Paul D Myers

Title: The Innovations of Paul D. Myers

Introduction

Paul D. Myers is a notable inventor based in Bloomfield Hills, MI (US). He has made significant contributions to the field of machine learning and clinical risk prediction. With a total of 3 patents, his work has had a considerable impact on predictive modeling and healthcare analytics.

Latest Patents

One of his latest patents is titled "Data model processing in machine learning employing feature selection using sub-population analysis." This invention involves a computer system that selects features of 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 significant patent is "Assessing unreliability of clinical risk prediction." This invention identifies estimated clinical risk scores for different patient groups and generates alternative probability estimates. It assesses the unreliability of a patient's clinical risk score based on specific patient features and the differences between the alternative estimates and the determined scores.

Career Highlights

Throughout his career, Paul D. Myers has worked with prestigious organizations such as IBM and the Massachusetts Institute of Technology. His experience in these institutions has allowed him to develop innovative solutions that address complex problems in data analysis and healthcare.

Collaborations

Paul has collaborated with notable individuals in his field, including Uri Kartoun and Kristen Ann Severson. These partnerships have contributed to the advancement of his research and inventions.

Conclusion

Paul D. Myers is a distinguished inventor whose work in machine learning and clinical risk prediction has led to valuable innovations. His patents reflect a commitment to improving predictive modeling and healthcare outcomes.