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Matos J, Gallifant J, Chowdhury A, Economou-Zavlanos N, Charpignon ML, Gichoya J, Celi LA, Nazer L, King H, Wong AKI. A Clinician's Guide to Understanding Bias in Critical Clinical Prediction Models. Crit Care Clin 2024; 40:827-857. [PMID: 39218488 DOI: 10.1016/j.ccc.2024.05.011] [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] [Indexed: 09/04/2024]
Abstract
This narrative review focuses on the role of clinical prediction models in supporting informed decision-making in critical care, emphasizing their 2 forms: traditional scores and artificial intelligence (AI)-based models. Acknowledging the potential for both types to embed biases, the authors underscore the importance of critical appraisal to increase our trust in models. The authors outline recommendations and critical care examples to manage risk of bias in AI models. The authors advocate for enhanced interdisciplinary training for clinicians, who are encouraged to explore various resources (books, journals, news Web sites, and social media) and events (Datathons) to deepen their understanding of risk of bias.
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Affiliation(s)
- João Matos
- University of Porto (FEUP), Porto, Portugal; Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal; Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jack Gallifant
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Critical Care, Guy's and St Thomas' NHS Trust, London, UK
| | - Anand Chowdhury
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Duke University, Durham, NC, USA
| | | | - Marie-Laure Charpignon
- Institute for Data Systems and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Judy Gichoya
- Department of Radiology, Emory University, Atlanta, GA, USA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Lama Nazer
- Department of Pharmacy, King Hussein Cancer Center, Amman, Jordan
| | - Heather King
- Durham VA Health Care System, Health Services Research and Development, Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham, NC, USA; Department of Population Health Sciences, Duke University, Durham, NC, USA; Division of General Internal Medicine, Duke University, Duke University School of Medicine, Durham, NC, USA
| | - An-Kwok Ian Wong
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Duke University, Durham, NC, USA; Department of Biostatistics and Bioinformatics, Duke University, Division of Translational Biomedical Informatics, Durham, NC, USA.
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G A, K L N, M S AS. Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications. J Crit Care 2024; 83:154815. [PMID: 38723336 DOI: 10.1016/j.jcrc.2024.154815] [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: 12/26/2023] [Revised: 04/10/2024] [Accepted: 04/13/2024] [Indexed: 08/04/2024]
Abstract
PURPOSE This study investigates the potential of machine learning (ML) algorithms in improving sepsis diagnosis and prediction, focusing on their relevance in healthcare decision-making. The primary objective is to contribute to healthcare decision-making by evaluating the performance of various supervised and unsupervised models. MATERIALS AND METHODS Through an extensive literature review, optimal ML models used in sepsis research were identified. Diverse datasets from relevant sources were employed, and rigorous evaluation metrics, including accuracy, specificity, and sensitivity, were applied. Innovative techniques were introduced, such as a Stacked Blended Ensemble Model and Skopt Optimization with Blended Ensemble, incorporating Bayesian optimization for hyperparameter tuning. RESULTS ML algorithms demonstrate efficacy in sepsis diagnosis, presenting an improved balance between specificity and sensitivity, critical for effective clinical decision-making. Classifier ensemble models show enhanced accuracy and efficiency, with novel optimization techniques contributing to improved adaptability. CONCLUSION The study emphasizes the potential benefits of ML algorithms in sepsis management, advocating for ongoing research to optimize performance and ensure ethical utilization in healthcare decision-making. Ethical considerations, interpretability, and transparency are crucial factors in implementing these algorithms in clinical practice.
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Affiliation(s)
- Anjana G
- Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
| | - Nisha K L
- Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India.
| | - Arun Sankar M S
- School of CS&IT, University College Cork, Cork, Ireland; Electronic and Communications Department, South East Technological University, Carlow, Ireland
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Wang Z, Wu H, Guo Y, Zhu L, Dai Z, Zhang H, Ma X. Development and validation of a novel prediction model for Carbapenem-resistant organism infection in a large-scale hospitalized patients. Diagn Microbiol Infect Dis 2024; 110:116415. [PMID: 38970947 DOI: 10.1016/j.diagmicrobio.2024.116415] [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: 04/07/2024] [Revised: 06/22/2024] [Accepted: 06/24/2024] [Indexed: 07/08/2024]
Abstract
Carbapenem-resistant organism (CRO) are defined as gram-negative bacteria. The lack of safe and effective antibiotics has led to an increase in incidence rate. The purpose of this study is to establish and determine a risk nomogram to predict CRO infection in hospitalized patients. Hospitalized patients' information were collected from the electronic medical record system of hospital between January 2019 and December 2022. Based on the inclusion and exclusion criteria, we identified 131390 inpatients who met the criteria for this study. For the training cohort, the area under the curves (AUC) for predicting the CRO infection was 0.935. For the validation cohort, the AUC for predicting the CRO infection was 0.937. We have developed the first novel nomogram to predict CRO infection in hospitalized patients, which is reliable and high-performance. The nomogram performs well among hospitalized patients and has good predictive ability.
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Affiliation(s)
- Zhiqiang Wang
- Department of Biostatistics, Akeso Biopharma Inc, Shanghai, China
| | - Hao Wu
- Department of Medical Infection Management, Pudong New Area People's Hospital, Shanghai, China
| | - Yunping Guo
- Department of Traditional Chinese Medicine, Pudong New Area People's Hospital, Shanghai, China
| | - Linyin Zhu
- Department of Medical Infection Management, Pudong New Area People's Hospital, Shanghai, China
| | - Zhuangqing Dai
- Department of Medical Infection Management, Pudong New Area People's Hospital, Shanghai, China
| | - Huihui Zhang
- Department of Medical Infection Management, Pudong New Area People's Hospital, Shanghai, China
| | - Xiaoting Ma
- Department of Medical Infection Management, Pudong New Area People's Hospital, Shanghai, China.
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Gao J, Lu Y, Ashrafi N, Domingo I, Alaei K, Pishgar M. Prediction of sepsis mortality in ICU patients using machine learning methods. BMC Med Inform Decis Mak 2024; 24:228. [PMID: 39152423 PMCID: PMC11328468 DOI: 10.1186/s12911-024-02630-z] [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: 04/17/2024] [Accepted: 08/05/2024] [Indexed: 08/19/2024] Open
Abstract
PROBLEM Sepsis, a life-threatening condition, accounts for the deaths of millions of people worldwide. Accurate prediction of sepsis outcomes is crucial for effective treatment and management. Previous studies have utilized machine learning for prognosis, but have limitations in feature sets and model interpretability. AIM This study aims to develop a machine learning model that enhances prediction accuracy for sepsis outcomes using a reduced set of features, thereby addressing the limitations of previous studies and enhancing model interpretability. METHODS This study analyzes intensive care patient outcomes using the MIMIC-IV database, focusing on adult sepsis cases. Employing the latest data extraction tools, such as Google BigQuery, and following stringent selection criteria, we selected 38 features in this study. This selection is also informed by a comprehensive literature review and clinical expertise. Data preprocessing included handling missing values, regrouping categorical variables, and using the Synthetic Minority Over-sampling Technique (SMOTE) to balance the data. We evaluated several machine learning models: Decision Trees, Gradient Boosting, XGBoost, LightGBM, Multilayer Perceptrons (MLP), Support Vector Machines (SVM), and Random Forest. The Sequential Halving and Classification (SHAC) algorithm was used for hyperparameter tuning, and both train-test split and cross-validation methodologies were employed for performance and computational efficiency. RESULTS The Random Forest model was the most effective, achieving an area under the receiver operating characteristic curve (AUROC) of 0.94 with a confidence interval of ±0.01. This significantly outperformed other models and set a new benchmark in the literature. The model also provided detailed insights into the importance of various clinical features, with the Sequential Organ Failure Assessment (SOFA) score and average urine output being highly predictive. SHAP (Shapley Additive Explanations) analysis further enhanced the model's interpretability, offering a clearer understanding of feature impacts. CONCLUSION This study demonstrates significant improvements in predicting sepsis outcomes using a Random Forest model, supported by advanced machine learning techniques and thorough data preprocessing. Our approach provided detailed insights into the key clinical features impacting sepsis mortality, making the model both highly accurate and interpretable. By enhancing the model's practical utility in clinical settings, we offer a valuable tool for healthcare professionals to make data-driven decisions, ultimately aiming to minimize sepsis-induced fatalities.
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Affiliation(s)
- Jiayi Gao
- Department of Industrial System Engineering, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA
| | - Yuying Lu
- Department of Industrial System Engineering, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA
| | - Negin Ashrafi
- Department of Industrial System Engineering, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA
| | - Ian Domingo
- Department of Information and Computer Science, University of California, Irvine, Inner Ring Rd, Irvine, CA, 92697, USA
| | - Kamiar Alaei
- Department of Health Science, California State University, Long Beach, 1250 Bellflower Blvd. HHS2-117, Long Beach, CA, 90840, USA
| | - Maryam Pishgar
- Department of Industrial System Engineering, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA.
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Wang Y, Gao Z, Zhang Y, Lu Z, Sun F. Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study. Intern Emerg Med 2024:10.1007/s11739-024-03732-2. [PMID: 39141286 DOI: 10.1007/s11739-024-03732-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 07/27/2024] [Indexed: 08/15/2024]
Abstract
Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsis outcomes early is vital. Despite machine learning's (ML) use in medical research, local validation within the Medical Information Mart for Intensive Care IV (MIMIC-IV) database is lacking. We aimed to devise a prognostic model, leveraging MIMIC-IV data, to predict sepsis mortality and validate it in a Chinese teaching hospital. MIMIC-IV provided patient data, split into training and internal validation sets. Four ML models logistic regression (LR), support vector machine (SVM), deep neural networks (DNN), and extreme gradient boosting (XGBoost) were employed. Shapley additive interpretation offered early and interpretable mortality predictions. Area under the ROC curve (AUROC) gaged predictive performance. Results were cross verified in a Chinese teaching hospital. The study included 27,134 sepsis patients from MIMIC-IV and 487 from China. After comparing, 52 clinical indicators were selected for ML model development. All models exhibited excellent discriminative ability. XGBoost surpassed others, with AUROC of 0.873 internally and 0.844 externally. XGBoost outperformed other ML models (LR: 0.829; SVM: 0.830; DNN: 0.837) and clinical scores (Simplified Acute Physiology Score II: 0.728; Sequential Organ Failure Assessment: 0.728; Oxford Acute Severity of Illness Score: 0.738; Glasgow Coma Scale: 0.691). XGBoost's hospital mortality prediction achieved AUROC 0.873, sensitivity 0.818, accuracy 0.777, specificity 0.768, and F1 score 0.551. We crafted an interpretable model for sepsis death risk prediction. ML algorithms surpassed traditional scores for sepsis mortality forecast. Validation in a Chinese teaching hospital echoed these findings.
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Affiliation(s)
- Yiping Wang
- Department of Emergency, The First Affiliated Hospital of WenZhou Medical University, Wenzhou, 325000, China
| | - Zhihong Gao
- Department of Computer Technology and Information Management, The First Affiliated Hospital of WenZhou Medical University, Wenzhou, 325000, China
| | - Yang Zhang
- Department of Computer Technology and Information Management, The First Affiliated Hospital of WenZhou Medical University, Wenzhou, 325000, China
| | - Zhongqiu Lu
- Department of Emergency, The First Affiliated Hospital of WenZhou Medical University, Wenzhou, 325000, China.
| | - Fangyuan Sun
- Department of Computer Technology and Information Management, The First Affiliated Hospital of WenZhou Medical University, Wenzhou, 325000, China.
