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He B, Qiu Z. Development and validation of an interpretable machine learning for mortality prediction in patients with sepsis. Front Artif Intell 2024; 7:1348907. [PMID: 39040922 PMCID: PMC11262051 DOI: 10.3389/frai.2024.1348907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 06/26/2024] [Indexed: 07/24/2024] Open
Abstract
Introduction Sepsis is a leading cause of death. However, there is a lack of useful model to predict outcome in sepsis. Herein, the aim of this study was to develop an explainable machine learning (ML) model for predicting 28-day mortality in patients with sepsis based on Sepsis 3.0 criteria. Methods We obtained the data from the Medical Information Mart for Intensive Care (MIMIC)-III database (version 1.4). The overall data was randomly assigned to the training and testing sets at a ratio of 3:1. Following the application of LASSO regression analysis to identify the modeling variables, we proceeded to develop models using Extreme Gradient Boost (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) techniques with 5-fold cross-validation. The optimal model was selected based on its area under the curve (AUC). Finally, the Shapley additive explanations (SHAP) method was used to interpret the optimal model. Results A total of 5,834 septic adults were enrolled, the median age was 66 years (IQR, 54-78 years) and 2,342 (40.1%) were women. After feature selection, 14 variables were included for developing model in the training set. The XGBoost model (AUC: 0.806) showed superior performance with AUC, compared with RF (AUC: 0.794), LR (AUC: 0.782) and SVM model (AUC: 0.687). SHAP summary analysis for XGBoost model showed that urine output on day 1, age, blood urea nitrogen and body mass index were the top four contributors. SHAP dependence analysis demonstrated insightful nonlinear interactive associations between factors and outcome. SHAP force analysis provided three samples for model prediction. Conclusion In conclusion, our study successfully demonstrated the efficacy of ML models in predicting 28-day mortality in sepsis patients, while highlighting the potential of the SHAP method to enhance model transparency and aid in clinical decision-making.
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Affiliation(s)
- Bihua He
- Department of Neurology, Third People's Hospital of Hubei Province, Wuhan, China
- Department of Neurology, Hubei NO. 3 People’s Hospital of Jianghan University, Wuhan, China
| | - Zheng Qiu
- Department of Neurology, Third People's Hospital of Hubei Province, Wuhan, China
- Department of Neurology, Hubei NO. 3 People’s Hospital of Jianghan University, Wuhan, China
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Chang TL, Xia H, Mahajan S, Mahajan R, Maisog J, Vattikuti S, Chow CC, Chang JC. Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death. PLoS One 2024; 19:e0302871. [PMID: 38722929 PMCID: PMC11081343 DOI: 10.1371/journal.pone.0302871] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 04/15/2024] [Indexed: 05/13/2024] Open
Abstract
We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death that focuses on discharge placement, adjusting for confounding in estimating causal local average treatment effects. We trained the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on their 2009-2011 inpatient episodes (approximately 1.2 million), and then tested the model on 2012 episodes (approximately 400 thousand). The model scored an out-of-sample AUROC of approximately 0.75 on predicting all-cause readmissions-defined using official Centers for Medicare and Medicaid Services (CMS) methodology-or death within 30-days of discharge, being competitive against XGBoost and a Bayesian deep neural network, demonstrating that one need-not sacrifice interpretability for accuracy. Crucially, as a regression model, it provides what blackboxes cannot-its exact gold-standard global interpretation, explicitly defining how the model performs its internal "reasoning" for mapping the input data features to predictions. In doing so, we identify relative risk factors and quantify the effect of discharge placement. We also show that the posthoc explainer SHAP provides explanations that are inconsistent with the ground truth model reasoning that our model readily admits.
