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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Aparajita Kashyap
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients
Published in Nature Scientific Reports, 2023
We leverage JH-CROWN: The COVID Precision Medicine Analytics Platform Registry to identify subgroups of COVID-19 patients who are at high risks for severe disease progression.
Recommended citation: Cowley HP, Robinette MS, Matelsky JK, Xenes D, Kashyap A., Ibrahim NF, Robinson ML, Zeger S, Garibaldi BT, Gray-Roncal W. Using machine learning on clinical data to identify unexpected patterns in groups of covid-19 patients. Scientific Reports, 13(1). February 2023. https://doi.org/10.1038/s41598-022-26294-9 https://www.nature.com/articles/s41598-022-26294-9
Trade-offs in concentration sensing in dynamic environments
Published in Biophysical Journal, 2024
We model a eukaryotic cell sensing a chemical secreted from bacteria and develop analytical calculations and stochastic simulations of sensing in this environment. We find that cells can have a huge variety of optimal sensing strategies ranging from not time averaging at all to averaging over an arbitrarily long time or having a finite optimal averaging time.
Recommended citation: Kashyap A, Wang W, Camley BA. Trade-offs in concentration sensing in dynamic environments. Biophysical Journal, 123(10). May 2024. 10.1016/j.bpj.2024.03.025 https://www.cell.com/biophysj/fulltext/S0006-3495(24)00205-4
The Impact of Evolving Endometriosis Guidelines on Diagnosis and Observational Health Studies
Published in medRxiv [Preprint], 2024
We define five cohorts of endometriosis patients, each based on different diagnostic criteria, and examine population differences between individuals within each phenotype.
Recommended citation: Reyes Nieva H, Kashyap A, Voss EA, Ostropolets A, Anand A, Ketenci M, DeFalco FJ, Choi YS, Li Y, Allen MN, Guang S, Natarajan K, Ryan P, Elhadad N. The Impact of Evolving Endometriosis Guidelines on Diagnosis and Observational Health Studies. medRxiv [Preprint]. 2024 December. doi: 10.1101/2024.12.13.24319010. https://pmc.ncbi.nlm.nih.gov/articles/PMC11702737/
Investigating racial disparities in drug prescriptions for patients with endometriosis
Published in npj Women's Health, 2025
We assess racial disparities in medication prescription patterns for endometriosis patients across Medicaid administrative claims data, and find larger disparities pre-diagnosis compared to post-diagnosis.
Recommended citation: Kashyap, A., Aziz, M., Sun, T.Y., Lipsky Gorman, S., Opoku-Anane, J., Elhadad, N. Investigating racial disparities in drug prescriptions for patients with endometriosis. npj Womens Health 3, 6 (2025). https://doi.org/10.1038/s44294-025-00053-3 https://www.nature.com/articles/s44294-025-00053-3
Something Similar: Exploring the Usefulness of an On-the-Spot Meal Recommendation System for Health Goal Attainment
Published in Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2025
We conduct a feasibility study to evaluate the utility of similarity-based recommendations in constrained settings.
Recommended citation: Desai PM, Raj A, Albers D, Kashyap A, Mamykina L. 2025. Something Similar: Exploring the Usefulness of an On-the-Spot Meal Recommendation System for Health Goal Attainment. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '25). Association for Computing Machinery, New York, NY, USA, Article 490, 1–8. https://doi.org/10.1145/3706599.3719813 https://dl.acm.org/doi/abs/10.1145/3706599.3719813
FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records
Published in ArXiv [Preprint], 2025
We benchmark foundation models for structured electronic health record data, focusing on clinically relevant tasks and assessing models for discrimination, calibration, and fairness.
Recommended citation: Pang C, Jeanselme V, Choi YS, Jiang X, Jing Z, Kashyap A, Kobayashi Y, Li Y, Pollet F, Natarajan K, Joshi S. FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records. ArXiv [Preprint]. 2025 June. doi: https://arxiv.org/abs/2505.16941 https://arxiv.org/abs/2505.16941
talks
Building a Research Agenda for Justice Informatics
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Co-facilitator of the workshop put on by the Justice Informatics group, focused on assessing the state of justice-oriented research practices in the informatics field.
Investigating racial inequality in drug prescriptions for patients with endometriosis
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Podium abstract at the World Congress on Endometriosis (2023) on racial disparities in drug prescriptions for patients with endometriosis
Synthesizing Gaps and Priorities for a Justice Informatics Research
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Co-presenter of the Justice Informatics Collaborative’s podium abstract; we presented our principles of justice-oriented biomedical informatics research synthesized from our previous workshop and suggested next steps for various stakeholders interested in incorporating justice into their work.
Machine learning for time-series biomedical data
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I was a guest lecturer for Columbia University’s Machine Learning for Healthcare class. My lecture covered processing of various types of time-series biomedical data and provided an overview of machine learning methods suitable for time-series data (e.g. RNNs, transformers, survival analysis).
Machine learning predicts diagnostic transition to schizophrenia using administrative claims data
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Invited lecture about leveraging deep learning to predict diagnostic transition from psychosis to schizophrenia
teaching
Acculturation to Programming and Statistics
Teaching Assistant, Columbia University, 2023
I was a TA for Acculturation to Programming and Statistics in the Departemnt of Biomedical Informatics at Columbia University. This class is caters to students from a wide variety of mathematical and computational backgrounds and provides tools for navigating the informatics landscape. For this class, I created and delivered weekly lectures and semiweekly lab assignments.
Machine Learning for Healthcare
Teaching Assistant, Columbia University, 2024
I was a TA for Machine Learning for Healthcare in the Departemnt of Biomedical Informatics at Columbia University. This class offers a survey of various machine learning topics, with an emphasis on biological and clinical applications. For this class, I designed weekly homework assignments and mentored students for their final project.