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Parag Aggarwal

ORCID: 0009-0009-0904-0205
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About
Contact & Profiles
A5103025125
Research Areas
  • Radiomics and Machine Learning in Medical Imaging
  • Lung Cancer Diagnosis and Treatment
  • Lung Cancer Treatments and Mutations

Sema4 (United States)
 2022-2023

 Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning–Based Natural Language Processing
OPENALEX - Publications 
Kyeryoung Lee Zongzhi Liu Urmila Chandran Iftekhar Kalsekar Balaji Laxmanan and 14 more Mitchell K. Higashi Tomi Jun Meng Ma Minghao Li Yun Mai Christopher Gilman Tongyu Wang Lei Ai Parag Aggarwal Qi Pan William Oh Gustavo Stolovitzky Eric E. Schadt Xiaoyan Wang

Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development cancer. Electronic health records are rich sources information GGO nodules and granular features, but most valuable is embedded unstructured clinical notes.

10.2196/44537 article EN cc-by JMIR AI 2023-03-31
 Automated Extraction and Longitudinal Analysis of Ground Glass OpacityFeatures in Lung Cancer Patients Powered by Natural Language Processing
OPENALEX - Publications 
K. Lee Z. Liu Meng Ma Urmila Chandran Iftekhar Kalsekar and 12 more Balaji Laxmanan Mengshi Li Yun Mai Christopher Gilman Tiankuo Wang Mingguang Zhang Lei Ai Parag Aggarwal Qi Pan William Oh Eric E. Schadt X. Wang

10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a5077 article EN 2022-05-01
 Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning–Based Natural Language Processing (Preprint)
OPENALEX - Publications 
Kyeryoung Lee Zongzhi Liu Urmila Chandran Iftekhar Kalsekar Balaji Laxmanan and 14 more Mitchell K. Higashi Tomi Jun Meng Ma Minghao Li Yun Mai Christopher Gilman Tongyu Wang Lei Ai Parag Aggarwal Qi Pan William Oh Gustavo Stolovitzky Eric E. Schadt Xiaoyan Wang

<sec> <title>BACKGROUND</title> Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development cancer. Electronic health records are rich sources information GGO nodules and granular features, but most valuable is embedded unstructured clinical notes. </sec> <title>OBJECTIVE</title> We aimed to develop, test, validate a deep learning–based natural language...

10.2196/preprints.44537 preprint EN 2022-11-23
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