Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU
Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the jo...
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| Published in: | Artificial intelligence in medicine Vol. 137; p. 102493 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Netherlands
Elsevier B.V
01.03.2023
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| ISSN: | 0933-3657, 1873-2860, 1873-2860 |
| Online Access: | Get full text |
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| Abstract | Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model’s covariate-specific effect on oxygen therapy for more personalized intervention.
•Causal inference can help estimate causal effects, given the causal model is known.•Using Causal Inference, we aim to find the causal effect of oxygen therapy at ICU.•We leveraged observational data and expert knowledge to find underlying causal model.•We extracted cohort data from MIMIC-III database, a large public healthcare dataset.•Proposed causal method is suitable for exploring a broader set of clinical questions. |
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| AbstractList | Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention.Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention. Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention. Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model’s covariate-specific effect on oxygen therapy for more personalized intervention. •Causal inference can help estimate causal effects, given the causal model is known.•Using Causal Inference, we aim to find the causal effect of oxygen therapy at ICU.•We leveraged observational data and expert knowledge to find underlying causal model.•We extracted cohort data from MIMIC-III database, a large public healthcare dataset.•Proposed causal method is suitable for exploring a broader set of clinical questions. |
| ArticleNumber | 102493 |
| Author | Hasan, Uzma Adibuzzaman, Mohammad Adib, Riddhiman Kethireddy, Shravan Griffin, Paul Gani, Md Osman |
| Author_xml | – sequence: 1 givenname: Md Osman surname: Gani fullname: Gani, Md Osman email: mogani@umbc.edu organization: Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA – sequence: 2 givenname: Shravan surname: Kethireddy fullname: Kethireddy, Shravan email: kethirs@ccf.org organization: Respiratory Institute, Cleveland Clinic, Cleveland, OH, USA – sequence: 3 givenname: Riddhiman orcidid: 0000-0002-2855-342X surname: Adib fullname: Adib, Riddhiman email: adib@ohsu.edu organization: Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR, USA – sequence: 4 givenname: Uzma surname: Hasan fullname: Hasan, Uzma email: uzmahasan@umbc.edu organization: Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA – sequence: 5 givenname: Paul surname: Griffin fullname: Griffin, Paul email: pmg14@psu.edu organization: Department of Industrial and Manufacturing Engineering, Penn State University, University Park, PA, USA – sequence: 6 givenname: Mohammad surname: Adibuzzaman fullname: Adibuzzaman, Mohammad email: adibuzza@ohsu.edu organization: Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36868692$$D View this record in MEDLINE/PubMed |
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| Keywords | Causal inference Critical care Structural causal model Oxygen therapy Expert augmented knowledge |
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| Title | Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU |
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