Technology executive skilled at using unique early-stage venture capital models to commercialize internal corporate IP for new markets.
Mike Keymer is an engineer and business leader with broad experience helping small companies grow and thrive. As Manager of the New Ventures practice at Right Start Consulting, Mike has helped many startups and early-stage companies with strategy, IP, sales & marketing, product development, and financing needs. In 2013, Mike co-founded Topspin Labs, a business incubator focused on helping established companies commercialize their technologies for new markets by founding new startups to pursue those opportunities.
Mike currently holds the position of CEO of Origent Data Sciences, a DC-based healthtech startup focused on revolutionizing drug development for neurological disorders like ALS, Huntington's disease, Parkinson's disease, and Alzheimer's disease.
Mike is formerly the Vice President of Operations for Gannon Technologies Group, a high-tech R&D firm serving various federal government clients, and Litigation Systems Inc., which he led through a turnaround to profitability. Mike holds engineering degrees from Washington University in St. Louis and the Massachusetts Institute of Technology, and an MBA from the Kellogg School of Management.
It is essential to develop predictive algorithms for Amyotrophic Lateral Sclerosis (ALS) disease progression to allow for efficient clinical trials and patient care. The best existing predictive models rely on several months of baseline data and have only been validated in clinical trial research datasets. We asked whether a model developed using clinical research patient data could be applied to the broader ALS population typically seen at a tertiary care ALS clinic.
Based on the PRO-ACT ALS database, we developed random forest (RF), pre-slope, and generalized linear (GLM) models to test whether accurate, unbiased models could be created using only baseline data. Secondly, we tested whether a model could be validated with a clinical patient dataset to demonstrate broader applicability.
We found that a random forest model using only baseline data could accurately predict disease progression for a clinical trial research dataset as well as a population of patients being treated at a tertiary care clinic. The RF Model outperformed a pre-slope model and was similar to a GLM model in terms of root mean square deviation at early time points. At later time points, the RF Model was far superior to either model. Finally, we found that only the RF Model was unbiased and was less subject to overfitting than either of the other two models when applied to a clinic population.
We conclude that the RF Model delivers superior predictions of ALS disease progression.