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Hu X, Zhi S, Wu W, Tao Y, Zhang Y, Li L, Li X, Pan L, Fan H, Li W. The application of metagenomics, radiomics and machine learning for diagnosis of sepsis. Front Med (Lausanne) 2024; 11:1400166. [PMID: 39371337 PMCID: PMC11449737 DOI: 10.3389/fmed.2024.1400166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 09/09/2024] [Indexed: 10/08/2024] Open
Abstract
Introduction Sepsis poses a serious threat to individual life and health. Early and accessible diagnosis and targeted treatment are crucial. This study aims to explore the relationship between microbes, metabolic pathways, and blood test indicators in sepsis patients and develop a machine learning model for clinical diagnosis. Methods Blood samples from sepsis patients were sequenced. α-diversity and β-diversity analyses were performed to compare the microbial diversity between the sepsis group and the normal group. Correlation analysis was conducted on microbes, metabolic pathways, and blood test indicators. In addition, a model was developed based on medical records and radiomic features using machine learning algorithms. Results The results of α-diversity and β-diversity analyses showed that the microbial diversity of sepsis group was significantly higher than that of normal group (p < 0.05). The top 10 microbial abundances in the sepsis and normal groups were Vitis vinifera, Mycobacterium canettii, Solanum pennellii, Ralstonia insidiosa, Ananas comosus, Moraxella osloensis, Escherichia coli, Staphylococcus hominis, Camelina sativa, and Cutibacterium acnes. The enriched metabolic pathways mainly included Protein families: genetic information processing, Translation, Protein families: signaling and cellular processes, and Unclassified: genetic information processing. The correlation analysis revealed a significant positive correlation (p < 0.05) between IL-6 and Membrane transport. Metabolism of other amino acids showed a significant positive correlation (p < 0.05) with Cutibacterium acnes, Ralstonia insidiosa, Moraxella osloensis, and Staphylococcus hominis. Ananas comosus showed a significant positive correlation (p < 0.05) with Poorly characterized and Unclassified: metabolism. Blood test-related indicators showed a significant negative correlation (p < 0.05) with microorganisms. Logistic regression (LR) was used as the optimal model in six machine learning models based on medical records and radiomic features. The nomogram, calibration curves, and AUC values demonstrated that LR performed best for prediction. Discussion This study provides insights into the relationship between microbes, metabolic pathways, and blood test indicators in sepsis. The developed machine learning model shows potential for aiding in clinical diagnosis. However, further research is needed to validate and improve the model.
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Affiliation(s)
- Xiefei Hu
- Clinical Laboratory, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing, China
| | - Shenshen Zhi
- Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing, China
- Department of Blood Transfusion, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Wenyan Wu
- Clinical Laboratory, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing, China
| | - Yang Tao
- Clinical Laboratory, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
- Intensive Care Unit, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Yuanyuan Zhang
- Clinical Laboratory, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing, China
| | - Lijuan Li
- Clinical Laboratory, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing, China
| | - Xun Li
- Clinical Laboratory, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing, China
| | - Liyan Pan
- Clinical Laboratory, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing, China
| | - Haiping Fan
- Clinical Laboratory, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing, China
| | - Wei Li
- Clinical Laboratory, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing, China
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Zhou L, Shao M, Wang C, Wang Y. An early sepsis prediction model utilizing machine learning and unbalanced data processing in a clinical context. Prev Med Rep 2024; 45:102841. [PMID: 39188971 PMCID: PMC11345914 DOI: 10.1016/j.pmedr.2024.102841] [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: 09/15/2023] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/28/2024] Open
Abstract
Background Early and accurate diagnoses of sepsis patients are essential to reduce the mortality. However, the sepsis is still diagnosed in a traditional way in China despite the increasing number of related studies, which may to some extent lead to delays in the treatment. Methods The study included 2,385 patients, including 364 with sepsis, collected from the First Affiliated Hospital of Anhui Medical University and partner hospitals from April to July 2022. External validation was conducted using the MIMIC-III database (over 60,000 patients from 2001 to 2012) and the eICU Collaborative Research Database (139,000 patients from 2014 to 2015). Multiple algorithm models, along with the SHapley Additive exPlanations (SHAP) analysis, are applied to explore the main risk factors for the accurate prediction of the sepsis. Multiple Imputations for filling missing data and the Synthetic Minority Oversampling (SMOTE) balancing method for balancing data are used for the data processing. Result Eighteen diagnostic features are used in the predictive model for early sepsis. The Random Forest model has the best performance among all the models, with an Area Under the Curve (AUC) of 87% and an F1-score (F1) of 77%. Moreover, the interpretation from the SHAP analysis is generally consistent with the current clinical situation. Conclusion The study revealed the relationship between these 18 clinical features and diagnostic outcomes. The results indicate that patients with laboratory values of Systolic Blood Pressure, Albumin, and Heart Rate exceeding certain thresholds are at a high likelihood of developing sepsis.
