International Business Machines Corporation

David Osofsky

Uri Kartoun

Tolga Oral

Kenney Ng

Sara Beth Weber

Michael James Cordes

Paul C Tang

Yoonyoung Park

Charalambos Stavropoulos

Sophie Batchelder

Francis Campion

Amy Chiu

Tanya Rudakevych

Veronica Aldous

Di Wang

A system and associated method for assessing level of completeness of healthcare data storage based on collected data. The system includes a collection system, a measurement system, an expectation system, and an alerting system. In a method of assessing the health of stored data, the system collects real-time data from at least one data source, determines measurements for a plurality of parameters based on the collected data, and generates expectations for a future period of time for the plurality of parameters based on data analysis technique. The system also compares the expectations for the future period of time to subsequent measurements collected for that period of time to determine whether the subsequent measurements satisfy an expectation threshold and provide an alert to a client terminal. The alert is a result of the comparison of the expectations and the subsequent measurements and provides an assessment of data storage quality and alerts on anticipated deficiencies.

Systems and methods for determining data storage health and alerting to breakdowns in data collection

A method for training a machine learning model for open domain question answering includes receiving trained classifiers for question answering. The received trained classifiers are used to generate a set of candidate answers to a question. Second trained classifiers are used for scoring each of the candidate answers. The scoring indicates a measure of how well each candidate answer answers the question. Using the second trained classifiers for scoring each of the candidate answers includes comparing each candidate answer to a first ground truth corresponding to the question. A set of top-scoring candidate answers is presented to a human operator who marks each as correct or incorrect. The correct candidate answers are treated as additional ground truths for further training the first trained classifiers.

Training machine learning models for open-domain question answering system

Growing community of inventors

Arlington, MA, United States of America

David Osofsky