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Gupta A, Chauhan R, G S, Shreekumar A. Improving sepsis prediction in intensive care with SepsisAI: A clinical decision support system with a focus on minimizing false alarms. PLOS DIGITAL HEALTH 2024; 3:e0000569. [PMID: 39133661 PMCID: PMC11318852 DOI: 10.1371/journal.pdig.0000569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 07/01/2024] [Indexed: 08/15/2024]
Abstract
Prediction of sepsis using machine-learning approaches has recently gained traction. However, the lack of translation of these algorithms into clinical routine remains a major issue. Existing early sepsis detection methods are either based on the older definition of sepsis or do not accurately detect sepsis leading to the high frequency of false-positive alarms. This results in a well-known issue of clinicians' "alarm fatigue", leading to decreased responsiveness and identification, ultimately resulting in delayed clinical intervention. Hence, there is a fundamental, unmet need for a clinical decision system capable of accurate and timely sepsis diagnosis, running at the point of need. In this work, SepsisAI-a deep-learning algorithm based on long short-term memory (LSTM) networks was developed to predict the early onset of hospital-acquired sepsis in real-time for patients admitted to the ICU. The models are trained and validated with data from the PhysioNet Challenge, consisting of 40,336 patient data files from two healthcare systems: Beth Israel Deaconess Medical Center and Emory University Hospital. In the short term, the algorithm tracks frequently measured vital signs, sparsely available lab parameters, demographic features, and certain derived features for making predictions. A real-time alert system, which monitors the trajectory of the predictions, is developed on top of the deep-learning framework to minimize false alarms. On a balanced test dataset, the model achieves an AUROC, AUPRC, sensitivity, and specificity of 0.95, 0.96, 88.19%, and 96.75%, respectively at the patient level. In terms of lookahead time, the model issues a warning at a median of 6 hours (IQR 6 to 20 hours) and raises an alert at a median of 4 hours (IQR 2 to 5 hours) ahead of sepsis onset. Most importantly, the model achieves a false-alarm ratio of 3.18% for alerts, which is significantly less than other sepsis alarm systems. Additionally, on a disease prevalence-based test set, the algorithm reported similar outcomes with AUROC and AUPRC of 0.94 and 0.87, respectively, with sensitivity, and specificity of 97.05%, and 96.75%, respectively. The proposed algorithm might serve as a clinical decision support system to assist clinicians in the accurate and timely diagnosis of sepsis. With exceptionally high specificity and low false-alarm rate, this algorithm also helps mitigate the well-known issue of clinician alert fatigue arising from currently proposed sepsis alarm systems. Consequently, the algorithm partially addresses the challenges of successfully integrating machine-learning algorithms into routine clinical care.
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Affiliation(s)
- Ankit Gupta
- Center for Innovation in Diagnostics, Siemens Healthcare Private Limited, Bangalore, India
| | - Ruchi Chauhan
- Center for Innovation in Diagnostics, Siemens Healthcare Private Limited, Bangalore, India
| | - Saravanan G
- Center for Innovation in Diagnostics, Siemens Healthcare Private Limited, Bangalore, India
| | - Ananth Shreekumar
- Center for Innovation in Diagnostics, Siemens Healthcare Private Limited, Bangalore, India
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Zhang H, Wang C, Yang N. Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis. Technol Health Care 2024:THC240087. [PMID: 38968031 DOI: 10.3233/thc-240087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2024]
Abstract
BACKGROUND Early identification of sepsis has been shown to significantly improve patient prognosis. OBJECTIVE Therefore, the aim of this meta-analysis is to systematically evaluate the diagnostic efficacy of machine-learning algorithms for sepsis prediction. METHODS Systematic searches were conducted in PubMed, Embase and Cochrane databases, covering literature up to December 2023. The keywords included machine learning, sepsis and prediction. After screening, data were extracted and analysed from studies meeting the inclusion criteria. Key evaluation metrics included sensitivity, specificity and the area under the curve (AUC) for diagnostic accuracy. RESULTS The meta-analysis included a total of 21 studies with a data sample size of 4,158,941. Overall, the pooled sensitivity was 0.82 (95% confidence interval [CI] = 0.70-0.90; P< 0.001; I2=99.7%), the specificity was 0.91 (95% CI = 0.86-0.94; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.91-0.96). The subgroup analysis revealed that in the emergency department setting (6 studies), the pooled sensitivity was 0.79 (95% CI = 0.68-0.87; P< 0.001; I2= 99.6%), the specificity was 0.94 (95% CI 0.90-0.97; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.92-0.96). In the Intensive Care Unit setting (11 studies), the sensitivity was 0.91 (95% CI = 0.75-0.97; P< 0.001; I2= 98.3%), the specificity was 0.85 (95% CI = 0.75-0.92; P< 0.001; I2= 99.9%), and the AUC was 0.93 (95% CI = 0.91-0.95). Due to the limited number of studies in the in-hospital and mixed settings (n< 3), no pooled analysis was performed. CONCLUSION Machine-learning algorithms have demonstrated excellent diagnostic accuracy in predicting the occurrence of sepsis, showing potential for clinical application.
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Pérez-Tome JC, Parrón-Carreño T, Castaño-Fernández AB, Nievas-Soriano BJ, Castro-Luna G. Sepsis mortality prediction with Machine Learning Tecniques. Med Intensiva 2024:S2173-5727(24)00131-0. [PMID: 38876921 DOI: 10.1016/j.medine.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/30/2024] [Indexed: 06/16/2024]
Abstract
OBJECTIVE To develop a sepsis death classification model based on machine learning techniques for patients admitted to the Intensive Care Unit (ICU). DESIGN Cross-sectional descriptive study. SETTING The Intensive Care Units (ICUs) of three Hospitals from Murcia (Spain) and patients from the MIMIC III open-access database. PATIENTS 180 patients diagnosed with sepsis in the ICUs of three hospitals and a total of 4559 patients from the MIMIC III database. MAIN VARIABLES OF INTEREST Age, weight, heart rate, respiratory rate, temperature, lactate levels, partial oxygen saturation, systolic and diastolic blood pressure, pH, urine, and potassium levels. RESULTS A random forest classification model was calculated using the local and MIMIC III databases. The sensitivity of the model of our database, considering all the variables classified as important by the random forest, was 95.45%, the specificity was 100%, the accuracy was 96.77%, and an AUC of 95%. . In the case of the model based on the MIMIC III database, the sensitivity was 97.55%, the specificity was 100%, and the precision was 98.28%, with an AUC of 97.3%. CONCLUSIONS According to random forest classification in both databases, lactate levels, urine output and variables related to acid.base equilibrium were the most important variable in mortality due to sepsis in the ICU. The potassium levels were more critical in the MIMIC III database than the local database.
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Affiliation(s)
| | - Tesifón Parrón-Carreño
- Department of Nursing: Physiotherapy and Medicine, University of Almeria, 04120 Almeria, Spain
| | | | | | - Gracia Castro-Luna
- Department of Nursing: Physiotherapy and Medicine, University of Almeria, 04120 Almeria, Spain.
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Peterson KS, Chapman AB, Widanagamaachchi W, Sutton J, Ochoa B, Jones BE, Stevens V, Classen DC, Jones MM. Automating detection of diagnostic error of infectious diseases using machine learning. PLOS DIGITAL HEALTH 2024; 3:e0000528. [PMID: 38848317 PMCID: PMC11161023 DOI: 10.1371/journal.pdig.0000528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/07/2024] [Indexed: 06/09/2024]
Abstract
Diagnostic error, a cause of substantial morbidity and mortality, is largely discovered and evaluated through self-report and manual review, which is costly and not suitable to real-time intervention. Opportunities exist to leverage electronic health record data for automated detection of potential misdiagnosis, executed at scale and generalized across diseases. We propose a novel automated approach to identifying diagnostic divergence considering both diagnosis and risk of mortality. Our objective was to identify cases of emergency department infectious disease misdiagnoses by measuring the deviation between predicted diagnosis and documented diagnosis, weighted by mortality. Two machine learning models were trained for prediction of infectious disease and mortality using the first 24h of data. Charts were manually reviewed by clinicians to determine whether there could have been a more correct or timely diagnosis. The proposed approach was validated against manual reviews and compared using the Spearman rank correlation. We analyzed 6.5 million ED visits and over 700 million associated clinical features from over one hundred emergency departments. The testing set performances of the infectious disease (Macro F1 = 86.7, AUROC 90.6 to 94.7) and mortality model (Macro F1 = 97.6, AUROC 89.1 to 89.1) were in expected ranges. Human reviews and the proposed automated metric demonstrated positive correlations ranging from 0.231 to 0.358. The proposed approach for diagnostic deviation shows promise as a potential tool for clinicians to find diagnostic errors. Given the vast number of clinical features used in this analysis, further improvements likely need to either take greater account of data structure (what occurs before when) or involve natural language processing. Further work is needed to explain the potential reasons for divergence and to refine and validate the approach for implementation in real-world settings.
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Affiliation(s)
- Kelly S. Peterson
- Veterans Health Administration, Office of Analytics and Performance Integration, Washington D.C., District of Columbia, United States of America
- Department of Internal Medicine, Division of Epidemiology, University of Utah, Salt Lake City, Utah, United States of America
| | - Alec B. Chapman
- Department of Internal Medicine, Division of Epidemiology, University of Utah, Salt Lake City, Utah, United States of America
- Veterans Affairs Health Care System, Salt Lake City, Utah, United States of America
| | - Wathsala Widanagamaachchi
- Department of Internal Medicine, Division of Epidemiology, University of Utah, Salt Lake City, Utah, United States of America
- Veterans Affairs Health Care System, Salt Lake City, Utah, United States of America
| | - Jesse Sutton
- Veterans Affairs Health Care System, Minneapolis, Minnesota, United States of America
| | - Brennan Ochoa
- Rocky Mountain Infectious Diseases Specialists, Aurora, Colorado, United States of America
| | - Barbara E. Jones
- Veterans Affairs Health Care System, Salt Lake City, Utah, United States of America
- Division of Pulmonary & Critical Care Medicine, University of Utah, Salt Lake City, Utah, United States of America
| | - Vanessa Stevens
- Veterans Health Administration, Office of Analytics and Performance Integration, Washington D.C., District of Columbia, United States of America
- Department of Internal Medicine, Division of Epidemiology, University of Utah, Salt Lake City, Utah, United States of America
| | - David C. Classen
- Department of Internal Medicine, Division of Epidemiology, University of Utah, Salt Lake City, Utah, United States of America
| | - Makoto M. Jones
- Veterans Health Administration, Office of Analytics and Performance Integration, Washington D.C., District of Columbia, United States of America
- Department of Internal Medicine, Division of Epidemiology, University of Utah, Salt Lake City, Utah, United States of America
- Veterans Affairs Health Care System, Salt Lake City, Utah, United States of America
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Lin S, Yang M, Liu C, Wang Z, Long X. A pretrain-finetune approach for improving model generalizability in outcome prediction of acute respiratory distress syndrome patients. Int J Med Inform 2024; 186:105397. [PMID: 38507979 DOI: 10.1016/j.ijmedinf.2024.105397] [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: 10/03/2023] [Revised: 12/20/2023] [Accepted: 02/25/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Early prediction of acute respiratory distress syndrome (ARDS) of critically ill patients in intensive care units (ICUs) has been intensively studied in the past years. Yet a prediction model trained on data from one hospital might not be well generalized to other hospitals. It is therefore essential to develop an accurate and generalizable ARDS prediction model adaptive to different hospital or medical centers. METHODS We analyzed electronic medical records of 200,859 and 50,920 hospitalized patients within 24 h after being diagnosed with ARDS from the Philips eICU Institute (eICU-CRD) and the Medical Information Mart for Intensive Care (MIMIC-IV) dataset, respectively. Patients were sorted into three groups, including rapid death, long stay, and recovery, based on their condition or outcome between 24 and 72 h after ARDS diagnosis. To improve prediction performance and generalizability, a "pretrain-finetune" approach was applied, where we pretrained models on the eICU-CRD dataset and performed model finetuning using only a part (35%) of the MIMIC-IV dataset, and then tested the finetuned models on the remaining data from the MIMIC-IV dataset. Well-known machine-learning algorithms, including logistic regression, random forest, extreme gradient boosting, and multilayer perceptron neural networks, were employed to predict ARDS outcomes. Prediction performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS Results show that, in general, multilayer perceptron neural networks outperformed the other models. The use of pretrain-finetune yielded improved performance in predicting ARDS outcomes achieving a micro-AUC of 0.870 for the MIMIC-IV dataset, an improvement of 0.046 over the pretrain model. CONCLUSIONS The proposed pretrain-finetune approach can effectively improve model generalizability from one to another dataset in ARDS prediction.