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Affiliation(s)
- Ted L. Chang
- Sound Prediction Inc., Columbus, OH, United States of America
- Mederrata Research Inc., Columbus, OH, United States of America
| | - Hongjing Xia
- Sound Prediction Inc., Columbus, OH, United States of America
- Mederrata Research Inc., Columbus, OH, United States of America
| | - Sonya Mahajan
- Sound Prediction Inc., Columbus, OH, United States of America
- Mederrata Research Inc., Columbus, OH, United States of America
| | - Rohit Mahajan
- Sound Prediction Inc., Columbus, OH, United States of America
- Mederrata Research Inc., Columbus, OH, United States of America
| | - Joe Maisog
- Lee Health, Fort Meyers, FL, United States of America
| | - Shashaank Vattikuti
- Sleep Research Center, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
| | - Carson C. Chow
- Mederrata Research Inc., Columbus, OH, United States of America
- Laboratory of Biological Modeling, NIDDK, National Institutes of Health, Bethesda, MD, United States of America
| | - Joshua C. Chang
- Sound Prediction Inc., Columbus, OH, United States of America
- Mederrata Research Inc., Columbus, OH, United States of America
- Epidemiology and Biostatistics Section, Rehabilitation Medicine Department, The National Institutes of Health, Besthesda, MD, United States of America
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3
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Li H, Hayward J, Aguilar LS, Franc JM. Desired clinical applications of artificial intelligence in emergency medicine: A Delphi study. Am J Emerg Med 2024; 79:217-220. [PMID: 38458952 DOI: 10.1016/j.ajem.2024.02.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 02/01/2024] [Accepted: 02/08/2024] [Indexed: 03/10/2024] Open
Affiliation(s)
- Henry Li
- University of Alberta, Faculty of Medicine and Dentistry, Department of Emergency Medicine, 750 University Terrace Building, 8303-112 Street NW, Edmonton T6G 2T4, Canada.
| | - Jake Hayward
- University of Alberta, Faculty of Medicine and Dentistry, Department of Emergency Medicine, 750 University Terrace Building, 8303-112 Street NW, Edmonton T6G 2T4, Canada
| | - Leandro Solis Aguilar
- University of Alberta, Faculty of Medicine and Dentistry, Department of Biochemistry, 474 Medical Sciences Building, Edmonton T6G 2H7, Canada
| | - Jeffrey Michael Franc
- University of Alberta, Faculty of Medicine and Dentistry, Department of Emergency Medicine, 750 University Terrace Building, 8303-112 Street NW, Edmonton T6G 2T4, Canada; Università del Piemonte Orientale, Center for Research and Training in Disaster Medicine, Humanitarian Aid, and Global Health, Via Lanino 1, Novara 28100, Italy
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Marzano L, Darwich AS, Jayanth R, Sven L, Falk N, Bodeby P, Meijer S. Diagnosing an overcrowded emergency department from its Electronic Health Records. Sci Rep 2024; 14:9955. [PMID: 38688997 PMCID: PMC11061188 DOI: 10.1038/s41598-024-60888-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
Emergency department overcrowding is a complex problem that persists globally. Data of visits constitute an opportunity to understand its dynamics. However, the gap between the collected information and the real-life clinical processes, and the lack of a whole-system perspective, still constitute a relevant limitation. An analytical pipeline was developed to analyse one-year of production data following the patients that came from the ED (n = 49,938) at Uppsala University Hospital (Uppsala, Sweden) by involving clinical experts in all the steps of the analysis. The key internal issues to the ED were the high volume of generic or non-specific diagnoses from non-urgent visits, and the delayed decision regarding hospital admission caused by several imaging assessments and lack of hospital beds. Furthermore, the external pressure of high frequent re-visits of geriatric, psychiatric, and patients with unspecified diagnoses dramatically contributed to the overcrowding. Our work demonstrates that through analysis of production data of the ED patient flow and participation of clinical experts in the pipeline, it was possible to identify systemic issues and directions for solutions. A critical factor was to take a whole systems perspective, as it opened the scope to the boundary effects of inflow and outflow in the whole healthcare system.
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Affiliation(s)
- Luca Marzano
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Adam S Darwich
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Raghothama Jayanth
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | - Nina Falk
- Uppsala University Hospital, Uppsala, Sweden
| | | | - Sebastiaan Meijer
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
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Gadár L, Abonyi J. Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations. Sci Rep 2024; 14:9036. [PMID: 38641683 PMCID: PMC11031594 DOI: 10.1038/s41598-024-59690-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 04/14/2024] [Indexed: 04/21/2024] Open
Abstract
In real-world classification problems, it is important to build accurate prediction models and provide information that can improve decision-making. Decision-support tools are often based on network models, and this article uses information encoded by social networks to solve the problem of employer turnover. However, understanding the factors behind black-box prediction models can be challenging. Our question was about the predictability of employee turnover, given information from the multilayer network that describes collaborations and perceptions that assess the performance of organizations that indicate the success of cooperation. Our goal was to develop an accurate prediction procedure, preserve the interpretability of the classification, and capture the wide variety of specific reasons that explain positive cases. After a feature engineering, we identified variables with the best predictive power using decision trees and ranked them based on their added value considering their frequent co-occurrence. We applied the Random Forest using the SMOTE balancing technique for prediction. We calculated the SHAP values to identify the variables that contribute the most to individual predictions. As a last step, we clustered the sample based on SHAP values to fine-tune the explanations for quitting due to different background factors.
