Chandramouli Maduri

Title: Innovations by Chandramouli Maduri

Introduction

Chandramouli Maduri is an accomplished inventor based in Elmsford, NY (US). He has made significant contributions to the field of technology, holding a total of three patents. His work focuses on cognitive bias detection and personalized medication adherence, showcasing his commitment to improving healthcare and data accuracy.

Latest Patents

One of his latest patents is titled "Cognitive bias detection and correction in self-reported data." This invention provides embodiments for detecting and correcting cognitive biases in self-reported data. The system includes a processor that executes computer-executable components stored in memory, which create an ontology of bias descriptor features to identify cognitive biases. These biases can stem from various sources, including device-induced, testing-application-induced, or study-design-induced factors.

Another notable patent is "Personalized medication non-adherence evaluation." This invention encompasses a method, a computer program product, and a computer system that predict a patient's medication adherence. The method identifies risk factors associated with medication adherence and determines likely behavior based on these factors and a temporal causal model. This model is based on features of a patient cluster, allowing for a more accurate prediction of medication adherence values.

Career Highlights

Chandramouli Maduri is currently employed at International Business Machines Corporation (IBM), where he continues to innovate and develop new technologies. His work at IBM has allowed him to collaborate with other talented professionals in the field.

Collaborations

Some of his notable coworkers include Ching-Hua Chen and Pei-Yun S Hsueh. Their collaborative efforts contribute to the advancement of technology and the development of innovative solutions.

Conclusion

Chandramouli Maduri's contributions to the field of technology through his patents demonstrate his expertise and dedication to innovation. His work in cognitive bias detection and medication adherence evaluation highlights the importance of accuracy in data and healthcare.