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Affiliation(s)
- Songlu Lin
- Instrument Science and Electrical Engineering, Jilin University, Changchun, China; Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands
| | - Meicheng Yang
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands; State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Chengyu Liu
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Zhihong Wang
- Instrument Science and Electrical Engineering, Jilin University, Changchun, China
| | - Xi Long
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics (Basel) 2024; 14:484. [PMID: 38472956 DOI: 10.3390/diagnostics14050484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/23/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
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Affiliation(s)
- Buket Baddal
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Ferdiye Taner
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
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De Backer D, Deutschman CS, Hellman J, Myatra SN, Ostermann M, Prescott HC, Talmor D, Antonelli M, Pontes Azevedo LC, Bauer SR, Kissoon N, Loeches IM, Nunnally M, Tissieres P, Vieillard-Baron A, Coopersmith CM. Surviving Sepsis Campaign Research Priorities 2023. Crit Care Med 2024; 52:268-296. [PMID: 38240508 DOI: 10.1097/ccm.0000000000006135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
OBJECTIVES To identify research priorities in the management, epidemiology, outcome, and pathophysiology of sepsis and septic shock. DESIGN Shortly after publication of the most recent Surviving Sepsis Campaign Guidelines, the Surviving Sepsis Research Committee, a multiprofessional group of 16 international experts representing the European Society of Intensive Care Medicine and the Society of Critical Care Medicine, convened virtually and iteratively developed the article and recommendations, which represents an update from the 2018 Surviving Sepsis Campaign Research Priorities. METHODS Each task force member submitted five research questions on any sepsis-related subject. Committee members then independently ranked their top three priorities from the list generated. The highest rated clinical and basic science questions were developed into the current article. RESULTS A total of 81 questions were submitted. After merging similar questions, there were 34 clinical and ten basic science research questions submitted for voting. The five top clinical priorities were as follows: 1) what is the best strategy for screening and identification of patients with sepsis, and can predictive modeling assist in real-time recognition of sepsis? 2) what causes organ injury and dysfunction in sepsis, how should it be defined, and how can it be detected? 3) how should fluid resuscitation be individualized initially and beyond? 4) what is the best vasopressor approach for treating the different phases of septic shock? and 5) can a personalized/precision medicine approach identify optimal therapies to improve patient outcomes? The five top basic science priorities were as follows: 1) How can we improve animal models so that they more closely resemble sepsis in humans? 2) What outcome variables maximize correlations between human sepsis and animal models and are therefore most appropriate to use in both? 3) How does sepsis affect the brain, and how do sepsis-induced brain alterations contribute to organ dysfunction? How does sepsis affect interactions between neural, endocrine, and immune systems? 4) How does the microbiome affect sepsis pathobiology? 5) How do genetics and epigenetics influence the development of sepsis, the course of sepsis and the response to treatments for sepsis? CONCLUSIONS Knowledge advances in multiple clinical domains have been incorporated in progressive iterations of the Surviving Sepsis Campaign guidelines, allowing for evidence-based recommendations for short- and long-term management of sepsis. However, the strength of existing evidence is modest with significant knowledge gaps and mortality from sepsis remains high. The priorities identified represent a roadmap for research in sepsis and septic shock.
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Affiliation(s)
- Daniel De Backer
- Department of Intensive Care, CHIREC Hospitals, Université Libre de Bruxelles, Brussels, Belgium
| | - Clifford S Deutschman
- Department of Pediatrics, Cohen Children's Medical Center, Northwell Health, New Hyde Park, NY
- Sepsis Research Lab, the Feinstein Institutes for Medical Research, Manhasset, NY
| | - Judith Hellman
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, CA
| | - Sheila Nainan Myatra
- Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Marlies Ostermann
- Department of Critical Care, King's College London, Guy's & St Thomas' Hospital, London, United Kingdom
| | - Hallie C Prescott
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Daniel Talmor
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Massimo Antonelli
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario A.Gemelli IRCCS, Rome, Italy
- Istituto di Anestesiologia e Rianimazione, Università Cattolica del Sacro Cuore, Rome, Italy
| | | | - Seth R Bauer
- Department of Pharmacy, Cleveland Clinic, Cleveland, OH
| | - Niranjan Kissoon
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Ignacio-Martin Loeches
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James's Hospital, Leinster, Dublin, Ireland
| | | | - Pierre Tissieres
- Pediatric Intensive Care, Neonatal Medicine and Pediatric Emergency, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, France
| | - Antoine Vieillard-Baron
- Service de Medecine Intensive Reanimation, Hopital Ambroise Pare, Universite Paris-Saclay, Le Kremlin-Bicêtre, France
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14
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O'Reilly D, McGrath J, Martin-Loeches I. Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future. JOURNAL OF INTENSIVE MEDICINE 2024; 4:34-45. [PMID: 38263963 PMCID: PMC10800769 DOI: 10.1016/j.jointm.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 01/25/2024]
Abstract
Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.
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Affiliation(s)
- Darragh O'Reilly
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Jennifer McGrath
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Ignacio Martin-Loeches
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
- Department of Respiratory Intensive care, Hospital Clinic, Universitat de Barcelona, IDIBAPS, CIBERES, Barcelona, Spain
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15
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Popat A, Yadav S, Patel SK, Baddevolu S, Adusumilli S, Rao Dasari N, Sundarasetty M, Anand S, Sankar J, Jagtap YG. Artificial Intelligence in the Early Prediction of Cardiogenic Shock in Acute Heart Failure or Myocardial Infarction Patients: A Systematic Review and Meta-Analysis. Cureus 2023; 15:e50395. [PMID: 38213372 PMCID: PMC10783597 DOI: 10.7759/cureus.50395] [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] [Accepted: 12/12/2023] [Indexed: 01/13/2024] Open
Abstract
Cardiogenic shock (CS) may have a negative impact on mortality in patients with heart failure (HF) or acute myocardial infarction (AMI). Early prediction of CS can result in improved survival. Artificial intelligence (AI) through machine learning (ML) models have shown promise in predictive medicine. Here, we conduct a systematic review and meta-analysis to assess the effectiveness of these models in the early prediction of CS. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from the time of inception until November 2, 2023, to find relevant studies. Our outcomes were area under the curve (AUC), the sensitivity and specificity of the ML model, the accuracy of the ML model, and the predictor variables that had the most impact in predicting CS. Comprehensive Meta-Analysis (CMA) Version 3.0 was used to conduct the meta-analysis. Six studies were considered in our study. The pooled mean AUC was 0.808 (95% confidence interval: 0.727, 0.890). The AUC in the included studies ranged from 0.77 to 0.91. ML models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%-78% and 88%-93%, respectively. Age, blood pressure, heart rate, oxygen saturation, and blood glucose were the most significant variables required by ML models to acquire their outputs. In conclusion, AI has the potential for early prediction of CS, which may lead to a decrease in the high mortality rate associated with it. Future studies are needed to confirm the results.
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Affiliation(s)
- Apurva Popat
- Internal Medicine, Marshfield Clinic Health System, Marshfield, USA
| | - Sweta Yadav
- Internal Medicine, Gujarat Medical Education & Research Society (GMERS) Medical College, Ahmedabad, IND
| | - Sagar K Patel
- Internal Medicine, Gujarat Adani Institute of Medical Sciences, Bhuj, IND
| | | | | | - Nikitha Rao Dasari
- College of Medicine, Kamineni Academy of Medical Sciences and Research Centre, Hyderabad, IND
| | - Manoj Sundarasetty
- Radiodiagnosis, Bhaskar Medical College and General Hospital, Hyderabad, IND
| | - Sunethra Anand
- Internal Medicine, Chengalpattu Medical College and Hospital, Chennai, IND
| | - Jawahar Sankar
- Internal Medicine, Chengalpattu Medical College and Hospital, Chennai, IND
| | - Yugandha G Jagtap
- Paediatrics, General Medicine, Mahatma Gandhi Mission (MGM) Medical School, Mumbai, IND
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16
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Susanto AP, Lyell D, Widyantoro B, Berkovsky S, Magrabi F. Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. J Am Med Inform Assoc 2023; 30:2050-2063. [PMID: 37647865 PMCID: PMC10654852 DOI: 10.1093/jamia/ocad180] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 09/01/2023] Open
Abstract
OBJECTIVE This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings. MATERIALS AND METHODS We conducted a review in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Review). Four databases, including PubMed, Medline, Embase, and Scopus were searched for studies published from January 2016 to April 2021 evaluating the use of ML-based CDS in clinical settings. We extracted the study design, care setting, clinical task, CDS task, and ML method. The level of CDS autonomy was examined using a previously published 3-level classification based on the division of clinical tasks between the clinician and CDS; effects on decision-making, care delivery, and patient outcomes were summarized. RESULTS Thirty-two studies evaluating the use of ML-based CDS in clinical settings were identified. All were undertaken in developed countries and largely in secondary and tertiary care settings. The most common clinical tasks supported by ML-based CDS were image recognition and interpretation (n = 12) and risk assessment (n = 9). The majority of studies examined assistive CDS (n = 23) which required clinicians to confirm or approve CDS recommendations for risk assessment in sepsis and for interpreting cancerous lesions in colonoscopy. Effects on decision-making, care delivery, and patient outcomes were mixed. CONCLUSION ML-based CDS are being evaluated in many clinical areas. There remain many opportunities to apply and evaluate effects of ML-based CDS on decision-making, care delivery, and patient outcomes, particularly in resource-constrained settings.
