About me

I am postdoctoral researcher at University of California, San Francisco with Prof. Atul Butte. I have obtained a PhD in clinical Natural Language Processing at the Computational Linguistics and Psycholinguistics (CLiPS) Research Center, University of Antwerp, Belgium, under Prof. Dr. Walter Daelemans and Dr. Simon Šuster. My research interest is to develop foundation models to understand the nuances in electronic health record data, and to use these insights for answering clinical research questions. I am particularly interested in developing strategies for inferring causal patterns from observational textual data, improving generalization for low frequency data samples, and developing multi-modal and interpretable foundation models to improve the understanding of patients’ disease and treatment trajectory. I have extensive experience with the development of methodology for deep learning model interpretability and retrieval-augmented classification, and in creating benchmarking datasets for advanced oncology-specific information extraction from clinical notes.

During my PhD, I have worked as a research intern at the Google Brain Applied team in Zurich, where I investigated linguistic reasoning skills of BERT representations. I have additionally been involved in several academic service positions throughout. I hold a Master of Science in Language Science and Technology (spec. Language Technology) from Saarland University, Germany, and a Bachelor of Technology in Computer Science and Engineering from VIT University, Vellore, India. I have additionally worked on clinical text understanding as a Junior Research Developer at the Antwerp University Hospital, Belgium, and contributed towards recognizing textual entailment for the EU-funded Excitement project as a Research Assistant at the German Research Center for Artifical Intelligence (DFKI), Saarbrücken, Germany.

Research Grants

• Co-lead, NIH-NCI supplement award to the Helen Diller Family Comprehensive Cancer Center on Large language models for treatment and pathology data extraction ($300,000).

Selected Publications

• CORAL: Expert-Curated Oncology Reports to Advance Language Model Inference
  Madhumita Sushil, Vanessa E. Kennedy^#, Divneet Mandair^#, Brenda Miao, Travis Zack^*, Atul J. Butte^*
  New England Journal of Medicine (NEJM)-AI, 2024
  bibtex | preprint | Dataset

• Cross-institution natural language processing for reliable clinical association studies: a methodological exploration
  Madhumita Sushil, Atul J. Butte, Ewoud Schuit, Maarten van Smeden, Artuur M. Leeuwenberg
  Journal of Clinical Epidemiology, 2024
  bibtex

• Algorithmic identification of treatment-emergent adverse events from clinical notes using large language models: a pilot study in inflammatory bowel disease
  Anna L Silverman^*, Madhumita Sushil^*, Balu Bhasuran^*, Dana Ludwig, James Buchanan, Rebecca Racz, Mahalakshmi Parakala, Samer El-Kamary, Ohenewaa Ahima, Artur Belov, Lauren Choi, Monisha Billings, Yan Li, Nadia Habal, Qi Liu, Jawahar Tiwari, Atul J. Butte, and Vivek A. Rudrapatna.
  Journal of Clinical Pharmacology and Therapeutics, 2024.
  bibtex | preprint

• Topic modeling on clinical social work notes for exploring social determinants of health factors
  Shenghuan Sun, Travis Zack, Christopher Y. K. Williams, Madhumita Sushil*, Atul J. Butte*
  JAMIA Open, 2024
  bibtex

• Are we there yet? Exploring clinical domain knowledge of BERT models
  Madhumita Sushil, Simon Šuster, Walter Daelemans
  BioNLP Workshop, NAACL 2021
  bibtex | slides

• Contextual explanation rules for neural clinical classifiers
  Madhumita Sushil, Simon Šuster, Walter Daelemans
  BioNLP Workshop, NAACL 2021
  bibtex | code | poster

• Rule induction for global explanation of trained models
  Madhumita Sushil, Simon Šuster, Walter Daelemans
  Workshop on Analyzing and interpreting neural networks for NLP (BlackboxNLP), EMNLP 2018
  bibtex | code | poster

• Revisiting neural relation classification in clinical notes with external information
  Simon Šuster, Madhumita Sushil, Walter Daelemans
  Workshop on Health Text Mining and Information Analysis (LOUHI), EMNLP 2018
  bibtex | code | poster

• Patient representation learning and interpretable evaluation using clinical notes
  Madhumita Sushil, Simon Šuster, Kim Luyckx, Walter Daelemans
  Journal of Biomedical Informatics, 2018
  bibtex | code | arXiv preprint

• Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports
  Elyne Scheurwegs, Madhumita Sushil, Stéphan Tulkens, Walter Daelemans, Kim Luyckx
  Journal of Biomedical Informatics, 2017
  bibtex | code

Mentoring

• MEng Capstone project, UC Berkeley, 2022-2023: Breast cancer pathology extraction from pathology reports.
  Mentees: Ahmed Wali, Yan-Ning Yu, Yuwei Quan, Zhiwei Zheng
• Biomedical Informatics PhD student, UCSF: Social determinants of health exploration from social work notes.
  Mentee: Shenghuan Sun
• Google Summer of Code, 2019: Bias identification in machine learning models
  Mentee: Panagiotis Lantavos, Artistotle University of Thessaloniki

Service

• Reviewer/Program Committee Member: Journal of American Medical Informatics Association 2024; Journal of Biomedical Informatics 2023; Biomedical Natural Language Processing (BioNLP) workshop at ACL 2022, 2023; BMC Medical Informatics 2021; AMIA Informatics Summit ’23, AMIA Annual Symposium ’22, ’23; International Conference on Information Management and Big Data (SIMBig) ’21, ’23; Workshop on Machine Learning for Health at NeurIPS 2017, 2018, 2019; Student Research Workshop and Widening NLP Workshop at ACL 2019.
• Session chair: clinical NLP; AMIA 2021
• Student Board: EACL 2019–20.
• Co-chair: Student Research Workshop at EACL 2021.

Talks

• Natural language processing for inferences from electronic health record notes
  Clinical Informatics Data Science Pathway lecture series, UCSF, 2023.
  UCSF-Stanford Center of Excellence in Regulatory Science and Innovation (CERSI) EHR training series to the FDA, 2023.

• Lessons learned from clinical language processing
  Seminar at Butte lab, University of California-San Francisco, July 2020

• Synthetic dataset for explaining and evaluating rules learned by RNNs
  Blackbox@NL Workshop, May 2019

• Understanding Machine Learning models for healthcare: Why, and how?
  Project Accumulate Industry Meeting, March 2019

• Identifying Patients with Major Diabetes-related Complications
  CLiPS Lab Meeting, May 2018

• Model Agnostic Interpretability Techniques
  CLiPS Lab Meeting, March 2018

• Unsupervised patient representations with interpretable classification decisions
  Computational Linguistics in the Netherlands 28 (CLIN28), January 2018
  

• Clinical Data Characteristics and Processing Challenges
  Project Accumulate Technical Meeting, December 2016

• Psychiatric symptom severity identification, and experiences with cTakes
  Project Accumulate Technical Meeting, August 2016

Posters

• Large Language Models are Zero-shot Oncology Information Extractors
  AMIA Annual Symposium, 2023

• Training a transferrable clinical language model from 75 million notes
  AMIA Annual Symposium, 2022

• Rule induction for global explanation of recurrent neural classifiers
  3rd Google NLP Summit, Zurich, June 2019

• Symptom Severity Identification from Psychiatric Evaluation Notes
  ATILA Workshop, Nijmegen, October 2016

• Evolution of Language from an Information Theoretic Point of View
  Saarland University, May 2015

Thesis

• Exploring and Understanding Neural Models for Clinical Tasks
  Ph.D. Thesis, March 2021
  slides

• Recognizing Textual Entailment
  M.Sc. Thesis, February 2016
  slides