Publications
Showing 13 results for Author: Mayanka Chandra Shekar
May, 2026
Journal
Evolving language of pediatric anxiety in electronic health records
Objectives: This study aimed to identify and quantify semantic drift (ie, the change in semantic meaning over time) within expert-defined anxiety-related (AR) terminology and compare it to common electronic health record (EHR) vocabulary across longitudinal pediatric clinical notes. Materials and Methods: A corpus of pediatric clinical notes from 2009 to 2022 was analyzed…
Mar, 2026
Conference Paper
Characterizing Quantum Classifier Utility in Natural Language Processing Workflows
Quantum Natural Language Processing (QNLP) develops natural language processing (NLP) models for deployment on quantum computers. We explore feature and data prototype selection techniques to address challenges posed by encoding high dimensional features. Our study builds quantum circuit classifiers that includes classical feature pre-processing, quantum embedding and quan…
Feb, 2026
Journal
Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and Data Harmonization
Background: The ability to comprehensively collect treatment information from cancer patient medical records would enable studies to evaluate real-world benefits and risks tied to specific treatments. Currently, it is difficult to systematically collect high- quality treatment information because it is often stored in unstructured text. Manually extracting and standardizin…
Aug, 2025
Journal
Comparison of Expert Vocabulary Usage Patterns Between Mental Health and Nonmental Health Clinicians When Diagnosing Pediatric Anxiety Disorders
Objective: To compare the utilization patterns of expert vocabulary (EVo) in diagnosing pediatric anxiety between mental health and non-mental health clinical notes from electronic health records to understand the role of Evo in informing classification and decision-making in anxiety diagnoses. Study design: We conducted a retrospective study using a cohort less than age 2…
Jun, 2025
Journal
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. We used a DenseNet121 model pre-trained MIMIC-CXR dataset for deep learning-based multi-label classification using ground truth labels from radiology reports ex…
May, 2025
Journal
Evaluating algorithmic bias on biomarker classification of breast cancer pathology reports
Background: This work evaluated algorithmic bias on biomarkers using electronic pathology reports of female breast cancer across five subgroups: National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) registry, racial categories, Hispanic ethnicity, age at diagnosis, and socioeconomic status. Methods: The Framework for Exploring Scalable Computational…
Nov, 2023
Conference Paper
Language Models for the Prediction of SARS-CoV-2 Inhibitors
The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 pe…
Nov, 2023
Journal
In with the old, in with the new: machine learning for time to event biomedical research
The predictive modeling literature for biomedical applications is dominated by biostatistical methods for survival analysis, and more recently some out of the box machine learning approaches. In this article, we show a presentation of a machine learning method appropriate for time-to-event modeling in the area of prostate cancer long-term disease progression. Using XGBoost…
Nov, 2023
Journal
Automating Genetic Algorithm Mutations for Molecules Using a Masked Language Model
Inspired by the evolution of biological systems, genetic algorithms have been applied to generate solutions for optimization problems in a variety of scientific and engineering disciplines. For a given problem, a suitable genome representation must be defined along with a mutation operator to generate subsequent generations. Unlike natural systems which display a variety o…