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Affiliation(s)
- László Gadár
- HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, Hungary.
| | - János Abonyi
- HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, Hungary
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Wang J, Sourlos N, Heuvelmans M, Prokop M, Vliegenthart R, van Ooijen P. Explainable machine learning model based on clinical factors for predicting the disappearance of indeterminate pulmonary nodules. Comput Biol Med 2024; 169:107871. [PMID: 38154157 DOI: 10.1016/j.compbiomed.2023.107871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/01/2023] [Accepted: 12/17/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND During lung cancer screening, indeterminate pulmonary nodules (IPNs) are a frequent finding. We aim to predict whether IPNs are resolving or non-resolving to reduce follow-up examinations, using machine learning (ML) models. We incorporated dedicated techniques to enhance prediction explainability. METHODS In total, 724 IPNs (size 50-500 mm3, 575 participants) from the Dutch-Belgian Randomized Lung Cancer Screening Trial were used. We implemented six ML models and 14 factors to predict nodule disappearance. Random search was applied to determine the optimal hyperparameters on the training set (579 nodules). ML models were trained using 5-fold cross-validation and tested on the test set (145 nodules). Model predictions were evaluated by utilizing the recall, precision, F1 score, and the area under the receiver operating characteristic curve (AUC). The best-performing model was used for three feature importance techniques: mean decrease in impurity (MDI), permutation feature importance (PFI), and SHAPley Additive exPlanations (SHAP). RESULTS The random forest model outperformed the other ML models with an AUC of 0.865. This model achieved a recall of 0.646, a precision of 0.816, and an F1 score of 0.721. The evaluation of feature importance achieved consistent ranking across all three methods for the most crucial factors. The MDI, PFI, and SHAP methods highlighted volume, maximum diameter, and minimum diameter as the top three factors. However, the remaining factors revealed discrepant ranking across methods. CONCLUSION ML models effectively predict IPN disappearance using participant demographics and nodule characteristics. Explainable techniques can assist clinicians in developing understandable preliminary assessments.
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Affiliation(s)
- Jingxuan Wang
- Department of Radiology, University of Groningen, University Medical Center of Groningen, Groningen, the Netherlands.
| | - Nikos Sourlos
- Department of Radiology, University of Groningen, University Medical Center of Groningen, Groningen, the Netherlands
| | - Marjolein Heuvelmans
- Department of Epidemiology, University of Groningen, University Medical Center of Groningen, Groningen, the Netherlands
| | - Mathias Prokop
- Department of Radiology, University of Groningen, University Medical Center of Groningen, Groningen, the Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center of Groningen, Groningen, the Netherlands; Data Science in Health (DASH), University of Groningen, University Medical Center of Groningen, Groningen, the Netherlands
| | - Peter van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center of Groningen, Groningen, the Netherlands; Data Science in Health (DASH), University of Groningen, University Medical Center of Groningen, Groningen, the Netherlands.
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Lee YC, Ng CJ, Hsu CC, Cheng CW, Chen SY. Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review. BMC Emerg Med 2024; 24:20. [PMID: 38287243 PMCID: PMC10826225 DOI: 10.1186/s12873-024-00939-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 01/22/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Unscheduled return visits (URVs) to emergency departments (EDs) are used to assess the quality of care in EDs. Machine learning (ML) models can incorporate a wide range of complex predictors to identify high-risk patients and reduce errors to save time and cost. However, the accuracy and practicality of such models are questionable. This review compares the predictive power of multiple ML models and examines the effects of multiple research factors on these models' performance in predicting URVs to EDs. METHODS We conducted the present scoping review by searching eight databases for data from 2010 to 2023. The criteria focused on eligible articles that used ML to predict ED return visits. The primary outcome was the predictive performances of the ML models, and results were analyzed on the basis of intervals of return visits, patient population, and research scale. RESULTS A total of 582 articles were identified through the database search, with 14 articles selected for detailed analysis. Logistic regression was the most widely used method; however, eXtreme Gradient Boosting generally exhibited superior performance. Variations in visit interval, target group, and research scale did not significantly affect the predictive power of the models. CONCLUSION This is the first study to summarize the use of ML for predicting URVs in ED patients. The development of practical ML prediction models for ED URVs is feasible, but improving the accuracy of predicting ED URVs to beyond 0.75 remains a challenge. Including multiple data sources and dimensions is key for enabling ML models to achieve high accuracy; however, such inclusion could be challenging within a limited timeframe. The application of ML models for predicting ED URVs may improve patient safety and reduce medical costs by decreasing the frequency of URVs. Further research is necessary to explore the real-world efficacy of ML models.
