Ananya A Poddar

20230047800, Feb 16, 2023

Aug 13, 2021

17/402123

- Armonk NY, US
Ching-Huei Tsou - Briarcliff Manor NY, US
Ananya Aniruddha Poddar - White Plains NY, US
Diwakar Mahajan - New York NY, US
Bharath Dandala - White Plains NY, US
Divya Ranganathan Pathak - Westchester NY, US
Piyush Madan - Boston MA, US
Michal Rosen-Zvi - Jerusalem, IL
Aisha Walcott - Nairobi, KE

G16H 50/70
G16H 50/20
G06F 40/30
G06F 16/951

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.

20210240934, Aug 5, 2021

Jan 30, 2020

16/777426

- Armonk NY, US
Ananya Aniruddha Poddar - White Plains NY, US
Bharath Dandala - White Plains NY, US
Ching-Huei Tsou - Briarcliff Manor NY, US

G06F 40/295
G06F 40/211
G06N 5/04
G06F 40/284
G06N 20/00

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.

20210057068, Feb 25, 2021

Aug 22, 2019

16/548443

- Armonk NY, US
Ananya Aniruddha Poddar - White Plains NY, US
Murthy V. Devarakonda - Peekskill NY, US

G16H 15/00
G06N 20/10
G06F 17/27
G06N 3/08
G16H 10/60

A clinical information extraction and training mechanism is provided for automatically extracting and identifying information in plain text narratives in a set of electronic medical records. The mechanism segments each clinical note in a plurality of clinical notes into one or more identified sections, labels each identified section with an associated tag, and generate a tag data structure utilizing explicitly tagged sequences of sentences and associated tags. The mechanism performs statistical analysis of the identified sections that contain tags identified in the tag data structure to identify one or more valid stop/start conditions; extracts a first set of positive examples of sentences for a selected type of information, and then trains a cognitive system to identify sentences in the plurality of clinical notes that fail to have a tag associated with the selected type using the positive examples of sentences for different types of information.

20190295695, Sep 26, 2019

Mar 23, 2018

15/934335

- Armonk NY, US
Murthy V. Devarakonda - Peekskill NY, US
Ananya Aniruddha Poddar - White Plains NY, US
Paul C. Tang - Los Altos CA, US

G16H 10/60
G16H 80/00
G16H 10/20

Mechanisms are provided for presentation of pertinent information for a medical treatment plan. The mechanisms receive a medical condition for an interaction with a patient, and a medical mental model that emulates thinking of a medical professional with regard to reviewing a patient electronic medical record (EMR) to identify pertinent information for a medical treatment plan to treat the medical condition based on categorizations of elements in the patient EMR. The mechanisms analyze the EMR for the patient to identify a portion of the EMR relevant to the medical treatment plan using the categorizations, and analyze the identified portion of the EMR to extract relevant patient information that is directed to the medical treatment plan for the medical condition based on the categorizations. The mechanisms generate and output a cognitive summary correlating the extracted relevant patient information and the medical treatment plan in association with the medical condition.