Exploring a global interpretation mechanism for deep learning networks when predicting sepsis
Abstract The purpose of this study is to identify additional clinical features for sepsis detection through the use of a novel mechanism for interpreting black-box machine learning models trained and to provide a suitable evaluation for the mechanism. We use the publicly available dataset from the 2...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-30091-3 |
| _version_ | 1797864949766160384 |
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| author | Ethan A. T. Strickler Joshua Thomas Johnson P. Thomas Bruce Benjamin Rittika Shamsuddin |
| author_facet | Ethan A. T. Strickler Joshua Thomas Johnson P. Thomas Bruce Benjamin Rittika Shamsuddin |
| author_sort | Ethan A. T. Strickler |
| collection | DOAJ |
| description | Abstract The purpose of this study is to identify additional clinical features for sepsis detection through the use of a novel mechanism for interpreting black-box machine learning models trained and to provide a suitable evaluation for the mechanism. We use the publicly available dataset from the 2019 PhysioNet Challenge. It has around 40,000 Intensive Care Unit (ICU) patients with 40 physiological variables. Using Long Short-Term Memory (LSTM) as the representative black-box machine learning model, we adapted the Multi-set Classifier to globally interpret the black-box model for concepts it learned about sepsis. To identify relevant features, the result is compared against: (i) features used by a computational sepsis expert, (ii) clinical features from clinical collaborators, (iii) academic features from literature, and (iv) significant features from statistical hypothesis testing. Random Forest was found to be the computational sepsis expert because it had high accuracies for solving both the detection and early detection, and a high degree of overlap with clinical and literature features. Using the proposed interpretation mechanism and the dataset, we identified 17 features that the LSTM used for sepsis classification, 11 of which overlaps with the top 20 features from the Random Forest model, 10 with academic features and 5 with clinical features. Clinical opinion suggests, 3 LSTM features have strong correlation with some clinical features that were not identified by the mechanism. We also found that age, chloride ion concentration, pH and oxygen saturation should be investigated further for connection with developing sepsis. Interpretation mechanisms can bolster the incorporation of state-of-the-art machine learning models into clinical decision support systems, and might help clinicians to address the issue of early sepsis detection. The promising results from this study warrants further investigation into creation of new and improvement of existing interpretation mechanisms for black-box models, and into clinical features that are currently not used in clinical assessment of sepsis. |
| first_indexed | 2024-04-09T23:00:35Z |
| format | Article |
| id | doaj.art-4f6c94787f8c4fb58d99ed9a4d6f9722 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T23:00:35Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4f6c94787f8c4fb58d99ed9a4d6f97222023-03-22T10:59:59ZengNature PortfolioScientific Reports2045-23222023-02-0113111710.1038/s41598-023-30091-3Exploring a global interpretation mechanism for deep learning networks when predicting sepsisEthan A. T. Strickler0Joshua Thomas1Johnson P. Thomas2Bruce Benjamin3Rittika Shamsuddin4Physics and Mathematics, East Central UniversityDepartment of Internal Medicine, Rush University Medical CenterOklahoma State UniversitySchool of Biomedical Sciences, Center for Health SciencesOklahoma State UniversityAbstract The purpose of this study is to identify additional clinical features for sepsis detection through the use of a novel mechanism for interpreting black-box machine learning models trained and to provide a suitable evaluation for the mechanism. We use the publicly available dataset from the 2019 PhysioNet Challenge. It has around 40,000 Intensive Care Unit (ICU) patients with 40 physiological variables. Using Long Short-Term Memory (LSTM) as the representative black-box machine learning model, we adapted the Multi-set Classifier to globally interpret the black-box model for concepts it learned about sepsis. To identify relevant features, the result is compared against: (i) features used by a computational sepsis expert, (ii) clinical features from clinical collaborators, (iii) academic features from literature, and (iv) significant features from statistical hypothesis testing. Random Forest was found to be the computational sepsis expert because it had high accuracies for solving both the detection and early detection, and a high degree of overlap with clinical and literature features. Using the proposed interpretation mechanism and the dataset, we identified 17 features that the LSTM used for sepsis classification, 11 of which overlaps with the top 20 features from the Random Forest model, 10 with academic features and 5 with clinical features. Clinical opinion suggests, 3 LSTM features have strong correlation with some clinical features that were not identified by the mechanism. We also found that age, chloride ion concentration, pH and oxygen saturation should be investigated further for connection with developing sepsis. Interpretation mechanisms can bolster the incorporation of state-of-the-art machine learning models into clinical decision support systems, and might help clinicians to address the issue of early sepsis detection. The promising results from this study warrants further investigation into creation of new and improvement of existing interpretation mechanisms for black-box models, and into clinical features that are currently not used in clinical assessment of sepsis.https://doi.org/10.1038/s41598-023-30091-3 |
| spellingShingle | Ethan A. T. Strickler Joshua Thomas Johnson P. Thomas Bruce Benjamin Rittika Shamsuddin Exploring a global interpretation mechanism for deep learning networks when predicting sepsis Scientific Reports |
| title | Exploring a global interpretation mechanism for deep learning networks when predicting sepsis |
| title_full | Exploring a global interpretation mechanism for deep learning networks when predicting sepsis |
| title_fullStr | Exploring a global interpretation mechanism for deep learning networks when predicting sepsis |
| title_full_unstemmed | Exploring a global interpretation mechanism for deep learning networks when predicting sepsis |
| title_short | Exploring a global interpretation mechanism for deep learning networks when predicting sepsis |
| title_sort | exploring a global interpretation mechanism for deep learning networks when predicting sepsis |
| url | https://doi.org/10.1038/s41598-023-30091-3 |
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