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Affiliation(s)
- Yi-Chih Lee
- Department of Emergency Medicine, Chang Gung Memorial Hospital and Chang Gung University, College of Medicine, Taoyuan City, 333, Taiwan
| | - Chip-Jin Ng
- Department of Emergency Medicine, Chang Gung Memorial Hospital and Chang Gung University, College of Medicine, Taoyuan City, 333, Taiwan
| | - Chun-Chuan Hsu
- Department of Emergency Medicine, Chang Gung Memorial Hospital and Chang Gung University, College of Medicine, Taoyuan City, 333, Taiwan
| | - Chien-Wei Cheng
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung and Chang Gung University, College of Medicine, No. 5 Fushing St., Gueishan Shiang, Taoyuan City, 333, Taiwan
| | - Shou-Yen Chen
- Department of Emergency Medicine, Chang Gung Memorial Hospital and Chang Gung University, College of Medicine, Taoyuan City, 333, Taiwan.
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung and Chang Gung University, College of Medicine, No. 5 Fushing St., Gueishan Shiang, Taoyuan City, 333, Taiwan.
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8
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Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107161. [PMID: 36228495 DOI: 10.1016/j.cmpb.2022.107161] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/16/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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Di Martino F, Delmastro F. Explainable AI for clinical and remote health applications: a survey on tabular and time series data. Artif Intell Rev 2022; 56:5261-5315. [PMID: 36320613 PMCID: PMC9607788 DOI: 10.1007/s10462-022-10304-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
AbstractNowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining. Explainable AI (XAI) techniques are defined to unveil the reasoning behind the system’s predictions and decisions, and they become even more critical when dealing with sensitive and personal health data. It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare. In particular, many clinical and remote health applications are based on tabular and time series data, respectively, and XAI is not commonly analysed on these data types, while computer vision and Natural Language Processing (NLP) are the reference applications. To provide an overview of XAI methods that are most suitable for tabular and time series data in the healthcare domain, this paper provides a review of the literature in the last 5 years, illustrating the type of generated explanations and the efforts provided to evaluate their relevance and quality. Specifically, we identify clinical validation, consistency assessment, objective and standardised quality evaluation, and human-centered quality assessment as key features to ensure effective explanations for the end users. Finally, we highlight the main research challenges in the field as well as the limitations of existing XAI methods.
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Duckworth C, Chmiel FP, Burns DK, Zlatev ZD, White NM, Daniels TWV, Kiuber M, Boniface MJ. Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19. Sci Rep 2021; 11:23017. [PMID: 34837021 PMCID: PMC8626460 DOI: 10.1038/s41598-021-02481-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 11/15/2021] [Indexed: 01/11/2023] Open
Abstract
A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of clinical outcomes. Despite this, clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (data drift), and so can the relationship between episode characteristics and associated clinical outcomes (concept drift). Practically this means deployed algorithms must be monitored to ensure their safety. We demonstrate how explainable machine learning can be used to monitor data drift, using the COVID-19 pandemic as a severe example. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission during an emergency department attendance. We then evaluate our model's performance on attendances occurring pre-pandemic (AUROC of 0.856 with 95%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC of 0.826 with 95%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature's SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.
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Affiliation(s)
- Christopher Duckworth
- School of Electronics and Computer Science, University of Southampton, Southampton, UK.
| | - Francis P Chmiel
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Dan K Burns
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Zlatko D Zlatev
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Neil M White
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Thomas W V Daniels
- Department of Respiratory Medicine, Minerva House, University Hospital Southampton, Southampton, UK
- School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton General Hospital, LF13A, South Academic Block, Southampton, UK
| | - Michael Kiuber
- Emergency Department, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Michael J Boniface
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
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