Mining electronic medical records for cancer treatment decisions
Abstract/Contents
- Abstract
- With the growing amount of medical data available and the increasing reliance of the medical community on artificial intelligence (AI) tools, there is an emerging demand for techniques that support medical decisions based on patient outcomes. To be adopted, machine learning (ML) tools must be accurate, trustworthy, and interpretable. Clinicians and patients should be able to understand the reasoning due to the high-stakes and sensitive nature of most medical decisions. Ultimately, medical ML tools will inform the decision-making process, empower clinicians and patients, and help improve clinical outcomes. We focus on using electronic medical records (EMRs) to improve retrospective comparative effectiveness research for reliable and interpretable decision-making in oncology. Decision making is fundamentally causal, intervening to improve outcomes. EMRs are a rich source of information that can be used to inform those decisions. Natural language processing (NLP) can analyze the unstructured clinical notes. Our research adapts causal inference, ML, and NLP techniques to discover insights from high-dimensional and high-noise EMR data. We study how we can 1) use NLP to identify cancer treatments, 2) reduce selection bias in observational studies, and 3) build ML tools to supplement radiologist decision making. In the first study, we use EMR notes to identify cancer treatments. We apply the method to prostate, esophagus, and oropharynx cancer datasets. It achieves over 90% accuracy for treatment identification. The method can be used to supplement cancer treatment records and help with future research on treatment planning and comparison. In the second study, we show how clinical notes can be used to uncover potential confounders and adjust for bias in retrospective comparative effectiveness studies. We apply our approach to prostate and lung cancer cohorts and found that we reduce the amount of bias when compared against established randomized control trials. In the third study, we develop a tool that could aid radiologist decision-making for mammogram diagnosis. It quantifies the decision threshold of each radiologist and can improve radiologist consistency and practice
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zeng, Jiaming |
|---|---|
| Degree supervisor | Athey, Susan |
| Degree supervisor | Shachter, Ross D |
| Thesis advisor | Athey, Susan |
| Thesis advisor | Shachter, Ross D |
| Thesis advisor | Rubin, Daniel (Daniel L.) |
| Degree committee member | Rubin, Daniel (Daniel L.) |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jiaming Zeng |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering |
| Thesis | Thesis Ph.D. Stanford University 2021 |
| Location | https://purl.stanford.edu/nz365qq5966 |
Access conditions
- Copyright
- © 2021 by Jiaming Zeng
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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