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Affiliation(s)
- Anindya Pradipta Susanto
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
- Faculty of Medicine, Universitas Indonesia, Jakarta, DKI Jakarta 10430, Indonesia
| | - David Lyell
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
| | - Bambang Widyantoro
- Faculty of Medicine, Universitas Indonesia, Jakarta, DKI Jakarta 10430, Indonesia
- National Cardiovascular Center Harapan Kita Hospital, Jakarta, DKI Jakarta 11420, Indonesia
| | - Shlomo Berkovsky
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
| | - Farah Magrabi
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
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17
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Dou Y, Li W, Nan Y, Zhang Y, Peng S. Feature augmentation and semi-supervised conditional transfer learning for early detection of sepsis. Comput Biol Med 2023; 165:107418. [PMID: 37716243 DOI: 10.1016/j.compbiomed.2023.107418] [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: 06/07/2023] [Revised: 07/25/2023] [Accepted: 08/28/2023] [Indexed: 09/18/2023]
Abstract
Early detection of Sepsis is crucial for improving patient outcomes, as it is a significant public health concern that results in substantial morbidity and mortality. However, despite the widespread use of the Sequential Organ Failure Assessment (SOFA) in clinical settings to identify sepsis, obtaining sufficient physiological data before onset remains challenging, limiting early detection of sepsis. To address this challenge, we propose an interpretable machine learning model, ITFG (Interpretable Tree-based Feature Generation), that leverages potential correlations between features based on existing knowledge to identify sepsis within six hours of onset using valuable and continuous physiological measures. Furthermore, we introduce a Semi-supervised Attention-based Conditional Transfer Learning (SAC-TL) framework to enhance the model's generality and enable it to be used for early warning of sepsis in the target domain with less information from the source domain. Our proposed approaches effectively address the problem of systematic feature sparsity and missing data, while also being practical for different degrees of generalizability. We evaluated our proposed approaches on open datasets, MIMIC and PhysioNet, obtaining AUC of 97.98% and 86.21%, respectively, demonstrating their effectiveness in different data environments and achieving the best early detection results.
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Affiliation(s)
- Yutao Dou
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China; Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Darlington, NSW, 2008, Australia.
| | - Wei Li
- Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Darlington, NSW, 2008, Australia
| | - Yucen Nan
- Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Darlington, NSW, 2008, Australia
| | - Yidi Zhang
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.
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18
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Rojas JC, Teran M, Umscheid CA. Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated. Crit Care Clin 2023; 39:769-782. [PMID: 37704339 DOI: 10.1016/j.ccc.2023.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Predictive analytics based on artificial intelligence (AI) offer clinicians the opportunity to leverage big data available in electronic health records (EHR) to improve clinical decision-making, and thus patient outcomes. Despite this, many barriers exist to facilitating trust between clinicians and AI-based tools, limiting its current impact. Potential solutions are available at both the local and national level. It will take a broad and diverse coalition of stakeholders, from health-care systems, EHR vendors, and clinical educators to regulators, researchers and the patient community, to help facilitate this trust so that the promise of AI in health care can be realized.
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Affiliation(s)
- Juan C Rojas
- Department of Internal Medicine, Rush University, 1725 West Harrison Street, Suite 010, Chicago, IL 60612, USA.
| | - Mario Teran
- Agency for Healthcare Research and Quality, 5600 Fishers Lane, Mail Stop 06E53A, Rockville, MD 20857, USA
| | - Craig A Umscheid
- Agency for Healthcare Research and Quality, 5600 Fishers Lane, Mail Stop 06E53A, Rockville, MD 20857, USA
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19
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Abu-Khudir R, Hafsa N, Badr BE. Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning. Diagnostics (Basel) 2023; 13:3091. [PMID: 37835833 PMCID: PMC10572229 DOI: 10.3390/diagnostics13193091] [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: 06/01/2023] [Revised: 09/08/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.
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Affiliation(s)
- Rasha Abu-Khudir
- Chemistry Department, College of Science, King Faisal University, P.O. Box 380, Hofuf 31982, Al-Ahsa, Saudi Arabia
- Chemistry Department, Biochemistry Branch, Faculty of Science, Tanta University, Tanta 31527, Egypt
| | - Noor Hafsa
- Computer Science Department, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Hofuf 31982, Al-Ahsa, Saudi Arabia;
| | - Badr E. Badr
- Egyptian Ministry of Labor, Training and Research Department, Tanta 31512, Egypt;
- Botany Department, Microbiology Unit, Faculty of Science, Tanta University, Tanta 31527, Egypt
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20
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Yang Z, Cui X, Song Z. Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis. BMC Infect Dis 2023; 23:635. [PMID: 37759175 PMCID: PMC10523763 DOI: 10.1186/s12879-023-08614-0] [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: 05/21/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Sepsis is a life-threatening condition caused by an abnormal response of the body to infection and imposes a significant health and economic burden worldwide due to its high mortality rate. Early recognition of sepsis is crucial for effective treatment. This study aimed to systematically evaluate the performance of various machine learning models in predicting the onset of sepsis. METHODS We conducted a comprehensive search of the Cochrane Library, PubMed, Embase, and Web of Science databases, covering studies from database inception to November 14, 2022. We used the PROBAST tool to assess the risk of bias. We calculated the predictive performance for sepsis onset using the C-index and accuracy. We followed the PRISMA guidelines for this study. RESULTS We included 23 eligible studies with a total of 4,314,145 patients and 26 different machine learning models. The most frequently used models in the studies were random forest (n = 9), extreme gradient boost (n = 7), and logistic regression (n = 6) models. The random forest (test set n = 9, acc = 0.911) and extreme gradient boost (test set n = 7, acc = 0.957) models were the most accurate based on our analysis of the predictive performance. In terms of the C-index outcome, the random forest (n = 6, acc = 0.79) and extreme gradient boost (n = 7, acc = 0.83) models showed the highest performance. CONCLUSION Machine learning has proven to be an effective tool for predicting sepsis at an early stage. However, to obtain more accurate results, additional machine learning methods are needed. In our research, we discovered that the XGBoost and random forest models exhibited the best predictive performance and were most frequently utilized for predicting the onset of sepsis. TRIAL REGISTRATION CRD42022384015.
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Affiliation(s)
- Zhenyu Yang
- Kunming Medical University, Kunming, Yunnan, China
| | - Xiaoju Cui
- Chengyang District People's Hospital, Qingdao, Shandong, China
| | - Zhe Song
- Qinghai University, Xining, Qinghai, China.
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21
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Thapa R, Garikipati A, Ciobanu M, Singh NP, Browning E, DeCurzio J, Barnes G, Dinenno FA, Mao Q, Das R. Machine Learning Differentiation of Autism Spectrum Sub-Classifications. J Autism Dev Disord 2023:10.1007/s10803-023-06121-4. [PMID: 37751097 DOI: 10.1007/s10803-023-06121-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/19/2023] [Indexed: 09/27/2023]
Abstract
PURPOSE Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum. METHODS We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data. RESULTS The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum. CONCLUSION Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs.
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Affiliation(s)
- R Thapa
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - A Garikipati
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - M Ciobanu
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - N P Singh
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - E Browning
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - J DeCurzio
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - G Barnes
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - F A Dinenno
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
| | - Q Mao
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA.
| | - R Das
- Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA
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22
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Islam KR, Prithula J, Kumar J, Tan TL, Reaz MBI, Sumon MSI, Chowdhury MEH. Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review. J Clin Med 2023; 12:5658. [PMID: 37685724 PMCID: PMC10488449 DOI: 10.3390/jcm12175658] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/13/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predict sepsis using electronic health records (EHRs) has gained considerable attention for timely intervention. METHODS PubMed, IEEE Xplore, Google Scholar, and Scopus were searched for relevant studies. All studies that used ML/DL to detect or early-predict the onset of sepsis in the adult population using EHRs were considered. Data were extracted and analyzed from all studies that met the criteria and were also evaluated for their quality. RESULTS This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria. The chosen studies were predominantly retrospective (n = 38) and spanned diverse geographic settings, with a focus on the United States. Different datasets, sepsis definitions, and prevalence rates were employed, necessitating data augmentation. Heterogeneous parameter utilization, diverse model distribution, and varying quality assessments were observed. Longitudinal data enabled early sepsis prediction, and quality criteria fulfillment varied, with inconsistent funding-article quality correlation. CONCLUSIONS This systematic review underscores the significance of ML/DL methods for sepsis detection and early prediction through EHR data.
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Affiliation(s)
- Khandaker Reajul Islam
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Johayra Prithula
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh
| | - Jaya Kumar
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Toh Leong Tan
- Department of Emergency Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Mamun Bin Ibne Reaz
- Department of Electrical and Electronic Engineering, Independent University, Bangladesh Bashundhara, Dhaka 1229, Bangladesh
| | - Md. Shaheenur Islam Sumon
- Department of Biomedical Engineering, Military Institute of Science and Technology (MIST), Dhaka 1216, Bangladesh
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Cheung HC, De Louche C, Komorowski M. Artificial Intelligence Applications in Space Medicine. Aerosp Med Hum Perform 2023; 94:610-622. [PMID: 37501303 DOI: 10.3357/amhp.6178.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
INTRODUCTION:During future interplanetary space missions, a number of health conditions may arise, owing to the hostile environment of space and the myriad of stressors experienced by the crew. When managing these conditions, crews will be required to make accurate, timely clinical decisions at a high level of autonomy, as telecommunication delays and increasing distances restrict real-time support from the ground. On Earth, artificial intelligence (AI) has proven successful in healthcare, augmenting expert clinical decision-making or enhancing medical knowledge where it is lacking. Similarly, deploying AI tools in the context of a space mission could improve crew self-reliance and healthcare delivery.METHODS: We conducted a narrative review to discuss existing AI applications that could improve the prevention, recognition, evaluation, and management of the most mission-critical conditions, including psychological and mental health, acute radiation sickness, surgical emergencies, spaceflight-associated neuro-ocular syndrome, infections, and cardiovascular deconditioning.RESULTS: Some examples of the applications we identified include AI chatbots designed to prevent and mitigate psychological and mental health conditions, automated medical imaging analysis, and closed-loop systems for hemodynamic optimization. We also discuss at length gaps in current technologies, as well as the key challenges and limitations of developing and deploying AI for space medicine to inform future research and innovation. Indeed, shifts in patient cohorts, space-induced physiological changes, limited size and breadth of space biomedical datasets, and changes in disease characteristics may render the models invalid when transferred from ground settings into space.Cheung HC, De Louche C, Komorowski M. Artificial intelligence applications in space medicine. Aerosp Med Hum Perform. 2023; 94(8):610-622.
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Valik JK, Ward L, Tanushi H, Johansson AF, Färnert A, Mogensen ML, Pickering BW, Herasevich V, Dalianis H, Henriksson A, Nauclér P. Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data. Sci Rep 2023; 13:11760. [PMID: 37474597 PMCID: PMC10359402 DOI: 10.1038/s41598-023-38858-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/16/2023] [Indexed: 07/22/2023] Open
Abstract
Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model-SepsisFinder-which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care.
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Affiliation(s)
- John Karlsson Valik
- Division of Infectious Diseases, Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden.
