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Transformer Models for Clinical Targ...

Kefeli, Jenna.

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Transformer Models for Clinical Target Prediction Using Pathology Report Text.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Transformer Models for Clinical Target Prediction Using Pathology Report Text./
作者:	Kefeli, Jenna.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	135 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:	Dissertations Abstracts International85-07B.
標題:	Systematic biology. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30812528
ISBN:	9798381326123

Transformer Models for Clinical Target Prediction Using Pathology Report Text.
Kefeli, Jenna.

Transformer Models for Clinical Target Prediction Using Pathology Report Text. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 135 p.

Source: Dissertations Abstracts International, Volume: 85-07, Section: B.

Thesis (Ph.D.)--Columbia University, 2024.

This item must not be sold to any third party vendors.

Structured electronic health record (EHR) data are commonly incomplete and can lack diagnostic detail. Clinical reports, on the other hand, are typically comprehensive and contain a wealth of detailed medical information. Pathologists invest considerable time and specialized training to create information-rich pathology reports, but the necessary manual review of these reports for clinical or research use is a high barrier to their routine utilization. The automated extraction of clinical targets directly from pathology reports would allow for the structured aggregation of relevant patient data that are not currently routinely captured in the EHR.In this dissertation, I apply recently developed transformer models to predict clinical targets from cancer pathology report text. In the first chapter, I present a pathology report corpus that I fully processed and made publicly available, and perform a proof-of-concept cancer type classification. In the second chapter, I discuss a set of cancer stage classification models that I fine-tune on the pathology report corpus and then externally validate on reports from Columbia University Irving Medical Center (CUIMC). In the last chapter, I explore additional applications for this methodology, developing a generalizable model to classify prostate cancer reports into primary Gleason score categories, applying a transformer model to classify reports into diagnosis categories for a Barrett's esophagus patient cohort in a low-data environment, and performing a proof-of-concept prediction of adverse drug events from 1D drug representations.

ISBN: 9798381326123Subjects--Topical Terms:

3173492
Systematic biology.
Subjects--Index Terms:

Electronic health record

Transformer Models for Clinical Target Prediction Using Pathology Report Text.
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506 $a This item must not be sold to any third party vendors.
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