자료유형  

 학위논문

Control Number  

0017163745

International Standard Book Number  

9798342108980

Dewey Decimal Classification Number  

616.94

Main Entry-Personal Name  

Fleming, Scott Lanyon.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

189 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

General Note  

Advisor: Brunskill, Emma;Shah, Nigam.

Dissertation Note  

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

Summary, Etc.  

요약Machine learning to support decision making in healthcare typically assumes a sufficient representation of the patient's history for learning and inference, such that all information required to perform accurate inference is present in the materialized patient timeline. We propose three principles for designing health data representations that are more expressive and comprehensive compared to traditional representation approaches, in order to enable clinical decision support tasks. Concretely, we propose that patient timeline representations (1) explicitly model clinicians' observation and intervention processes; (2) enable integration of unstructured data and structured data by representing all data as text; and (3) leverage this ability by including clinical notes. We apply these principles to several clinical challenges and provide evidence supporting the superiority of domain-guided representations for machine learning in healthcare. We show that reinforcement learning algorithms built on data models which explicitly include clinician observation and intervention processes yield context-aware policies that perform better in the realistic setting of patient observation costs compared to algorithms which do not model these processes. We show that using text as a unifying representation for both structured and unstructured data can enable large language models to follow electronic health record-based instructions, potentially streamlining common clinical workflows, and that incorporating clinical notes provides a key benefit for propensity score models in causal inference with observational data. By improving downstream model performance, our proposed principles for representing patient data could help clinical machine learning researchers and practitioners increase both the effectiveness and efficiency of healthcare delivery, leading to decreased costs and improved patient outcomes.

Subject Added Entry-Topical Term  

Sepsis.

Subject Added Entry-Topical Term  

Planning.

Subject Added Entry-Topical Term  

Decision making.

Subject Added Entry-Topical Term  

Medical research.

Subject Added Entry-Topical Term  

Taxonomy.

Subject Added Entry-Topical Term  

Large language models.

Subject Added Entry-Topical Term  

Filtering systems.

Subject Added Entry-Topical Term  

Medicine.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 86-04B.

Electronic Location and Access  

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Control Number  

joongbu:658399