Collin M Stultz

Title: The Innovative Contributions of Collin M Stultz

Introduction

Collin M Stultz is a prominent inventor based in Newton, MA, known for his significant contributions to the field of machine learning and clinical risk prediction. With a total of 7 patents, Stultz has developed innovative solutions that enhance predictive modeling and clinical assessments.

Latest Patents

Stultz's latest patents include "Data model processing in machine learning employing feature selection using sub-population analysis." This invention involves a computer system that selects features from a dataset for predictive modeling. It identifies a subset of cases and controls with similar values for the first set of features and analyzes this subset to select additional relevant features. The invention evaluates two predictive models to determine which one more accurately predicts outcomes, ultimately utilizing the more effective model for predictions.

Another notable patent is "Assessing unreliability of clinical risk prediction." This invention identifies estimated clinical risk scores for two groups of patients and generates an alternative probability estimate using the same inputs. It determines the unreliability of a patient's clinical risk score based on patient features and the difference between the alternative estimate and the determined score.

Career Highlights

Collin M Stultz has had a distinguished career, working with prestigious organizations such as the Massachusetts Institute of Technology and IBM. His work in these institutions has allowed him to collaborate with leading experts in the field and contribute to groundbreaking research.

Collaborations

Stultz has worked alongside notable colleagues, including John V Guttag and Zeeshan H Syed. These collaborations have further enriched his research and innovation efforts.

Conclusion

Collin M Stultz's contributions to machine learning and clinical risk prediction exemplify the impact of innovative thinking in technology and healthcare. His patents reflect a commitment to advancing predictive modeling and improving clinical assessments, making him a significant figure in his field.