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.
| | - Logan Ward
- Treat Systems ApS, Aalborg, Denmark
- Department of Health Science and Technology, Center for Model-Based Medical Decision Support, Aalborg University, Aalborg, Denmark
| | - Hideyuki Tanushi
- Division of Infectious Diseases, Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Anders F Johansson
- Department of Clinical Microbiology and the Laboratory for Molecular Infection Medicine (MIMS), Umeå University, Umeå, Sweden
| | - Anna Färnert
- Division of Infectious Diseases, Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | | | - Brian W Pickering
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA
| | - Hercules Dalianis
- Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden
| | - Aron Henriksson
- Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden
| | - Pontus Nauclér
- Division of Infectious Diseases, Department of Medicine, Karolinska Institutet, Solna, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
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Alanazi A, Aldakhil L, Aldhoayan M, Aldosari B. Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1276. [PMID: 37512087 PMCID: PMC10385427 DOI: 10.3390/medicina59071276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/26/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023]
Abstract
Background and Objectives: Early detection of sepsis is crucial and can save lives. However, identifying sepsis early and accurately remains a difficult task in the medical field. This study aims to investigate a new machine-learning approach. By analyzing the clinical laboratory results and vital signs of adult patients in the ICU, this approach can predict and detect the initial signs of sepsis. Materials and Methods: To examine survival rates and predict outcomes, the study utilized several models, including the proportional hazards model and data mining algorithms. We analyzed data from the BESTCare database at KAMC, with a focus on patients aged 14 and older who were admitted to the ICU between April and October 2018. We conducted a thorough analysis of the medical records of a total of 1182 patients who were diagnosed with sepsis. Results: We studied two approaches to predict sepsis in ICU patients. The regression model utilizing survival analysis showed moderate predictive ability, emphasizing the importance of only three factors-time (from sepsis to an outcome; discharge or death), lactic acid, and temperature-had a significant p-value (p = 0.000568, p = 0.01, p = 0.02, respectively). Other data mining algorithms may have limitations due to their assumptions of variable independence and linear classification nature. Conclusions: To achieve progress and accuracy in the field of sepsis prediction, it is important to continuously strive for improvement. By meticulously cleaning and selecting data attributes, we can create a strong foundation for future advancements in this area.
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Affiliation(s)
- Abdullah Alanazi
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Lujain Aldakhil
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Mohammed Aldhoayan
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
| | - Bakheet Aldosari
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia
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Chen C, Wu X, Zhang W, Pu Y, Xu X, Sun Y, Fei Y, Zhou S, Fang B. Predictive value of risk factors for prognosis of patients with sepsis in intensive care unit. Medicine (Baltimore) 2023; 102:e33881. [PMID: 37335653 DOI: 10.1097/md.0000000000033881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/21/2023] Open
Abstract
Sepsis has emerged as a major global public health concern due to its elevated mortality and high cost of care. This study aimed to evaluate the risk factors associated with the mortality of sepsis patients in the Intensive Care Unit (ICU), and to intervene in the early stages of sepsis in order to improve patient outcomes and reduce mortality. From January 1st, 2021 to December 31st, 2021, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Huashan Hospital Affiliated to Fudan University, and The Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine were designated as sentinel hospitals, and sepsis patients in their respective ICU and Emergency ICU were selected as research subjects, and divided into survivors and non-survivors according to their discharge outcomes. The mortality risk of sepsis patients was subsequently analyzed by logistic regression. A total of 176 patients with sepsis were included, of which 130 (73.9%) were survivors and 46 (26.1%) were non-survivors. Factors identified as having an impact on death among sepsis patients included female [Odds Ratio (OR) = 5.135, 95% confidence interval (CI): 1.709, 15.427, P = .004)], cardiovascular disease (OR = 6.272, 95% CI: 1.828, 21.518, P = .004), cerebrovascular disease (OR = 3.133, 95% CI: 1.093, 8.981, P = .034), pulmonary infections (OR = 6.700, 95% CI: 1.744, 25.748, P = .006), use of vasopressors (OR = 34.085, 95% CI: 10.452, 111.155, P < .001), WBC < 3.5 × 109/L (OR = 9.752, 95% CI: 1.386, 68.620, P = .022), ALT < 7 U/L (OR = 7.672, 95% CI: 1.263, 46.594, P = .027), ALT > 40 U/L (OR = 3.343, 95% CI: 1.097, 10.185, P = .034). Gender, cardiovascular disease, cerebrovascular disease, pulmonary infections, the use of vasopressors, WBC, and ALT are important factors in evaluating the prognostic outcome of sepsis patients in the ICU. This suggests that medical professionals should recognize them expeditiously and implement aggressive treatment tactics to diminish the mortality rate and improve outcomes.
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Affiliation(s)
- Caiyu Chen
- Department of Emergency, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Xinxin Wu
- Department of Emergency, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Wen Zhang
- Department of Emergency, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Yuting Pu
- Department of Emergency, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Xiangru Xu
- Department of Emergency, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Yuting Sun
- Department of Emergency, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Yuerong Fei
- Department of Emergency, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Shuang Zhou
- Acupuncture and Massage College, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Bangjiang Fang
- Department of Emergency, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
- Institute of Emergency and Critical Care Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
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Stonko DP, Weller JH, Gonzalez Salazar AJ, Abdou H, Edwards J, Hinson J, Levin S, Byrne JP, Sakran JV, Hicks CW, Haut ER, Morrison JJ, Kent AJ. A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay. Surg Innov 2023; 30:356-365. [PMID: 36397721 PMCID: PMC10188661 DOI: 10.1177/15533506221139965] [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] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Trauma patients have diverse resource needs due to variable mechanisms and injury patterns. The aim of this study was to build a tool that uses only data available at time of admission to predict prolonged hospital length of stay (LOS). METHODS Data was collected from the trauma registry at an urban level one adult trauma center and included patients from 1/1/2014 to 3/31/2019. Trauma patients with one or fewer days LOS were excluded. Single layer and deep artificial neural networks were trained to identify patients in the top quartile of LOS and optimized on area under the receiver operator characteristic curve (AUROC). The predictive performance of the model was assessed on a separate test set using binary classification measures of accuracy, precision, and error. RESULTS 2953 admitted trauma patients with more than one-day LOS were included in this study. They were 70% male, 60% white, and averaged 47 years-old (SD: 21). 28% were penetrating trauma. Median length of stay was 5 days (IQR 3-9). For prediction of prolonged LOS, the deep neural network achieved an AUROC of 0.80 (95% CI: 0.786-0.814) specificity was 0.95, sensitivity was 0.32, with an overall accuracy of 0.79. CONCLUSION Machine learning can predict, with excellent specificity, trauma patients who will have prolonged length of stay with only physiologic and demographic data available at the time of admission. These patients may benefit from additional resources with respect to disposition planning at the time of admission.
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Affiliation(s)
- David P. Stonko
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
- R. Adams Cowley Shock Trauma Center, Baltimore, MD, USA
| | - Jennine H. Weller
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
| | - Andres J. Gonzalez Salazar
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
| | - Hossam Abdou
- R. Adams Cowley Shock Trauma Center, Baltimore, MD, USA
| | | | - Jeremiah Hinson
- Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Scott Levin
- Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - James P. Byrne
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
| | - Joseph V. Sakran
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
| | - Caitlin W. Hicks
- Division of Vascular and Endovascular Therapy, The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Elliott R. Haut
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Armstrong Institute for Patient Safety and Quality, Johns Hopkins Medicine, Baltimore, MD, USA
- Department of Health Policy and Management, Bloomberg School of Public Health, The Johns Hopkins Baltimore, MD, USA
| | | | - Alistair J. Kent
- Division of Trauma and Acute Care Surgery, The Johns Hopkins Hospital, The Johns Hopkins Department of Surgery, Baltimore, MD, USA
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Raven W, de Hond A, Bouma LM, Mulder L, de Groot B. Does machine learning combined with clinical judgment outperform clinical judgment alone in predicting in-hospital mortality in old and young suspected infection emergency department patients? Eur J Emerg Med 2023; 30:205-206. [PMID: 37103898 DOI: 10.1097/mej.0000000000000996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Affiliation(s)
| | - Anne de Hond
- Clinical AI Implementation and Research Lab, Department of Information Technology and Digital Innovation
- Department of Information Technology and Digital Innovation
| | - Lisa-Milou Bouma
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef, RC, Leiden
| | - Leandra Mulder
- Department of Information Technology and Digital Innovation
| | - Bas de Groot
- Department of Emergency Medicine
- Department of Emergency Medicine, Radboud University Medical Centre, Geert Grooteplein-Zuid, Nijmegen, The Netherlands
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van der Vegt AH, Scott IA, Dermawan K, Schnetler RJ, Kalke VR, Lane PJ. Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework. J Am Med Inform Assoc 2023:7161075. [PMID: 37172264 DOI: 10.1093/jamia/ocad075] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 04/04/2023] [Accepted: 04/23/2023] [Indexed: 05/14/2023] Open
Abstract
OBJECTIVE To retrieve and appraise studies of deployed artificial intelligence (AI)-based sepsis prediction algorithms using systematic methods, identify implementation barriers, enablers, and key decisions and then map these to a novel end-to-end clinical AI implementation framework. MATERIALS AND METHODS Systematically review studies of clinically applied AI-based sepsis prediction algorithms in regard to methodological quality, deployment and evaluation methods, and outcomes. Identify contextual factors that influence implementation and map these factors to the SALIENT implementation framework. RESULTS The review identified 30 articles of algorithms applied in adult hospital settings, with 5 studies reporting significantly decreased mortality post-implementation. Eight groups of algorithms were identified, each sharing a common algorithm. We identified 14 barriers, 26 enablers, and 22 decision points which were able to be mapped to the 5 stages of the SALIENT implementation framework. DISCUSSION Empirical studies of deployed sepsis prediction algorithms demonstrate their potential for improving care and reducing mortality but reveal persisting gaps in existing implementation guidance. In the examined publications, key decision points reflecting real-word implementation experience could be mapped to the SALIENT framework and, as these decision points appear to be AI-task agnostic, this framework may also be applicable to non-sepsis algorithms. The mapping clarified where and when barriers, enablers, and key decisions arise within the end-to-end AI implementation process. CONCLUSIONS A systematic review of real-world implementation studies of sepsis prediction algorithms was used to validate an end-to-end staged implementation framework that has the ability to account for key factors that warrant attention in ensuring successful deployment, and which extends on previous AI implementation frameworks.
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Affiliation(s)
- Anton H van der Vegt
- Queensland Digital Health Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Ian A Scott
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Australia
| | - Krishna Dermawan
- Centre for Information Resilience, The University of Queensland, St Lucia, Australia
| | - Rudolf J Schnetler
- School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Australia
| | - Vikrant R Kalke
- Patient Safety and Quality, Clinical Excellence Queensland, Queensland Health, Brisbane, Australia
| | - Paul J Lane
- Safety Quality & Innovation, The Prince Charles Hospital, Queensland Health, Brisbane, Australia
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Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K, Ahmady S, Koohestani HR. The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2023; 11:e38. [PMID: 37215232 PMCID: PMC10197918 DOI: 10.22037/aaem.v11i1.1974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Introduction Artificial Inteligence (AI) application in emergency medicine is subject to ethical and legal inconsistencies. The purposes of this study were to map the extent of AI applications in emergency medicine, to identify ethical issues related to the use of AI, and to propose an ethical framework for its use. Methods A comprehensive literature collection was compiled through electronic databases/internet search engines (PubMed, Web of Science Platform, MEDLINE, Scopus, Google Scholar/Academia, and ERIC) and reference lists. We considered studies published between 1 January 2014 and 6 October 2022. Articles that did not self-classify as studies of an AI intervention, those that were not relevant to Emergency Departments (EDs), and articles that did not report outcomes or evaluations were excluded. Descriptive and thematic analyses of data extracted from the included articles were conducted. Results A total of 137 out of the 2175 citations in the original database were eligible for full-text evaluation. Of these articles, 47 were included in the scoping review and considered for theme extraction. This review covers seven main areas of AI techniques in emergency medicine: Machine Learning (ML) Algorithms (10.64%), prehospital emergency management (12.76%), triage, patient acuity and disposition of patients (19.15%), disease and condition prediction (23.40%), emergency department management (17.03%), the future impact of AI on Emergency Medical Services (EMS) (8.51%), and ethical issues (8.51%). Conclusion There has been a rapid increase in AI research in emergency medicine in recent years. Several studies have demonstrated the potential of AI in diverse contexts, particularly when improving patient outcomes through predictive modelling. According to the synthesis of studies in our review, AI-based decision-making lacks transparency. This feature makes AI decision-making opaque.
