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Yindalon Aphinyanagphongs
Yindalon Aphinyanaphongs – Yin Aphinyanaphongs
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2020 – today
- 2024
- [j14]Kar-mun C. Woo, Gregory W. Simon, Olumide Akindutire, Yindalon Aphinyanaphongs, Jonathan S. Austrian, Jung G. Kim, Nicholas Genes, Jacob A Goldenring, Vincent J. Major
, Chloé S. Pariente, Edwin G. Pineda, Stella K. Kang
Evaluation of GPT-4 ability to identify and generate patient instructions for actionable incidental radiology findings. J. Am. Medical Informatics Assoc. 31(9): 1983-1993 (2024) - [c31]Jeff Zhang, Yin Aphinyanaphongs, Anthony Cardillo:
NYULangone at Chemotimelines 2024: Utilizing Open-Weights Large Language Models for Chemotherapy Event Extraction. ClinicalNLP@NAACL 2024: 428-430 - [c30]David S. Restrepo, Luis Filipe Nakayama, Robyn Gayle Dychiao, Chenwei Wu, Liam G. McCoy, Jose Carlo Artiaga, Marisa Cobanaj, João Matos, Jack Gallifant, Danielle S. Bitterman, Vincenz Ferrer, Yindalon Aphinyanaphongs, Leo Anthony Celi:
Seeing Beyond Borders: Evaluating LLMs in Multilingual Ophthalmological Question Answering. ICHI 2024: 565-566 - [i13]Salman Rahman, Lavender Yao Jiang, Saadia Gabriel, Yindalon Aphinyanaphongs, Eric Karl Oermann, Rumi Chunara:
Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model. CoRR abs/2402.10965 (2024) - 2023
- [j13]Lavender Yao Jiang
Health system-scale language models are all-purpose prediction engines. Nat. 619(7969): 357-362 (2023) - [c29]Vincent J. Major, Walter Wang, Yindalon Aphinyanaphongs:
Enabling AI-Augmented Clinical Workflows by Accessing Patient Data in Real-Time with FHIR. ICHI 2023: 531-533 - [c28]Vincent J. Major, Claudia S. Plottel, Yindalon Aphinyanaphongs:
Ten Years of Health Informatics Education for Physicians. ICHI 2023: 637-644 - [i12]Yuxuan Hu, Albert Lui, Mark Goldstein, Mukund Sudarshan, Andrea Tinsay, Cindy Tsui, Samuel Maidman, John Medamana, Neil Jethani, Aahlad Manas Puli, Vuthy Nguy, Yindalon Aphinyanaphongs, Nicholas Kiefer, Nathaniel Smilowitz, James Horowitz, Tania Ahuja, Glenn I Fishman, Judith Hochman, Stuart Katz, Samuel Bernard, Rajesh Ranganath:
A dynamic risk score for early prediction of cardiogenic shock using machine learning. CoRR abs/2303.12888 (2023) - [i11]Alexander Peysakhovich, Rich Caruana, Yin Aphinyanaphongs:
Diagnosis Uncertain Models For Medical Risk Prediction. CoRR abs/2306.17337 (2023) - [i10]Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori, Mark E. Nunnally, Yin Aphinyanaphongs, Manolis Kellis, Rich Caruana:
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs. CoRR abs/2308.01157 (2023) - 2022
- [j12]Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Caleb Ellington, Rich Caruana:
Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study. J. Biomed. Informatics 130: 104086 (2022) - [j11]Batia Mishan Wiesenfeld
AI model transferability in healthcare: a sociotechnical perspective. Nat. Mac. Intell. 4(10): 807-809 (2022) - [c27]Elisabeth Yang, Yin Aphinyanaphongs, Paawan Punjabi, Jonathan S. Austrian, Batia Mishan Wiesenfeld:
Quantitative and Qualitative Evaluation of Provider Use of a Novel Machine Learning Model for Favorable Outcome Prediction. AMIA 2022 - [i9]Neil Jethani, Aahlad Manas Puli, Hao Zhang, Leonid Garber, Lior Jankelson, Yindalon Aphinyanaphongs, Rajesh Ranganath:
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography. CoRR abs/2205.02900 (2022) - [i8]Benjamin J. Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Rich Caruana:
Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML. CoRR abs/2211.08991 (2022) - 2021
- [j10]Oded Nov, Yindalon Aphinyanaphongs
The transformation of patient-clinician relationships with AI-based medical advice. Commun. ACM 64(3): 46-48 (2021) - [j9]Farah E. Shamout, Yiqiu Shen
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. npj Digit. Medicine 4 (2021) - [c26]Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath:
