東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Datasets." ]

切換: 標籤 | MARC模式 | ISBD

Deep Understanding and Generation of...

Zhang, Yuhao.

FindBook

Google Book

Amazon

博客來

Deep Understanding and Generation of Medical Text and Beyond.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Understanding and Generation of Medical Text and Beyond./
作者:	Zhang, Yuhao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	176 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
標題:	Internships. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28483304
ISBN:	9798505571583

Deep Understanding and Generation of Medical Text and Beyond.
Zhang, Yuhao.

Deep Understanding and Generation of Medical Text and Beyond. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 176 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

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

Human language text plays a pivotal role in medicine. We use text to represent and store our biomedical knowledge, to communicate clinical findings, and to document various forms of medical data as well as healthcare outcomes. While deep language understanding techniques based on neural representation learning have fundamentally advanced our ability to process human language, can we leverage this advancement to transform our ability to understand, generate and utilize medical text? If so, how can we achieve this goal?This dissertation aims to provide answers to these questions from three distinct perspectives. We first focus on a common form of medical text, biomedical scientific text, and study the long-standing challenge of extracting structured relational knowledge from this text. To handle the long textual context where biomedical relations are commonly found, we introduce a novel linguistically-motivated neural architecture that learns to represent a relation by exploiting the syntactic structure of a sentence. We show that this model not only demonstrates robust performance for biomedical relation extraction, but also achieves a new state of the art on relation extraction over general-domain text.In the second part of this work, we focus on a different form of medical text, clinical report text, and more specifically, the radiology report text commonly used to describe medical imaging studies. We study the challenging problem of compressing long, detailed radiology reports into more succinct summary text. We demonstrate how a neural sequence-to-sequence model that is tailored to the structure of radiology reports can learn to generate fluent summaries with substantial clinical validity. We further present a reinforcement learning-based method that optimizes this system for correctness, a crucial metric in medicine. Our system has the potential of saving doctors from repetitive labor and improving clinical communications.Finally, we connect text and image modalities in medicine, by addressing the challenge of transferring the knowledge that we learn from text understanding to understanding medical images. We present a novel method for improving medical image understanding by jointly modeling text and images in an unsupervised, contrastive manner. By leveraging the knowledge encoded in text, our method reduces the amount of labeled data needed for medical image understanding by an order of magnitude. Altogether, our studies demonstrate the great potential that deep language understanding and generation has in transforming medicine.

ISBN: 9798505571583Subjects--Topical Terms:

3560137
Internships.

Deep Understanding and Generation of Medical Text and Beyond.
LDR:03586nmm a2200301 4500 001 2283806
005 20211115071652.5
008 220723s2021 ||||||||||||||||| ||eng d
020 $a 9798505571583
035 $a (MiAaPQ)AAI28483304
035 $a (MiAaPQ)STANFORDxg033vd7236
035 $a AAI28483304
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Yuhao. $3 3562844
245 1 0 $a Deep Understanding and Generation of Medical Text and Beyond.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 176 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
500 $a Advisor: Manning, Christopher;Langlotz, Curtis.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Human language text plays a pivotal role in medicine. We use text to represent and store our biomedical knowledge, to communicate clinical findings, and to document various forms of medical data as well as healthcare outcomes. While deep language understanding techniques based on neural representation learning have fundamentally advanced our ability to process human language, can we leverage this advancement to transform our ability to understand, generate and utilize medical text? If so, how can we achieve this goal?This dissertation aims to provide answers to these questions from three distinct perspectives. We first focus on a common form of medical text, biomedical scientific text, and study the long-standing challenge of extracting structured relational knowledge from this text. To handle the long textual context where biomedical relations are commonly found, we introduce a novel linguistically-motivated neural architecture that learns to represent a relation by exploiting the syntactic structure of a sentence. We show that this model not only demonstrates robust performance for biomedical relation extraction, but also achieves a new state of the art on relation extraction over general-domain text.In the second part of this work, we focus on a different form of medical text, clinical report text, and more specifically, the radiology report text commonly used to describe medical imaging studies. We study the challenging problem of compressing long, detailed radiology reports into more succinct summary text. We demonstrate how a neural sequence-to-sequence model that is tailored to the structure of radiology reports can learn to generate fluent summaries with substantial clinical validity. We further present a reinforcement learning-based method that optimizes this system for correctness, a crucial metric in medicine. Our system has the potential of saving doctors from repetitive labor and improving clinical communications.Finally, we connect text and image modalities in medicine, by addressing the challenge of transferring the knowledge that we learn from text understanding to understanding medical images. We present a novel method for improving medical image understanding by jointly modeling text and images in an unsupervised, contrastive manner. By leveraging the knowledge encoded in text, our method reduces the amount of labeled data needed for medical image understanding by an order of magnitude. Altogether, our studies demonstrate the great potential that deep language understanding and generation has in transforming medicine.
590 $a School code: 0212.
650 4 $a Internships. $3 3560137
650 4 $a Datasets. $3 3541416
650 4 $a Informatics. $3 2142115
650 4 $a Error analysis. $3 3562845
650 4 $a Ablation. $3 3562462
650 4 $a Image retrieval. $3 3562846
650 4 $a Mathematics. $3 515831
690 $a 0405
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 83-04B.
790 $a 0212
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28483304