Malvika Pillai

According to our database, Malvika Pillai authored at least 13 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Using an explainable machine learning approach to prioritize factors contributing to healthcare professionals' burnout.

Malvika Pillai, Chao Chin Liu, Elizabeth Kwong, Ian Kratzke, Nadia Charguia, Lukasz Mazur, Karthik Adapa

J. Intell. Inf. Syst., August, 2024

Measuring quality-of-care in treatment of young children with attention-deficit/hyperactivity disorder using pre-trained language models.

Malvika Pillai, José D. Posada, Rebecca M. Gardner, Tina Hernandez-Boussard, Yair Bannett

J. Am. Medical Informatics Assoc., 2024

Unveiling and Mitigating Bias in Mental Health Analysis with Large Language Models.

Yuqing Wang, Yun Zhao, Sara Alessandra Keller, Anne A. H. de Hond, Marieke M. van Buchem, Malvika Pillai, Tina Hernandez-Boussard

CoRR, 2024

FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records.

Yuqing Wang, Malvika Pillai, Yun Zhao, Catherine M. Curtin, Tina Hernandez-Boussard

CoRR, 2024

2022

Recommendations for design of a mobile application to support management of anxiety and depression among Black American women.

Terika McCall, Megan Threats, Malvika Pillai, Adnan Lakdawala, Clinton S. Bolton III

Frontiers Digit. Health, 2022

Using Explainable Supervised Machine Learning to Predict Burnout in Healthcare Professionals.

Karthik Adapa, Malvika Pillai, Meagan Foster, Nadia Charguia, Lukasz Mazur

Proceedings of the Challenges of Trustable AI and Added-Value on Health, 2022

An Interpretable Machine Learning Approach to Prioritizing Factors Contributing to Clinician Burnout.

Malvika Pillai, Karthik Adapa, Meagan Foster, Ian Kratzke, Nadia Charguia, Lukasz Mazur

Proceedings of the Foundations of Intelligent Systems - 26th International Symposium, 2022

Using natural language processing to predict oral cancer risk with dental pathology notes¬¬.

Malvika Pillai, Javed Mostafa, Alexander Tropsha, Amanda Seyerle, Jamie Arguello, Di Wu

Proceedings of the AMIA 2022, 2022

An Interpretable Machine Learning Approach to Prioritizing Factors Contributing to Clinician Burnout.

Malvika Pillai, Karthik Adapa, Lukasz Mazur

Proceedings of the AMIA 2022, 2022

2021

Feature Engineering for Interpretable Machine Learning for Quality Assurance in Radiation Oncology.

Malvika Pillai, Karthik Adapa, John W. Shumway, John Dooley, Shiva K. Das, Bhishamjit S. Chera, Lukasz Mazur

Proceedings of the MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation, 2021

Predicting Objective Performance Using Perceived Cognitive Workload Data in Healthcare Professionals: A Machine Learning Study.

Karthik Adapa, Malvika Pillai, Shiva K. Das, Prithima Mosaly, Lukasz Mazur

Proceedings of the MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation, 2021

2019

Using Interactive Data Visualization to Drive Quality Improvement in Emergency Medical Services.

Rachel Stemerman, Malvika Pillai

Proceedings of the AMIA 2019, 2019

Augmenting Quality Assurance Measures in Radiation Oncology with Machine Learning.

Malvika Pillai, Karthik Adapa

Proceedings of the AMIA 2019, 2019