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Affiliation(s)
- Luyao Zhou
- School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Min Shao
- Department of Critical Care Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Cui Wang
- Department of Critical Care Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yu Wang
- School of Biomedical Engineering, Anhui Medical University, Hefei, China
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Lee JW, Lee B, Park JD. Pediatric septic shock estimation using deep learning and electronic medical records. Acute Crit Care 2024; 39:400-407. [PMID: 39266275 PMCID: PMC11392703 DOI: 10.4266/acc.2024.00031] [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: 01/05/2024] [Accepted: 05/07/2024] [Indexed: 09/14/2024] Open
Abstract
BACKGROUND Diagnosing pediatric septic shock is difficult due to the complex and often impractical traditional criteria, such as systemic inflammatory response syndrome (SIRS), which result in delays and higher risks. This study aims to develop a deep learning-based model using SIRS data for early diagnosis in pediatric septic shock cases. METHODS The study analyzed data from pediatric patients (<18 years old) admitted to a tertiary hospital from January 2010 to July 2023. Vital signs, lab tests, and clinical information were collected. Septic shock cases were identified using SIRS criteria and inotrope use. A deep learning model was trained and evaluated using the area under the receiver operating characteristics curve (AUROC) and area under the precision-recall curve (AUPRC). Variable contributions were analyzed using the Shapley additive explanation value. RESULTS The analysis, involving 9,616,115 measurements, identified 34,696 septic shock cases (0.4%). Oxygen supply was crucial for 41.5% of the control group and 20.8% of the septic shock group. The final model showed strong performance, with an AUROC of 0.927 and AUPRC of 0.879. Key influencers were age, oxygen supply, sex, and partial pressure of carbon dioxide, while body temperature had minimal impact on estimation. CONCLUSIONS The proposed deep learning model simplifies early septic shock diagnosis in pediatric patients, reducing the diagnostic workload. Its high accuracy allows timely treatment, but external validation through prospective studies is needed.
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Affiliation(s)
- Ji Weon Lee
- Integrated and Respite Care Center for Children, Seoul National University, Seoul, Korea
| | - Bongjin Lee
- Department of Pediatrics, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea
| | - June Dong Park
- Department of Pediatrics, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
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Di Sarno L, Caroselli A, Tonin G, Graglia B, Pansini V, Causio FA, Gatto A, Chiaretti A. Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives. Biomedicines 2024; 12:1220. [PMID: 38927427 PMCID: PMC11200597 DOI: 10.3390/biomedicines12061220] [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: 04/23/2024] [Revised: 05/19/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.
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Affiliation(s)
- Lorenzo Di Sarno
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
| | - Anya Caroselli
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
| | - Giovanna Tonin
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Benedetta Graglia
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
| | - Valeria Pansini
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Francesco Andrea Causio
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
- Section of Hygiene and Public Health, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Antonio Gatto
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Antonio Chiaretti
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
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Huang J, Chen J, Wang C, Lai L, Mi H, Chen S. Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model. Front Genet 2024; 15:1294381. [PMID: 38348451 PMCID: PMC10859440 DOI: 10.3389/fgene.2024.1294381] [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] [Received: 09/15/2023] [Accepted: 01/16/2024] [Indexed: 02/15/2024] Open
Abstract
Introduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but its potential involvement in PS remains largely unexplored. Methods: We first analyzed the expression patterns of cuproptosis-related genes (CRGs) and assessed the immune landscape of PS using the GSE66099 dataset. Subsequently, PS samples were isolated from the same dataset, and consensus clustering was performed based on differentially expressed CRGs. We applied weighted gene co-expression network analysis to identify hub genes associated with PS and cuproptosis. Results: We observed aberrant expression of 27 CRGs and a specific immune landscape in PS samples. Our findings revealed that patients in the GSE66099 dataset could be categorized into two cuproptosis clusters, each characterized by unique immune landscapes and varying functional classifications or enriched pathways. Among the machine learning approaches, Extreme Gradient Boosting demonstrated optimal performance as a diagnostic model for PS. Discussion: Our study provides valuable insights into the molecular mechanisms underlying PS, highlighting the involvement of cuproptosis-related genes and immune cell infiltration.