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Affiliation(s)
| | | | - Karim Qayumi
- Centre of Excellence for Simulation Education and Innovation, Department of Surgery, University of British Columbia, Vancouver, BC, Canada
| | - Soleiman Ahmady
- Department of Medical Education, Virtual School of Medical Education & Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Reza Koohestani
- Department of Nursing, Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran
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Maharjan J, Garikipati A, Dinenno FA, Ciobanu M, Barnes G, Browning E, DeCurzio J, Mao Q, Das R. Machine learning determination of applied behavioral analysis treatment plan type. Brain Inform 2023; 10:7. [PMID: 36862316 PMCID: PMC9981822 DOI: 10.1186/s40708-023-00186-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/06/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND Applied behavioral analysis (ABA) is regarded as the gold standard treatment for autism spectrum disorder (ASD) and has the potential to improve outcomes for patients with ASD. It can be delivered at different intensities, which are classified as comprehensive or focused treatment approaches. Comprehensive ABA targets multiple developmental domains and involves 20-40 h/week of treatment. Focused ABA targets individual behaviors and typically involves 10-20 h/week of treatment. Determining the appropriate treatment intensity involves patient assessment by trained therapists, however, the final determination is highly subjective and lacks a standardized approach. In our study, we examined the ability of a machine learning (ML) prediction model to classify which treatment intensity would be most suited individually for patients with ASD who are undergoing ABA treatment. METHODS Retrospective data from 359 patients diagnosed with ASD were analyzed and included in the training and testing of an ML model for predicting comprehensive or focused treatment for individuals undergoing ABA treatment. Data inputs included demographics, schooling, behavior, skills, and patient goals. A gradient-boosted tree ensemble method, XGBoost, was used to develop the prediction model, which was then compared against a standard of care comparator encompassing features specified by the Behavior Analyst Certification Board treatment guidelines. Prediction model performance was assessed via area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS The prediction model achieved excellent performance for classifying patients in the comprehensive versus focused treatment groups (AUROC: 0.895; 95% CI 0.811-0.962) and outperformed the standard of care comparator (AUROC 0.767; 95% CI 0.629-0.891). The prediction model also achieved sensitivity of 0.789, specificity of 0.808, PPV of 0.6, and NPV of 0.913. Out of 71 patients whose data were employed to test the prediction model, only 14 misclassifications occurred. A majority of misclassifications (n = 10) indicated comprehensive ABA treatment for patients that had focused ABA treatment as the ground truth, therefore still providing a therapeutic benefit. The three most important features contributing to the model's predictions were bathing ability, age, and hours per week of past ABA treatment. CONCLUSION This research demonstrates that the ML prediction model performs well to classify appropriate ABA treatment plan intensity using readily available patient data. This may aid with standardizing the process for determining appropriate ABA treatments, which can facilitate initiation of the most appropriate treatment intensity for patients with ASD and improve resource allocation.
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Affiliation(s)
- Jenish Maharjan
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Anurag Garikipati
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Frank A. Dinenno
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Madalina Ciobanu
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Gina Barnes
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Ella Browning
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Jenna DeCurzio
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Qingqing Mao
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA, PMB 89605, USA.
| | - Ritankar Das
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
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Khope SR, Elias S. Strategies of Predictive Schemes and Clinical Diagnosis for Prognosis Using MIMIC-III: A Systematic Review. Healthcare (Basel) 2023; 11:healthcare11050710. [PMID: 36900715 PMCID: PMC10001415 DOI: 10.3390/healthcare11050710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 02/18/2023] [Accepted: 02/21/2023] [Indexed: 03/05/2023] Open
Abstract
The prime purpose of the proposed study is to construct a novel predictive scheme for assisting in the prognosis of criticality using the MIMIC-III dataset. With the adoption of various analytics and advanced computing in the healthcare system, there is an increasing trend toward developing an effective prognostication mechanism. Predictive-based modeling is the best alternative to work in this direction. This paper discusses various scientific contributions using desk research methodology towards the Medical Information Mart for Intensive Care (MIMIC-III). This open-access dataset is meant to help predict patient trajectories for various purposes ranging from mortality forecasting to treatment planning. With a dominant machine learning approach in this perspective, there is a need to discover the effectiveness of existing predictive methods. The resultant outcome of this paper offers an inclusive discussion about various available predictive schemes and clinical diagnoses using MIMIC-III in order to contribute toward better information associated with its strengths and weaknesses. Therefore, the paper provides a clear visualization of existing schemes for clinical diagnosis using a systematic review approach.
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Strickler EAT, Thomas J, Thomas JP, Benjamin B, Shamsuddin R. Exploring a global interpretation mechanism for deep learning networks when predicting sepsis. Sci Rep 2023; 13:3067. [PMID: 36810645 PMCID: PMC9945464 DOI: 10.1038/s41598-023-30091-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 02/15/2023] [Indexed: 02/24/2023] Open
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.
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Affiliation(s)
- Ethan A T Strickler
- Physics and Mathematics, East Central University, PO Box 385, Ada, OK, 74820, USA
| | - Joshua Thomas
- Department of Internal Medicine, Rush University Medical Center, 1700 W Van Buren St, 5th Floor, Chicago, IL, 60612, USA
| | - Johnson P Thomas
- Oklahoma State University, 201 Math and Science Building, Stillwater, OK, 74078, USA
| | - Bruce Benjamin
- School of Biomedical Sciences, Center for Health Sciences, 1111 W. 17th st., Tulsa, OK, 74107, USA
| | - Rittika Shamsuddin
- Oklahoma State University, 212 Math and Science Building, Stillwater, OK, 74078, USA.
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Debray TPA, Collins GS, Riley RD, Snell KIE, Van Calster B, Reitsma JB, Moons KGM. Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ 2023; 380:e071058. [PMID: 36750236 PMCID: PMC9903176 DOI: 10.1136/bmj-2022-071058] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 02/09/2023]
Affiliation(s)
- Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
- National Institute for Health and Care Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Kym I E Snell
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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Duan Y, Huo J, Chen M, Hou F, Yan G, Li S, Wang H. Early prediction of sepsis using double fusion of deep features and handcrafted features. APPL INTELL 2023; 53:1-17. [PMID: 36685641 PMCID: PMC9843111 DOI: 10.1007/s10489-022-04425-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2022] [Indexed: 01/19/2023]
Abstract
Sepsis is a life-threatening medical condition that is characterized by the dysregulated immune system response to infections, having both high morbidity and mortality rates. Early prediction of sepsis is critical to the decrease of mortality. This paper presents a novel early warning model called Double Fusion Sepsis Predictor (DFSP) for sepsis onset. DFSP is a double fusion framework that combines the benefits of early and late fusion strategies. First, a hybrid deep learning model that combines both the convolutional and recurrent neural networks to extract deep features is proposed. Second, deep features and handcrafted features, such as clinical scores, are concatenated to build the joint feature representation (early fusion). Third, several tree-based models based on joint feature representation are developed to generate the risk scores of sepsis onset that are combined with an End-to-End neural network for final sepsis detection (late fusion). To evaluate DFSP, a retrospective study was conducted, which included patients admitted to the ICUs of a hospital in Shanghai China. The results demonstrate that the DFSP outperforms state-of-the-art approaches in early sepsis prediction.
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Affiliation(s)
- Yongrui Duan
- School of Economics & Management, Tongji University, Shanghai, China
| | - Jiazhen Huo
- School of Economics & Management, Tongji University, Shanghai, China
| | - Mingzhou Chen
- School of Economics & Management, Tongji University, Shanghai, China
| | - Fenggang Hou
- Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Guoliang Yan
- Department of Geriatrics, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Shufang Li
- Emergency Department, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Haihui Wang
- Department of Geriatrics, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China
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Tang M, Mu F, Cui C, Zhao JY, Lin R, Sun KX, Guan Y, Wang JW. Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis. Front Med (Lausanne) 2023; 9:1043589. [PMID: 36714139 PMCID: PMC9878129 DOI: 10.3389/fmed.2022.1043589] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 12/23/2022] [Indexed: 01/14/2023] Open
Abstract
Background With the increasing interest of academics in the application of artificial intelligence to sepsis, thousands of papers on this field had been published in the past few decades. It is difficult for researchers to understand the themes and latest research frontiers in this field from a multi-dimensional perspective. Consequently, the purpose of this study is to analyze the relevant literature in the application of artificial intelligence to sepsis through bibliometrics software, so as to better understand the development status, study the core hotspots and future development trends of this field. Methods We collected relevant publications in the application of artificial intelligence to sepsis from the Web of Science Core Collection in 2000 to 2021. The type of publication was limited to articles and reviews, and language was limited to English. Research cooperation network, journals, cited references, keywords in this field were visually analyzed by using CiteSpace, VOSviewer, and COOC software. Results A total of 8,481 publications in the application of artificial intelligence to sepsis between 2000 and 2021 were included, involving 8,132 articles and 349 reviews. Over the past 22 years, the annual number of publications had gradually increased exponentially. The USA was the most productive country, followed by China. Harvard University, Schuetz, Philipp, and Intensive Care Medicine were the most productive institution, author, and journal, respectively. Vincent, Jl and Critical Care Medicine were the most cited author and cited journal, respectively. Several conclusions can be drawn from the analysis of the cited references, including the following: screening and identification of sepsis biomarkers, treatment and related complications of sepsis, and precise treatment of sepsis. Moreover, there were a spike in searches relating to machine learning, antibiotic resistance and accuracy based on burst detection analysis. Conclusion This study conducted a comprehensive and objective analysis of the publications on the application of artificial intelligence in sepsis. It can be predicted that precise treatment of sepsis through machine learning technology is still research hotspot in this field.
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Chen Q, Li R, Lin C, Lai C, Chen D, Qu H, Huang Y, Lu W, Tang Y, Li L. Transferability and interpretability of the sepsis prediction models in the intensive care unit. BMC Med Inform Decis Mak 2022; 22:343. [PMID: 36581881 PMCID: PMC9798724 DOI: 10.1186/s12911-022-02090-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/16/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND We aimed to develop an early warning system for real-time sepsis prediction in the ICU by machine learning methods, with tools for interpretative analysis of the predictions. In particular, we focus on the deployment of the system in a target medical center with small historical samples. METHODS Light Gradient Boosting Machine (LightGBM) and multilayer perceptron (MLP) were trained on Medical Information Mart for Intensive Care (MIMIC-III) dataset and then finetuned on the private Historical Database of local Ruijin Hospital (HDRJH) using transfer learning technique. The Shapley Additive Explanations (SHAP) analysis was employed to characterize the feature importance in the prediction inference. Ultimately, the performance of the sepsis prediction system was further evaluated in the real-world study in the ICU of the target Ruijin Hospital. RESULTS The datasets comprised 6891 patients from MIMIC-III, 453 from HDRJH, and 67 from Ruijin real-world data. The area under the receiver operating characteristic curves (AUCs) for LightGBM and MLP models derived from MIMIC-III were 0.98 - 0.98 and 0.95 - 0.96 respectively on MIMIC-III dataset, and, in comparison, 0.82 - 0.86 and 0.84 - 0.87 respectively on HDRJH, from 1 to 5 h preceding. After transfer learning and ensemble learning, the AUCs of the final ensemble model were enhanced to 0.94 - 0.94 on HDRJH and to 0.86 - 0.9 in the real-world study in the ICU of the target Ruijin Hospital. In addition, the SHAP analysis illustrated the importance of age, antibiotics, net balance, and ventilation for sepsis prediction, making the model interpretable. CONCLUSIONS Our machine learning model allows accurate real-time prediction of sepsis within 5-h preceding. Transfer learning can effectively improve the feasibility to deploy the prediction model in the target cohort, and ameliorate the model performance for external validation. SHAP analysis indicates that the role of antibiotic usage and fluid management needs further investigation. We argue that our system and methodology have the potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention. TRIAL REGISTRATION NCT05088850 (retrospectively registered).
