International Business Machines Corporation

Diwakar Mahajan

Bharath Dandala

Ching-Huei Tsou

Ananya Aniruddha Poddar

Aisha Walcott

Piyush Madan

Divya Pathak

Michal Rosen-Zvi

Jennifer J Liang

Parthasarathy Suryanarayanan

Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.

Artificial intelligence-assisted non-pharmaceutical intervention data curation

A method improves performance of natural language processing by automatically generating ground truth from electronic health records comprising unstructured clinical notes and structured data comprising entries each having respective values for fields. The method includes: linking a given one of the notes to a given one of the entries responsive to determining that a specified field within the given entry matches an item of metadata for the given note; determining an initial set of the notes which satisfy criteria selected such that the criteria are a proxy for the ground truth, wherein the given note is determined to satisfy the criteria based at least in part on the given entry linked thereto; and designating at least a portion of the initial set of notes which satisfy the criteria, and the entries linked to the portion of the initial set of notes which satisfy the criteria, as the ground truth.

Auto-generating ground truth on clinical text by leveraging structured electronic health record data

Text data including at least named entities can be received. From the named entities, continuous entities, overlapping entities and disjoint entities can be identified. The overlapping entities can be transformed into continuous entities. The continuous entities, the transformed entities and the disjoint entities can be encoded. The encoded entities can be input to a machine learning language model to train the machine learning model to predict candidate entities. The predicted entities can be decoded to reconstruct the predicted entities.

Lightweight tagging for disjoint entities

Growing community of inventors

New York, NY, United States of America

Diwakar Mahajan