國立虎尾科技大學 |

Transformer Models for Clinical Target Prediction Using Pathology Report Text.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Transformer Models for Clinical Target Prediction Using Pathology Report Text./
Author:	Kefeli, Jenna.
Description:	1 online resource (135 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:	Dissertations Abstracts International85-07B.
Subject:	Systematic biology. -
Online resource:	click for full text (PQDT)
ISBN:	9798381326123

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

Transformer Models for Clinical Target Prediction Using Pathology Report Text. - 1 online resource (135 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381326123Subjects--Topical Terms:

1179695
Systematic biology.
Subjects--Index Terms:

Electronic health recordIndex Terms--Genre/Form:

554714
Electronic books.

Transformer Models for Clinical Target Prediction Using Pathology Report Text.
LDR:03065ntm a2200433K 4500 001 1141648
005 20240318062706.5
006 m o d
007 cr mn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798381326123
035 $a (MiAaPQ)AAI30812528
035 $a AAI30812528
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Kefeli, Jenna. $3 1465560
245 1 0 $a Transformer Models for Clinical Target Prediction Using Pathology Report Text.
264 0 $c 2024
300 $a 1 online resource (135 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
500 $a Advisor: Tatonetti, Nicholas.
502 $a Thesis (Ph.D.)--Columbia University, 2024.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Systematic biology. $3 1179695
650 4 $a Computer science. $3 573171
650 4 $a Bioinformatics. $3 583857
650 4 $a Oncology. $3 593951
650 4 $a Pathology. $3 668553
653 $a Electronic health record
653 $a Machine learning
653 $a Natural language processing
653 $a Prostate cancer
653 $a Drug representations
655 7 $a Electronic books. $2 local $3 554714
690 $a 0423
690 $a 0984
690 $a 0715
690 $a 0992
690 $a 0800
690 $a 0571
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Columbia University. $b Cellular, Molecular and Biomedical Studies. $3 1193648
773 0 $t Dissertations Abstracts International $g 85-07B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30812528 $z click for full text (PQDT)