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Affiliation(s)
- Qiyu Chen
- Department of Applied Mathematics, School of Mathematical Sciences, Fudan University, Shanghai, 200433, China
| | - Ranran Li
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China
| | - ChihChe Lin
- Shanghai Electric Group Co., Ltd., Central Academe, Shanghai, China
| | - Chiming Lai
- Shanghai Electric Group Co., Ltd., Central Academe, Shanghai, China
| | - Dechang Chen
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China
| | - Hongping Qu
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China
| | - Yaling Huang
- Shanghai Electric Group Co., Ltd., Central Academe, Shanghai, China
| | - Wenlian Lu
- Department of Applied Mathematics, School of Mathematical Sciences, Fudan University, Shanghai, 200433, China.
| | - Yaoqing Tang
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
| | - Lei Li
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
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Komorowski M, Green A, Tatham KC, Seymour C, Antcliffe D. Sepsis biomarkers and diagnostic tools with a focus on machine learning. EBioMedicine 2022; 86:104394. [PMID: 36470834 PMCID: PMC9783125 DOI: 10.1016/j.ebiom.2022.104394] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 12/04/2022] Open
Abstract
Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.
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Affiliation(s)
- Matthieu Komorowski
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom,Corresponding author.
| | - Ashleigh Green
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Kate C. Tatham
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom,Anaesthetics, Perioperative Medicine and Pain Department, Royal Marsden NHS Foundation Trust, 203 Fulham Rd, London, SW3 6JJ, United Kingdom
| | - Christopher Seymour
- Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - David Antcliffe
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom
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Rangan ES, Pathinarupothi RK, Anand KJS, Snyder MP. Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning. JAMIA Open 2022; 5:ooac080. [PMID: 36267121 PMCID: PMC9566305 DOI: 10.1093/jamiaopen/ooac080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/07/2022] [Accepted: 09/20/2022] [Indexed: 11/15/2022] Open
Abstract
Objective To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO2), and temperature (Temp), considered both independently and in all possible combinations, for early detection of sepsis. Materials and methods By extracting features interpretable by clinicians, we applied Gradient Boosted Decision Tree machine learning on a dataset of 2630 patients to build 240 models. Validation was performed on a geographically distinct dataset. Relative to onset, predictions were clocked as per 16 pairs of monitoring intervals and prediction times, and the outcomes were ranked. Results The combination of HR and Temp was found to be a minimal feature set yielding maximal predictability with area under receiver operating curve 0.94, sensitivity of 0.85, and specificity of 0.90. Whereas HR and RR each directly enhance prediction, the effects of SpO2 and Temp are significant only when combined with HR or RR. In benchmarking relative to standard methods Systemic Inflammatory Response Syndrome (SIRS), National Early Warning Score (NEWS), and quick-Sequential Organ Failure Assessment (qSOFA), Vital-SEP outperformed all 3 of them. Conclusion It can be concluded that using intensive care unit data even 2 vital signs are adequate to predict sepsis upto 6 h in advance with promising accuracy comparable to standard scoring methods and other sepsis predictive tools reported in literature. Vital-SEP can be used for fast-track prediction especially in limited resource hospital settings where laboratory based hematologic or biochemical assays may be unavailable, inaccurate, or entail clinically inordinate delays. A prospective study is essential to determine the clinical impact of the proposed sepsis prediction model and evaluate other outcomes such as mortality and duration of hospital stay.
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Affiliation(s)
- Ekanath Srihari Rangan
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | | | - Kanwaljeet J S Anand
- Division of Critical Care, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
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Zhou A, Beyah R, Kamaleswaran R. OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3595-3603. [PMID: 34699366 PMCID: PMC10975783 DOI: 10.1109/tcbb.2021.3122405] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Sepsis is a major public concern due to its high mortality, morbidity, and financial cost. There are many existing works of early sepsis prediction using different machine learning models to mitigate the outcomes brought by sepsis. In the practical scenario, the dataset grows dynamically as new patients visit the hospital. Most existing models, being "offline" models and having used retrospective observational data, cannot be updated and improved dynamically using the new observational data. Incorporating the new data to improve the offline models requires retraining the model, which is very computationally expensive. To solve the challenge mentioned above, we propose an Online Artificial Intelligence Experts Competing Framework (OnAI-Comp) for early sepsis detection using an online learning algorithm called Multi-armed Bandit. We selected several machine learning models as the artificial intelligence experts and used average regret to evaluate the performance of our model. The experimental analysis demonstrated that our model would converge to the optimal strategy in the long run. Meanwhile, our model can provide clinically interpretable predictions using existing local interpretable model-agnostic explanation technologies, which can aid clinicians in making decisions and might improve the probability of survival.
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Decision support system and outcome prediction in a cohort of patients with necrotizing soft-tissue infections. Int J Med Inform 2022; 167:104878. [DOI: 10.1016/j.ijmedinf.2022.104878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/06/2022] [Accepted: 09/19/2022] [Indexed: 11/18/2022]
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Lombardi S, Partanen P, Francia P, Calamai I, Deodati R, Luchini M, Spina R, Bocchi L. Classifying sepsis from photoplethysmography. Health Inf Sci Syst 2022; 10:30. [PMID: 36330224 PMCID: PMC9622958 DOI: 10.1007/s13755-022-00199-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022] Open
Abstract
Sepsis is a life-threatening organ dysfunction. It is caused by a dysregulated immune response to an infection and is one of the leading causes of death in the intensive care unit (ICU). Early detection and treatment of sepsis can increase the survival rate of patients. The use of devices such as the photoplethysmograph could allow the early evaluation in addition to continuous monitoring of septic patients. The aim of this study was to verify the possibility of detecting sepsis in patients from whom the photoplethysmographic signal was acquired via a pulse oximeter. In this work, we developed a deep learning-based model for sepsis identification. The model takes a single input, the photoplethysmographic signal acquired by pulse oximeter, and performs a binary classification between septic and nonseptic samples. To develop the method, we used MIMIC-III database, which contains data from ICU patients. Specifically, the selected dataset includes 85 septic subjects and 101 control subjects. The PPG signals acquired from these patients were segmented, processed and used as input for the developed model with the aim of identifying sepsis. The proposed method achieved an accuracy of 76.37% with a sensitivity of 70.95% and a specificity of 81.04% on the test set. As regards the ROC curve, the Area Under Curve reached a value of 0.842. The results of this study indicate how the plethysmographic signal can be used as a warning sign for the early detection of sepsis with the aim of reducing the time for diagnosis and therapeutic intervention. Furthermore, the proposed method is suitable for integration in continuous patient monitoring.
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Affiliation(s)
- Sara Lombardi
- Department of Information Engineering, University of Florence, Via S. Marta, 3, 50139 Florence, Italy
| | - Petri Partanen
- Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, 90570 Oulu, Finland
| | - Piergiorgio Francia
- Department of Information Engineering, University of Florence, Via S. Marta, 3, 50139 Florence, Italy
| | - Italo Calamai
- S.O.C. Anestesia e Rianimazione, Ospedale S. Giuseppe, viale Giovanni Boccaccio, 16, 50053 Empoli, Italy
| | - Rossella Deodati
- S.O.C. Anestesia e Rianimazione, Ospedale S. Giuseppe, viale Giovanni Boccaccio, 16, 50053 Empoli, Italy
| | - Marco Luchini
- S.O.C. Anestesia e Rianimazione, Ospedale S. Giuseppe, viale Giovanni Boccaccio, 16, 50053 Empoli, Italy
| | - Rosario Spina
- S.O.C. Anestesia e Rianimazione, Ospedale S. Giuseppe, viale Giovanni Boccaccio, 16, 50053 Empoli, Italy
| | - Leonardo Bocchi
- Department of Information Engineering, University of Florence, Via S. Marta, 3, 50139 Florence, Italy
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Coombes CE, Coombes KR, Fareed N. Sequences of Events from the Electronic Medical Record and the Onset of Infection. Chem Biodivers 2022; 19:e202200657. [PMID: 36216587 DOI: 10.1002/cbdv.202200657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/15/2022] [Indexed: 11/06/2022]
Abstract
We present a novel model of time-series analysis to learn from electronic health record (EHR) data when infection occurred in the intensive care unit (ICU) by translating methods from proteomics and Bayesian statistics. Using 48,536 patients hospitalized in an ICU, we describe each hospital course as an 'alphabet' of 23 physician actions ('events') in temporal order. We analyze these as k-mers of length 3-12 events and apply a Bayesian model of (cumulative) relative risk (RR). The log2-transformed RR (median=0.248, mean=0.226) supported the conclusion that the events selected were individually associated with increased risk of infection. Selecting from all possible cutoffs of maximum gain (MG), MG>0.0244 predicts administration of antibiotics with PPV 82.0 %, NPV 44.4 %, and AUC 0.706. Our approach holds value for retrospective analysis of other clinical syndromes for which time-of-onset is critical to analysis but poorly marked in EHRs, including delirium and decompensation.
