Christophe Lambert christophe.lambert

I received my PhD in Computer Science from Duke University in 1997. In August 2014, following a faculty appointment at Montana State University Department of Computer Science, and nearly 15 years as CEO of a bioinformatics software company, Golden Helix, I became a faculty member in the University of New Mexico Center for Global Health, Division of Translational Informatics, and Department of Internal Medicine. My research areas include clinical research informatics, bioinformatics, and systems thinking. I develop and apply methods for the analysis of longitudinal healthcare data for predictive and preventative medicine. Since its inception, I have collaborated with other members of the Observational Health Data Sciences and Informatics collaborative. The OHDSI/OMOP common data model has been adopted to represent over 500M patients' electronic health and/or administrative claims records worldwide, enabling the development of a broad set of tools for the analysis of human health on these massive datasets. I am currently developing statistical and computational tools to compare treatment options and obtain better estimates of expected health outcomes despite large biases and confounding in the data, with a focus on mental illness (bipolar disorder, major depression, PTSD, suicidality), with pilot projects in human aging. In July 2016 I received an NIH NLM R21 award to research methods for observational comparative effectiveness research, and a PCORI award to compare bipolar disorder treatments and outcomes in large-scale administrative claims data. We are currently using a database of over 1 million bipolar disorder patients to answer questions about the safety and effectiveness of bipolar disorder therapies both short-term and over many years of treatment. In 2020, I received an R56 award from the NIH NIMH to investigate undiagnosed and/or unrecorded PTSD, TBI, and self-harm through machine learning to determine the degree to which this phenomenon exists, and to examine disparities in diagnosis/recording/outcomes by patient sociodemographic factors. In addition, I perform bioinformatic analyses of genomics datasets with current projects in pediatric Malaria and COVID-19 in collaboration with Dr. DJ Perkins in the Center for Global Health. I serve as the UNM Clinical and Translational Sciences Center (CTSC) Informatics Core Lead. I hold a secondary appointment in the UNM Department of Computer Science. I have developed machine learning software with two patents in the field and have applied it to numerous domains including genetics, winning top 3 finishes in two KDDCup prediction competitions. I participated in the FDA MAQC II effort to develop best practices in classifier development, being one of the co-authors on the final paper: MicroArray Quality Control (MAQC) Consortium. (2010) ?The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.? Nature Biotechnology, 28(8):827-38.

Albuquerque, NM, USA

Professor

Academia

University of New Mexico