( en | de | ru )

 

Disambiguation of "Churpek, Matthew M."

Author of the publication

copydeleteadd this publication to your clipboard
A multi-label classifier to screen different types of substance misuse in hospitalized patients.

, , , , , and . AMIA, AMIA, (2021)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Matthew Mellon

Matthew David

Matthew Smith

Olumuyiwa Matthew

Peter Matthew

 

Other publications of authors with the same name

A scoping review of publicly available language tasks in clinical natural language processing., , , , , , , , , and . J. Am. Medical Informatics Assoc., 29 (10): 1797-1806 (2022)DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing., , , , , , and . J. Biomed. Informatics, (February 2023)Identifying infected patients using semi-supervised and transfer learning., , , , , , , , , and 3 other author(s). J. Am. Medical Informatics Assoc., 29 (10): 1696-1704 (2022)Sepsis Prediction Using Semi-Supervised and Transfer Learning., , , , , , , , , and 3 other author(s). AMIA, AMIA, (2021)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., , , , and . AMIA, AMIA, (2021)Leveraging A Medical Knowledge Graph into Large Language Models for Diagnosis Prediction., , , , , , and . CoRR, (2023)DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing., , , , , , and . CoRR, (2022)Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding., , , , , , and . LREC, page 5484-5493. European Language Resources Association, (2022)Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups., , , , , , , , and . J. Am. Medical Informatics Assoc., 28 (11): 2393-2403 (2021)Summarizing Patients' Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models., , , , , and . COLING, page 2979-2991. International Committee on Computational Linguistics, (2022)