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. AISTATS 2021: 1459-1467 - [c25]Rich Caruana, Benjamin J. Lengerich, Yindalon Aphinyanaphongs:
Data-Driven Patterns in Protective Effects of Ibuprofen and Ketorolac on Hospitalized Covid-19 Patients. AMIA 2021 - [c24]Peter A. Stella, Elizabeth Haines, Yindalon Aphinyanaphongs:
Prediction of Resuscitation for Pediatric Sepsis from Data Available at Triage. AMIA 2021 - [c23]Seda Bilaloglu, Vincent J. Major, Himanshu Grover, Isabel Metzger, Yindalon Aphinyanaphongs:
Expanding the Reach of Structured EHR Data with Clinical Notes Improving End-of-Life Prediction. FLAIRS 2021 - [i7]Anuroop Sriram, Matthew J. Muckley, Koustuv Sinha, Farah Shamout, Joelle Pineau, Krzysztof J. Geras, Lea Azour, Yindalon Aphinyanaphongs, Nafissa Yakubova, William Moore:
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction. CoRR abs/2101.04909 (2021) - [i6]Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath:
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. CoRR abs/2103.01890 (2021) - 2020
- [j8]Vincent J. Major
Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites. BMC Medical Informatics Decis. Mak. 20(1): 214 (2020) - [j7]Narges Razavian
A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients. npj Digit. Medicine 3 (2020) - [c22]Isabel Metzger, Emir Y. Haskovic, Allison Black, Whitley M. Yi, Rajat S. Chandra, Mark T. Rutledge, William McMahon, Yindalon Aphinyanaphongs:
SMM4H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from Twitter: Machine Learning, Deep Learning, and Post-Processing. SMM4H@COLING 2020: 57-62 - [i5]Benedict Guzman, Isabel Metzger, Yindalon Aphinyanaphongs, Himanshu Grover:
Assessment of Amazon Comprehend Medical: Medication Information Extraction. CoRR abs/2002.00481 (2020) - [i4]Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanislaw Jastrzebski, Duo Wang, Ben Zhang
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. CoRR abs/2008.01774 (2020) - [i3]Oded Nov, Yindalon Aphinyanaphongs, Yvonne W. Lui, Devin M. Mann, Maurizio Porfiri, Mark O. Riedl, John-Ross Rizzo, Batia Mishan Wiesenfeld:
The Transformation of Patient-Clinician Relationships With AI-Based Medical Advice: A "Bring Your Own Algorithm" Era in Healthcare. CoRR abs/2008.05855 (2020)
2010 – 2019
- 2019
- [c21]Yindalon Aphinyanaphongs, Jonathan K. Wilt, Corey J. Chivers, Mark P. Sendak:
Translating, Implementing, Deploying, and Evaluating Clinical Interventions Using Machine Learning Based Predictive Models: Illustrative Case Studies. AMIA 2019 - [c20]Sara Kuppin Chokshi, Ji Chen, Roshini Hegde, Eduardo Iturrate, Yindalon Aphinyanaphongs, Javier Gonzalez, Devin M. Mann:
The Process of Developing, Validating and Operationalizing a Personalized Machine Learning Algorithm for Clinical Decision Support: A Case Study. AMIA 2019 - 2018
- [c19]Vincent J. Major, Alisa Surkis, Yindalon Aphinyanaphongs:
Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research. AMIA 2018 - 2017
- [c18]Yin Aphinyanaphongs, David Holmes, Berkman Sahiner, Parsa Mirhaji, Michael Draugelis:
The Good, The Bad, and The Ugly of Deploying and Adopting Machine Learning Based Models in Clinical Practice. AMIA 2017 - [c17]Vincent J. Major, Alisa Surkis, Yindalon Aphinyanaphongs:
Optimizing Parameters of word2vec for a Text Classification Task. AMIA 2017 - [c16]Walter Wang, Yindalon Aphinyanaphongs:
Neural Network Word Embeddings for Text Classification of MeSH terms. AMIA 2017 - [c15]Josua Krause, Aritra Dasgupta
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations. VAST 2017: 162-172 - [i2]Josua Krause, Aritra Dasgupta, Jordan Swartz, Yindalon Aphinyanaphongs, Enrico Bertini:
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations. CoRR abs/1705.01968 (2017) - [i1]Vincent J. Major, Alisa Surkis, Yindalon Aphinyanaphongs:
Utility of general and specific word embeddings for classifying translational stages of research. CoRR abs/1705.06262 (2017) - 2016
- [c14]Emily Kawaler, Yindalon Aphinyanaphongs:
Simplifying PheWAS Analysis: An R Package and Teaching Module. AMIA 2016 - [c13]Vincent J. Major, Monique S. Tanna, Simon Jones, Yindalon Aphinyanaphongs:
Reusable Filtering Functions for Application in ICU data: a case study. AMIA 2016 - [c12]Yin Aphinyanaphongs, Armine Lulejian, Duncan Penfold-Brown, Richard Bonneau, Paul Krebs:
Text Classification for Automatic Detection of E-Cigarette Use and Use for Smoking Cessation from Twitter: A Feasibility Pilot. PSB 2016: 480-491 - 2015
- [c11]Xuya Wang, Raul Caso Caso, Yindalon Aphinyanaphongs:
Building and evaluating predictive models for postoperative ileus prior to colorectal surgery. AMIA 2015 - [c10]Bisakha Ray, Yindalon Aphinyanaphongs
Text Classification-Based Automatic Recruitment of Patients for Clinical Trials: A Silver Standards-Based Case Study. ICHI 2015: 28-33 - 2014
- [j6]Yindalon Aphinyanaphongs
A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization. J. Assoc. Inf. Sci. Technol. 65(10): 1964-1987 (2014) - [c9]Yin Aphinyanaphongs, Bisakha Ray, Alexander R. Statnikov, Paul Krebs:
Text classification for automatic detection of alcohol use-related tweets: A feasibility study. IRI 2014: 93-97 - 2013
- [j5]Lawrence D. Fu, Yindalon Aphinyanaphongs
Computer models for identifying instrumental citations in the biomedical literature. Scientometrics 97(3): 871-882 (2013) - [c8]Bisakha Ray, Lawrence D. Fu, William Holloway, Yindalon Aphinyanaphongs:
Feasibility of Machine Learning Based Automatic Classification of Medical School Curricula. AMIA 2013 - [c7]Yindalon Aphinyanaphongs, Lawrence D. Fu, Constantin F. Aliferis:
Identifying Unproven Cancer Treatments on the Health Web: Addressing Accuracy, Generalizability and Scalability. MedInfo 2013: 667-671 - 2012
- [c6]Karen Hanson, Yindalon Aphinyanaphongs, Lawrence D. Fu:
Using Knowledge from APIs to Disambiguate Affiliation Names in MEDLINE. AMIA 2012 - 2011
- [j4]Lawrence D. Fu, Yindalon Aphinyanaphongs
A comparison of evaluation metrics for biomedical journals, articles, and websites in terms of sensitivity to topic. J. Biomed. Informatics 44(4): 587-594 (2011)
2000 – 2009
- 2007
- [c5]Lawrence D. Fu, Lily Wang, Yindalon Aphinyanagphongs, Constantin F. Aliferis:
A Comparison of Impact Factor, Clinical Query Filters, and Pattern Recognition Query Filters in Terms of Sensitivity to Topic. MedInfo 2007: 716-720 - [c4]Yindalon Aphinyanaphongs, Constantin F. Aliferis:
Text Categorization Models for Identifying Unproven Cancer Treatments on the Web. MedInfo 2007: 968-972 - 2006
- [j3]Elmer V. Bernstam, Jorge R. Herskovic
Research Paper: Using Citation Data to Improve Retrieval from MEDLINE. J. Am. Medical Informatics Assoc. 13(1): 96-105 (2006) - [j2]Yindalon Aphinyanaphongs
Research Paper: A Comparison of Citation Metrics to Machine Learning Filters for the Identification of High Quality MEDLINE Documents. J. Am. Medical Informatics Assoc. 13(4): 446-455 (2006) - [c3]Yindalon Aphinyanaphongs, Constantin F. Aliferis:
Prospective Validation of Text Categorization Filters for Identifying High-Quality, Content-Specific Articles in MEDLINE. AMIA 2006 - 2005
- [j1]Yindalon Aphinyanaphongs
Research Paper: Text Categorization Models for High-Quality Article Retrieval in Internal Medicine. J. Am. Medical Informatics Assoc. 12(2): 207-216 (2005) - 2004
- [c2]Yin Aphinyanaphongs, Constantin F. Aliferis:
Learning Boolean Queries for Article Quality Filtering. MedInfo 2004: 263-267 - 2003
- [c1]Yindalon Aphinyanaphongs, Constantin F. Aliferis:
Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine. AMIA 2003
Coauthor Index
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last updated on 2024-11-15 20:35 CET by the dblp team
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