In my MSc and PhD theses, I used artificial neural networks to delineate non-linear patterns in clinical data by combining Poisson regression model with neural networks (PRNN) to relax the traditional Poisson regression assumption of linearity. The theoretical aspect of this novel model was published in a methodological journal (Neural Computing and Application, 2009). This model contributed to new understanding of the role of androgens in coronary artery disease. Results revealed, a non-linear association of testosterone levels with coronary artery disease for first time. (European Journal of Epidemiology, 2009). Furthermore, the performance and utility of the model was illustrated for predicting five-year changes in cognitive scores (in Alzheimer Disease), using the Canadian Study of Health and Aging data. (Journal of Applied Statistics, 2011)
More recently in clinical application, we applied machine learning and Artificial Intelligence methods in spinal cord injury to accommodate the non-linear nature of data.
A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry. (The Spine Journal, 2017)
Impact of Therapy on Recovery during Rehabilitation in Patients with Traumatic Spinal Cord Injury. (Journal of Neurotrauma, 2017)
Predicting Injury Severity and Neurological Recovery after Acute Cervical Spinal Cord Injury: A Comparison of Cerebrospinal Fluid and Magnetic Resonance Imaging Biomarkers. (Journal of Neurotrauma, 2018)
Unbiased Recursive Partitioning to Stratify Patients with Acute Traumatic Spinal Cord Injuries: External Validity in an Observational Cohort Study. (Journal of Neurotrauma, 2019)
Highlighting discrepancies in walking prediction accuracy for patients with traumatic spinal cord injury: an evaluation of validated prediction models using a Canadian Multicenter Spinal Cord Injury Registry. (The Spine Journal, 2019)
Decision tree analysis to better control treatment effects in spinal cord injury clinical research. (The Journal of Neurosurgery, 2019)
Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients ( The Spine Journal, 2020)
Development of a Machine Learning Algorithm for Predicting In-hospital and One-year Mortality after Traumatic Spinal Cord Injury:Mortality prediction tool for Spinal Cord Injury (The Spine Journal, 2021)
In my postdoctoral training I developed and applied another machine learning method (multi-state transition model) for cognitive functions assessment and healthy aging. The model was based on the Markov process with interpretable parameters which could be adjusted for covariates. The model can compute probabilities of improvements, stabilization, worsening and dying as a function of baseline and the covariates estimate according to modified Poisson model between two assessments. This novel multi-state transition model has brought me several opportunities for collaboration and publication, so far model were used and published in the top clinical and methodological journals such as Annals of Epidemiology 2010, 2011, 2013; and Statistical Methods in Medical Research, 2014.
See below link for more technical explanations:
British Medical Journal (BMJ), Scientific Reports (Nature), Spinal Cord (Nature), BMC Geriatric, Neuroepidemiology, Clinical Endocrinology, Journal of Public Health, Journal of Applied Statistics, Neural Computing and Applications, Current Gerontology and Geriatric Research, Iranian Journal of Pathology