers.
The text is available below and here. The work provides and conceptual and practice framework for conducting a relatively simple machine learning study.
Dr. Conrad Harrison (University of Oxford) and I have written an updated version of Machine Learning in Medicine with a specific focus on natural language processing. You can find the manuscript here.
References from my talk at the National Cancer Institute "Machine Learning and Health Outcomes in Cancer Care Delivery Research" are below. Further references to our machine learning work can be found on my Google Scholar or by contacting me.
Gibbons, C., Porter, I., Gonçalves-Bradley, D.C., Stoilov, S., Ricci-Cabello, I., Tsangaris, E., Gangannagaripalli, J., Davey, A., Gibbons, E.J., Kotzeva, A. and Evans, J., 2021. Routine provision of feedback from patient‐reported outcome measurements to healthcare providers and patients in clinical practice. Cochrane Database of Systematic Reviews, (10).
Harrison, C, Sidey-Gibbons CJ, ... Rodrigues JN. "Recursive Partitioning vs Computerized Adaptive Testing to Reduce the Burden of Health Assessments in Cleft Lip and/or Palate: Comparative Simulation Study." Journal of medical Internet research 23, no. 7 (2021): e26412.
Geerards, D., Pusic, A., Hoogbergen, M., Van Der Hulst, R. and Sidey-Gibbons, C., 2019. Computerized quality of life assessment: a randomized experiment to determine the impact of individualized feedback on assessment experience. Journal of medical Internet research, 21(7), p.e12212.
Gibbons, C., Richards, S., Valderas, J.M. and Campbell, J., 2017. Supervised machine learning algorithms can classify open-text feedback of doctor performance with human-level accuracy. Journal of medical Internet research, 19(3), p.e6533.
Kosinski, M., Stillwell, D. and Graepel, T., 2013. Private traits and attributes are predictable from digital records of human behavior. Proceedings of the national academy of sciences, 110(15), pp.5802-5805.
Pfob, A., Mehrara, B.J., Nelson, J.A., Wilkins, E.G., Pusic, A.L. and Sidey-Gibbons, C., 2022. Towards patient-centered decision-making in breast cancer surgery: machine learning to predict individual patient-reported outcomes at 1-year follow-up. Annals of Surgery.
Pfob, A., Mehrara, B.J., Nelson, J.A., Wilkins, E.G., Pusic, A.L. and Sidey-Gibbons, C., 2021. Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001). The Breast, 60, pp.111-122.
Lu, S.C., Xu, C., Nguyen, C.H., Geng, Y., Pfob, A. and Sidey-Gibbons, C., 2022. Machine Learning–Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal. JMIR medical informatics, 10(3), p.e33182.
Example of the INSPiRED supervised machine learning pipeline