Professor Richard Woodman

My current interests include the use of AI and Machine Learning for medicine and healthcare using Python. My epidemiological research focuses include nutrition, hospital mortality and pharmacoepidemiology. I have expertise in the design and analysis of randomised controlled trials and observational studies and a broad range of biostatistical techniques. I have published more than 300 papers in high-ranking journals, including 150 peer reviewed articles in the past 5 years. These articles have over 11,000 citations. I have contributed to externally funded grants worth a total of AUS$21.0M including AUD$12.8M since 2018. In addition, I have provided published expert commentary on numerous topics including Bland-Altman analysis, 2-sided confidence intervals, propensity score matching, least-squares regression and pitfalls of allometric scaling. My international standing is highlighted by a consistent record of publication in leading medical journals with high impact factors including JAMA (45.5), Annals of Internal Med (21.3), Thorax (10.3), Chest (9.7), Clin Gastro Hepatology (8.5), Am J Clin Nutr (6.8), Stoke (6.0), Br J Cancer (5.7), and Sleep (5.1). Within Australia, I am a recognised expert in the reporting of indicators of patient safety including Hospital Standardised Mortality Ratios (HSMR’s) and am a member of the ACSQHC Patient Safety Reporting Committee tasked with defining KPI’s for patient safety. I am a past president of the Statistical Society of Australia (SSA) in South Australia and was a Council member for 5 years. In collaboration with the SSA I have organised methodology workshops with internationally renowned statisticians in Causal Inference, Spatial epidemiology, and Data Linkage. I have successfully supervised 11 PhD students, 2 Masters, 3 Honours and 5 MD students to completion, have 3 current PhD students and participate as a mentor in the Flinders University mentoring program and have created and taught Advanced MPH topics in Biostatistics, Epidemiology and Machine Learning.