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Affiliation(s)
- Caitlin E Coombes
- Department of Anesthesiology, Stanford University, 300 Pasteur Dr., Palo Alto, CA 94305, USA
| | - Kevin R Coombes
- Department of Population Health Sciences, Medical College of Georgia, 1420 Laney Walker Blvd, Augusta, GA 30912, USA
| | - Naleef Fareed
- Department of Biomedical Informatics, The Ohio State University College of Medicine, 370 W 9th Ave, Columbus, OH 43210, USA
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Kostaki A, Wacker JW, Safarika A, Solomonidi N, Katsaros K, Giannikopoulos G, Koutelidakis IM, Hogan CA, Uhle F, Liesenfeld O, Sweeney TE, Giamarellos-Bourboulis EJ. A 29-MRNA HOST RESPONSE WHOLE-BLOOD SIGNATURE IMPROVES PREDICTION OF 28-DAY MORTALITY AND 7-DAY INTENSIVE CARE UNIT CARE IN ADULTS PRESENTING TO THE EMERGENCY DEPARTMENT WITH SUSPECTED ACUTE INFECTION AND/OR SEPSIS. Shock 2022; 58:224-230. [PMID: 36125356 PMCID: PMC9512237 DOI: 10.1097/shk.0000000000001970] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 03/28/2022] [Accepted: 07/19/2022] [Indexed: 11/25/2022]
Abstract
ABSTRACT Background: Risk stratification of emergency department patients with suspected acute infections and/or suspected sepsis remains challenging. We prospectively validated a 29-messenger RNA host response classifier for predicting severity in these patients. Methods: We enrolled adults presenting with suspected acute infections and at least one vital sign abnormality to six emergency departments in Greece. Twenty-nine target host RNAs were quantified on NanoString nCounter and analyzed with the Inflammatix Severity 2 (IMX-SEV-2) classifier to determine risk scores as low, moderate, and high severity. Performance of IMX-SEV-2 for prediction of 28-day mortality was compared with that of lactate, procalcitonin, and quick sequential organ failure assessment (qSOFA). Results: A total of 397 individuals were enrolled; 38 individuals (9.6%) died within 28 days. Inflammatix Severity 2 classifier predicted 28-day mortality with an area under the receiver operator characteristics curve of 0.82 (95% confidence interval [CI], 0.74-0.90) compared with lactate, 0.66 (95% CI, 0.54-0.77); procalcitonin, 0.67 (95% CI, 0.57-0.78); and qSOFA, 0.81 (95% CI, 0.72-0.89). Combining qSOFA with IMX-SEV-2 improved prognostic accuracy from 0.81 to 0.89 (95% CI, 0.82-0.96). The high-severity (rule-in) interpretation band of IMX-SEV-2 demonstrated 96.9% specificity for predicting 28-day mortality, whereas the low-severity (rule-out) band had a sensitivity of 78.9%. Similarly, IMX-SEV-2 alone accurately predicted the need for day-7 intensive care unit care and further boosted overall accuracy when combined with qSOFA. Conclusions: Inflammatix Severity 2 classifier predicted 28-day mortality and 7-day intensive care unit care with high accuracy and boosted the accuracy of clinical scores when used in combination.
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Affiliation(s)
- Antigone Kostaki
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Greece
| | | | - Asimina Safarika
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Greece
| | - Nicky Solomonidi
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Greece
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Lian H, Zhang H, Ding X, Wang X. The importance of a sepsis layered early warning system for critical patients. Am J Transl Res 2022; 14:5229-5242. [PMID: 36105025 PMCID: PMC9452367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/12/2022] [Indexed: 06/15/2023]
Abstract
Critical illness, particularly sepsis, is associated with high mortality, so prevention is more important than effective therapy. Advances in medical science have provided more opportunities for early warning and early intervention to avoid the development of critical illness. Existing early warning systems (EWS) have the advantages of high efficiency and convenience. However, with the development of medical technology, they do not completely meet clinical needs. EWS should contain elements that meet many dimensions of clinical requirements, including risk warning, response warning, injury warning, critical warning, and death warning. By summarizing previous studies, we outlined a layered EWS that follows RISK bundles. RISK represents different warning sign categories: R: host response, I: organ injury, S: changes in vital signs, and K: gradual appearance of "killed" organs. We plan to construct a complete layered EWS to guide clinical activities and subsequent clinical studies in the near future.
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Affiliation(s)
- Hui Lian
- Department of Health Care, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730, P. R. China
| | - Hongmin Zhang
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730, P. R. China
| | - Xin Ding
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730, P. R. China
| | - Xiaoting Wang
- Department of Health Care, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730, P. R. China
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730, P. R. China
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Machine Learning Models for Early Prediction of Sepsis on Large Healthcare Datasets. ELECTRONICS 2022. [DOI: 10.3390/electronics11091507] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Sepsis is a highly lethal syndrome with heterogeneous clinical manifestation that can be hard to identify and treat. Early diagnosis and appropriate treatment are critical to reduce mortality and promote survival in suspected cases and improve the outcomes. Several screening prediction systems have been proposed for evaluating the early detection of patient deterioration, but the efficacy is still limited at individual level. The increasing amount and the versatility of healthcare data suggest implementing machine learning techniques to develop models for predicting sepsis. This work presents an experimental study of some machine-learning-based models for sepsis prediction considering vital signs, laboratory test results, and demographics using Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4), a publicly available dataset. The experimental results demonstrate an overall higher performance of machine learning models over the commonly used Sequential Organ Failure Assessment (SOFA) and Quick SOFA (qSOFA) scoring systems at the time of sepsis onset.
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Yoon HK, Yang HL, Jung CW, Lee HC. Artificial intelligence in perioperative medicine - a narrative review. Korean J Anesthesiol 2022; 75:202-215. [PMID: 35345305 PMCID: PMC9171545 DOI: 10.4097/kja.22157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/15/2022] [Indexed: 11/23/2022] Open
Abstract
Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses, and intensive care medicine have been developed in the field of perioperative medicine. Some of these models have been validated using external datasets and randomized controlled trials. Once these models are implemented in electronic health record systems or software medical devices, they could help anesthesiologists improve clinical outcomes by accurately predicting complications and suggesting optimal treatment strategies in real-time. This review provides an overview of the AI techniques used in perioperative medicine and a summary of the studies that have been published using these techniques. Understanding these techniques will aid in their appropriate application in clinical practice.
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Affiliation(s)
- Hyun-Kyu Yoon
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyun-Lim Yang
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chul-Woo Jung
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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Hong N, Liu C, Gao J, Han L, Chang F, Gong M, Su L. State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review. JMIR Med Inform 2022; 10:e28781. [PMID: 35238790 PMCID: PMC8931648 DOI: 10.2196/28781] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 07/02/2021] [Accepted: 12/01/2021] [Indexed: 12/23/2022] Open
Abstract
Background Modern clinical care in intensive care units is full of rich data, and machine learning has great potential to support clinical decision-making. The development of intelligent machine learning–based clinical decision support systems is facing great opportunities and challenges. Clinical decision support systems may directly help clinicians accurately diagnose, predict outcomes, identify risk events, or decide treatments at the point of care. Objective We aimed to review the research and application of machine learning–enabled clinical decision support studies in intensive care units to help clinicians, researchers, developers, and policy makers better understand the advantages and limitations of machine learning–supported diagnosis, outcome prediction, risk event identification, and intensive care unit point-of-care recommendations. Methods We searched papers published in the PubMed database between January 1980 and October 2020. We defined selection criteria to identify papers that focused on machine learning–enabled clinical decision support studies in intensive care units and reviewed the following aspects: research topics, study cohorts, machine learning models, analysis variables, and evaluation metrics. Results A total of 643 papers were collected, and using our selection criteria, 97 studies were found. Studies were categorized into 4 topics—monitoring, detection, and diagnosis (13/97, 13.4%), early identification of clinical events (32/97, 33.0%), outcome prediction and prognosis assessment (46/97, 47.6%), and treatment decision (6/97, 6.2%). Of the 97 papers, 82 (84.5%) studies used data from adult patients, 9 (9.3%) studies used data from pediatric patients, and 6 (6.2%) studies used data from neonates. We found that 65 (67.0%) studies used data from a single center, and 32 (33.0%) studies used a multicenter data set; 88 (90.7%) studies used supervised learning, 3 (3.1%) studies used unsupervised learning, and 6 (6.2%) studies used reinforcement learning. Clinical variable categories, starting with the most frequently used, were demographic (n=74), laboratory values (n=59), vital signs (n=55), scores (n=48), ventilation parameters (n=43), comorbidities (n=27), medications (n=18), outcome (n=14), fluid balance (n=13), nonmedicine therapy (n=10), symptoms (n=7), and medical history (n=4). The most frequently adopted evaluation metrics for clinical data modeling studies included area under the receiver operating characteristic curve (n=61), sensitivity (n=51), specificity (n=41), accuracy (n=29), and positive predictive value (n=23). Conclusions Early identification of clinical and outcome prediction and prognosis assessment contributed to approximately 80% of studies included in this review. Using new algorithms to solve intensive care unit clinical problems by developing reinforcement learning, active learning, and time-series analysis methods for clinical decision support will be greater development prospects in the future.
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Affiliation(s)
- Na Hong
- Digital Health China Technologies Ltd Co, Beijing, China
| | - Chun Liu
- Digital Health China Technologies Ltd Co, Beijing, China
| | - Jianwei Gao
- Digital Health China Technologies Ltd Co, Beijing, China
| | - Lin Han
- Digital Health China Technologies Ltd Co, Beijing, China
| | | | - Mengchun Gong
- Digital Health China Technologies Ltd Co, Beijing, China
| | - Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
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Ocampo-Quintero N, Vidal-Cortés P, Del Río Carbajo L, Fdez-Riverola F, Reboiro-Jato M, Glez-Peña D. Enhancing sepsis management through machine learning techniques: A review. Med Intensiva 2022; 46:140-156. [PMID: 35221003 DOI: 10.1016/j.medine.2020.04.015] [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: 11/29/2019] [Accepted: 04/05/2020] [Indexed: 06/14/2023]
Abstract
Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guidelines non-adherence have been proved to be associated with higher mortality. Machine Learning is increasingly being adopted in developing innovative Clinical Decision Support Systems in many areas of medicine, showing a great potential for automatic prediction of diverse patient conditions, as well as assistance in clinical decision making. In this context, this work conducts a narrative review to provide an overview of how specific Machine Learning techniques can be used to improve sepsis management, discussing the main tasks addressed, the most popular methods and techniques, as well as the obtained results, in terms of both intelligent system accuracy and clinical outcomes improvement.
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Affiliation(s)
- N Ocampo-Quintero
- ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain
| | - P Vidal-Cortés
- Intensive Care Unit, Complexo Hospitalario Universitario de Ourense, Ourense, Spain
| | - L Del Río Carbajo
- Intensive Care Unit, Complexo Hospitalario Universitario de Ourense, Ourense, Spain
| | - F Fdez-Riverola
- ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain; CINBIO - Centro de Investigaciones Biomédicas, Universidad de Vigo, Vigo, Spain; SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - M Reboiro-Jato
- ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain; CINBIO - Centro de Investigaciones Biomédicas, Universidad de Vigo, Vigo, Spain; SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain
| | - D Glez-Peña
- ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain; CINBIO - Centro de Investigaciones Biomédicas, Universidad de Vigo, Vigo, Spain; SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain.
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Machine Learning and Antibiotic Management. Antibiotics (Basel) 2022; 11:antibiotics11030304. [PMID: 35326768 PMCID: PMC8944459 DOI: 10.3390/antibiotics11030304] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 02/07/2022] [Accepted: 02/18/2022] [Indexed: 11/17/2022] Open
Abstract
Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt evaluation that an experienced clinician sometimes effortlessly, but often only after long, hard consideration and consultation with colleagues, relies upon for decision making, is what we wanted to achieve with the application of machine learning to antibiotic therapy and clinical monitoring in the present work. This is a single-center retrospective analysis proposing methods for evaluation of vitals and antimicrobial therapy in intensive care patients. For each patient included in the present study, duration of antibiotic therapy, consecutive days of treatment and type and combination of antimicrobial agents have been assessed and considered as single unique daily record for analysis. Each parameter, composing a record was normalized using a fuzzy logic approach and assigned to five descriptive categories (fuzzy domain sub-sets ranging from “very low” to “very high”). Clustering of these normalized therapy records was performed, and each patient/day was considered to be a pertaining cluster. The same methodology was used for hourly bed-side monitoring. Changes in patient conditions (monitoring) can lead to a shift of clusters. This can provide an additional tool for assessing progress of complex patients. We used Fuzzy logic normalization to descriptive categories of parameters as a form nearer to human language than raw numbers.
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