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Majid Afshar
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2020 – today
- 2024
- [j19]Jifan Gao, Guanhua Chen, Ann P. O'Rourke, John R. Caskey, Kyle A. Carey, Madeline Oguss, Anne Stey, Dmitriy Dligach, Timothy A. Miller, Anoop M. Mayampurath, Matthew M. Churpek, Majid Afshar:
Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models. J. Am. Medical Informatics Assoc. 31(6): 1291-1302 (2024) - [j18]Fereshteh S. Bashiri, Kyle A. Carey, Jennie Martin, Jay L. Koyner, Dana P. Edelson, Emily R. Gilbert, Anoop M. Mayampurath, Majid Afshar, Matthew M. Churpek:
Development and external validation of deep learning clinical prediction models using variable-length time series data. J. Am. Medical Informatics Assoc. 31(6): 1322-1330 (2024) - [j17]Majid Afshar, Yanjun Gao, Deepak Gupta, Emma Croxford, Dina Demner-Fushman:
On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models. J. Biomed. Informatics 157: 104707 (2024) - [i16]Shan Chen, Jack Gallifant, Marco Guevara, Yanjun Gao, Majid Afshar, Timothy Miller, Dmitriy Dligach, Danielle S. Bitterman:
Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data. CoRR abs/2403.19511 (2024) - [i15]Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy Miller, Danielle S. Bitterman, Matthew M. Churpek, Majid Afshar:
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications? CoRR abs/2408.11854 (2024) - [i14]Skatje Myers, Timothy A. Miller, Yanjun Gao, Matthew M. Churpek, Anoop M. Mayampurath, Dmitriy Dligach, Majid Afshar:
Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies. CoRR abs/2409.15163 (2024) - [i13]Emma Croxford, Yanjun Gao, Nicholas Pellegrino, Karen K. Wong, Graham Wills, Elliot First, Frank J. Liao, Cherodeep Goswami, Brian W. Patterson, Majid Afshar:
Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review. CoRR abs/2409.18170 (2024) - 2023
- [j16]Weipeng Zhou, Meliha Yetisgen, Majid Afshar, Yanjun Gao, Guergana Savova, Timothy A. Miller:
Improving model transferability for clinical note section classification models using continued pretraining. J. Am. Medical Informatics Assoc. 31(1): 89-97 (2023) - [j15]Yanjun Gao, Dmitriy Dligach, Timothy A. Miller, John R. Caskey, Brihat Sharma, Matthew M. Churpek, Majid Afshar:
DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing. J. Biomed. Informatics 138: 104286 (2023) - [j14]Yanjun Gao, Dmitriy Dligach, Timothy Miller, Matthew M. Churpek, Özlem Uzuner, Majid Afshar:
Progress Note Understanding - Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 shared task. J. Biomed. Informatics 142: 104346 (2023) - [c16]Brihat Sharma, Yanjun Gao, Timothy A. Miller, Matthew M. Churpek, Majid Afshar, Dmitriy Dligach:
Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning. ClinicalNLP@ACL 2023: 78-85 - [c15]Weipeng Zhou, Majid Afshar, Dmitriy Dligach, Yanjun Gao, Timothy Miller:
Improving the Transferability of Clinical Note Section Classification Models with BERT and Large Language Model Ensembles. ClinicalNLP@ACL 2023: 125-130 - [c14]Yanjun Gao, Dmitriy Dligach, Timothy Miller, Majid Afshar:
Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes. BioNLP@ACL 2023: 461-467 - [i12]Yanjun Gao, Dmitriy Dligach, Timothy A. Miller, Matthew M. Churpek, Özlem Uzuner, Majid Afshar:
Progress Note Understanding - Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 Shared Task. CoRR abs/2303.08038 (2023) - [i11]Brihat Sharma, Yanjun Gao, Timothy A. Miller, Matthew M. Churpek, Majid Afshar, Dmitriy Dligach:
Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning. CoRR abs/2306.04551 (2023) - [i10]Yanjun Gao, Dmitriy Dligach, Timothy Miller, Matthew M. Churpek, Majid Afshar:
Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes. CoRR abs/2306.05270 (2023) - [i9]Yanjun Gao, Ruizhe Li, John R. Caskey, Dmitriy Dligach, Timothy A. Miller, Matthew M. Churpek, Majid Afshar:
Leveraging A Medical Knowledge Graph into Large Language Models for Diagnosis Prediction. CoRR abs/2308.14321 (2023) - [i8]Weipeng Zhou, Danielle S. Bitterman, Majid Afshar, Timothy A. Miller:
Considerations for health care institutions training large language models on electronic health records. CoRR abs/2309.12339 (2023) - [i7]Shan Chen, Marco Guevara, Shalini Moningi, Frank Hoebers, Hesham Elhalawani, Benjamin H. Kann, Fallon E. Chipidza, Jonathan Leeman, Hugo J. W. L. Aerts, Timothy A. Miller, Guergana K. Savova, Raymond H. Mak, Maryam Lustberg, Majid Afshar, Danielle S. Bitterman:
The impact of using an AI chatbot to respond to patient messages. CoRR abs/2310.17703 (2023) - 2022
- [j13]Majid Afshar, Hamid Usefi:
Optimizing feature selection methods by removing irrelevant features using sparse least squares. Expert Syst. Appl. 200: 116928 (2022) - [j12]Frank Liao, Sabrina Adelaine, Majid Afshar, Brian W. Patterson:
Governance of Clinical AI applications to facilitate safe and equitable deployment in a large health system: Key elements and early successes. Frontiers Digit. Health 4 (2022) - [j11]Liam Butler, Ibrahim Karabayir, Mohammad Samie Tootooni, Majid Afshar, Ari Goldberg, Oguz Akbilgic:
Image and structured data analysis for prognostication of health outcomes in patients presenting to the ED during the COVID-19 pandemic. Int. J. Medical Informatics 158(February): 104662 (2022) - [j10]Fereshteh S. Bashiri, John R. Caskey, Anoop M. Mayampurath, Nicole Dussault, Jay Dumanian, Sivasubramanium V. Bhavani, Kyle A. Carey, Emily R. Gilbert, Christopher J. Winslow, Nirav S. Shah, Dana P. Edelson, Majid Afshar, Matthew M. Churpek:
Identifying infected patients using semi-supervised and transfer learning. J. Am. Medical Informatics Assoc. 29(10): 1696-1704 (2022) - [j9]Yanjun Gao, Dmitriy Dligach, Leslie Christensen, Samuel Tesch, Ryan Laffin, Dongfang Xu, Timothy A. Miller, Özlem Uzuner, Matthew M. Churpek, Majid Afshar:
A scoping review of publicly available language tasks in clinical natural language processing. J. Am. Medical Informatics Assoc. 29(10): 1797-1806 (2022) - [c13]Anoop M. Mayampurath, Kyle A. Carey, Priti Jani, Majid Afshar, Matthew M. Churpek, Dmitriy Dligach:
Explaining Alerts from a Pediatric Deterioration Prediction Model Using Clinical Text. AMIA 2022 - [c12]Yanjun Gao, Dmitriy Dligach, Timothy Miller, Dongfang Xu, Matthew M. Churpek, Majid Afshar:
Summarizing Patients' Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models. COLING 2022: 2979-2991 - [c11]Yanjun Gao, Dmitriy Dligach, Timothy A. Miller, Samuel Tesch, Ryan Laffin, Matthew M. Churpek, Majid Afshar:
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding. LREC 2022: 5484-5493 - [i6]Yanjun Gao, Dmitriy Dligach, Timothy A. Miller, Samuel Tesch, Ryan Laffin, Matthew M. Churpek, Majid Afshar:
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding. CoRR abs/2204.03035 (2022) - [i5]Yanjun Gao, Dmitriy Dligach, Timothy A. Miller, Dongfang Xu, Matthew M. Churpek, Majid Afshar:
Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models. CoRR abs/2208.08408 (2022) - [i4]Yanjun Gao, Dmitriy Dligach, Timothy A. Miller, John R. Caskey, Brihat Sharma, Matthew M. Churpek, Majid Afshar:
DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing. CoRR abs/2209.14901 (2022) - 2021
- [j8]Hale M. Thompson, Brihat Sharma, Sameer Bhalla, Randy Boley, Connor McCluskey, Dmitriy Dligach, Matthew M. Churpek, Niranjan S. Karnik, Majid Afshar:
Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups. J. Am. Medical Informatics Assoc. 28(11): 2393-2403 (2021) - [j7]Dmitriy Dligach, Majid Afshar, Timothy A. Miller:
Pre-training phenotyping classifiers. J. Biomed. Informatics 113: 103626 (2021) - [c10]Marissa Borgese, Cara Joyce, Emily E. Anderson, Matthew M. Churpek, Majid Afshar:
Bias Assessment and Correction in Machine Learning Algorithms: A Use-Case in a Natural Language Processing Algorithm to Identify Hospitalized Patients with Unhealthy Alcohol Use. AMIA 2021 - [c9]John R. Caskey, Fereshteh S. Bashiri, Anoop M. Mayampurath, Nicole Dussault, Jay Dumanian, Sivasubramanium V. Bhavani, Kyle A. Carey, Emily R. Gilbert, Christopher J. Winslow, Nirav S. Shah, Dana P. Edelson, Majid Afshar, Matthew M. Churpek:
Sepsis Prediction Using Semi-Supervised and Transfer Learning. AMIA 2021 - [c8]Brihat Sharma, Dmitriy Dligach, Hale Thomson, Matthew M. Churpek, Niranjan S. Karnik, Majid Afshar:
A multi-label classifier to screen different types of substance misuse in hospitalized patients. AMIA 2021 - [c7]Daniel To, Cara Joyce, Sujay Kulshrestha, Brihat Sharma, Dmitriy Dligach, Matthew M. Churpek, Majid Afshar:
The Addition of United States Census-Tract Data Does Not Improve the Prediction of Substance Misuse. AMIA 2021 - [c6]Majid Afshar, Saeed Samet, Hamid Usefi:
Incorporating Behavior in Attribute Based Access Control Model Using Machine Learning. SysCon 2021: 1-8 - [i3]Xin Su, Timothy Miller, Xiyu Ding, Majid Afshar, Dmitriy Dligach:
Classifying Long Clinical Documents with Pre-trained Transformers. CoRR abs/2105.06752 (2021) - [i2]Yanjun Gao, Dmitriy Dligach, Leslie Christensen, Samuel Tesch, Ryan Laffin, Dongfang Xu, Timothy A. Miller, Özlem Uzuner, Matthew M. Churpek, Majid Afshar:
A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing. CoRR abs/2112.05780 (2021) - 2020
- [j6]Majid Afshar, Hamid Usefi:
High-dimensional feature selection for genomic datasets. Knowl. Based Syst. 206: 106370 (2020) - [j5]Brihat Sharma, Dmitriy Dligach, Kristin Swope, Elizabeth Salisbury-Afshar, Niranjan S. Karnik, Cara Joyce, Majid Afshar:
Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients. BMC Medical Informatics Decis. Mak. 20(1): 79 (2020) - [c5]Xin Su, Timothy A. Miller, Majid Afshar, Dmitriy Dligach:
Learning Hierarchical Transformer-based Representations of Clinical Notes. AMIA 2020 - [i1]Majid Afshar, Hamid Usefi:
High-Dimensional Feature Selection for Genomic Datasets. CoRR abs/2002.12104 (2020)
2010 – 2019
- 2019
- [j4]Majid Afshar, Andrew Phillips, Niranjan S. Karnik, Jeanne Mueller, Daniel To, Richard Gonzalez, Ron Price, Richard S. Cooper, Cara Joyce, Dmitriy Dligach:
Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation. J. Am. Medical Informatics Assoc. 26(3): 254-261 (2019) - [j3]Dmitriy Dligach, Majid Afshar, Timothy A. Miller:
Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuse. J. Am. Medical Informatics Assoc. 26(11): 1272-1278 (2019) - [j2]Majid Afshar, Dmitriy Dligach, Brihat Sharma, Xiaoyuan Cai, Jason Boyda, Steven Birch, Daniel Valdez, Suzan Zelisko, Cara Joyce, François Modave, Ron Price:
Development and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies. J. Am. Medical Informatics Assoc. 26(11): 1364-1369 (2019) - [j1]Meisam Amani, Sahel Mahdavi, Majid Afshar, Brian Brisco, Weimin Huang, Sayyed Mohammad Javad Mirzadeh, Lori White, Sarah N. Banks, Joshua Montgomery, Christopher Hopkinson:
Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results. Remote. Sens. 11(7): 842 (2019) - [c4]Dmitriy Dligach, Majid Afshar, Timothy A. Miller:
Towards a Universal Document-Level Clinical Text Encoder: Methods for Neural Network Pre-training with Applications to Substance Misuse. AMIA 2019 - [c3]Amy L. Olex, Tamás Gál, Majid Afshar, Dmitriy Dligach, Niranjan S. Karnik, Travis Oakes, Brihat Sharma, Meng Xie, Bridget T. McInnes, Julian Solway, Abel N. Kho, William Cramer, F. G. Moeller:
Untapped Potential of Clinical Text for Opioid Surveillance. AMIA 2019 - [c2]Brihat Sharma, Majid Afshar, Dmitriy Dligach, Robert Kanie, Elizabeth Salisbury-Afshar, Niranjan S. Karnik, Cara Joyce:
Identification of Latent Subtypes of Patients with Opioid Misuse. AMIA 2019 - 2018
- [c1]Majid Afshar, Cara Joyce, Anthony Oakey, Perry Formanek, Philip Yang, Matthew M. Churpek, Richard S. Cooper, Ron Price, Susan J. Zelisko, Dmitriy Dligach:
A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning. AMIA 2018
Coauthor Index
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last updated on 2024-10-18 19:26 CEST by the dblp team
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