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Affiliation(s)
- Junming Huang
- Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jinji Chen
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Chengbang Wang
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Lichuan Lai
- Department of Laboratory, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Hua Mi
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Shaohua Chen
- Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
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Zhang R, Yin M, Jiang A, Zhang S, Xu X, Liu L. Automated machine learning for early prediction of acute kidney injury in acute pancreatitis. BMC Med Inform Decis Mak 2024; 24:16. [PMID: 38212745 PMCID: PMC10785491 DOI: 10.1186/s12911-024-02414-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 01/01/2024] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Acute kidney injury (AKI) represents a frequent and grave complication associated with acute pancreatitis (AP), substantially elevating both mortality rates and the financial burden of hospitalization. The aim of our study is to construct a predictive model utilizing automated machine learning (AutoML) algorithms for the early prediction of AKI in patients with AP. METHODS We retrospectively analyzed patients who were diagnosed with AP in our hospital from January 2017 to December 2021. These patients were randomly allocated into a training set and a validation set at a ratio of 7:3. To develop predictive models for each set, we employed the least absolute shrinkage and selection operator (LASSO) algorithm along with AutoML. A nomogram was developed based on multivariate logistic regression analysis outcomes. The model's efficacy was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Additionally, the performance of the model constructed via AutoML was evaluated using decision curve analysis (DCA), feature importance, SHapley Additive exPlanations (SHAP) plots, and locally interpretable model-agnostic explanations (LIME). RESULTS This study incorporated a total of 437 patients who met the inclusion criteria. Out of these, 313 were assigned to the training cohort and 124 to the validation cohort. In the training and validation cohorts, AKI occurred in 68 (21.7%) and 29(23.4%) patients, respectively. Comparative analysis revealed that the AutoML models exhibited enhanced performance over traditional logistic regression (LR). Furthermore, the deep learning (DL) model demonstrated superior predictive accuracy, evidenced by an area under the ROC curve of 0.963 in the training set and 0.830 in the validation set, surpassing other comparative models. The key variables identified as significant in the DL model within the training dataset included creatinine (Cr), urea (Urea), international normalized ratio (INR), etiology, smoking, alanine aminotransferase (ALT), hypertension, prothrombin time (PT), lactate dehydrogenase (LDH), and diabetes. CONCLUSION The AutoML model, utilizing DL algorithm, offers considerable clinical significance in the early detection of AKI among patients with AP.
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Affiliation(s)
- Rufa Zhang
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu NO.1 People's Hospital, No. 1 Shuyuan Street, 215500, Suzhou, Jiangsu, China
| | - Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Anqi Jiang
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu NO.1 People's Hospital, No. 1 Shuyuan Street, 215500, Suzhou, Jiangsu, China
| | - Shihou Zhang
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu NO.1 People's Hospital, No. 1 Shuyuan Street, 215500, Suzhou, Jiangsu, China
| | - Xiaodan Xu
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu NO.1 People's Hospital, No. 1 Shuyuan Street, 215500, Suzhou, Jiangsu, China.
| | - Luojie Liu
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu NO.1 People's Hospital, No. 1 Shuyuan Street, 215500, Suzhou, Jiangsu, China.
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Lemmon J, Guo LL, Steinberg E, Morse KE, Fleming SL, Aftandilian C, Pfohl SR, Posada JD, Shah N, Fries J, Sung L. Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks. J Am Med Inform Assoc 2023; 30:2004-2011. [PMID: 37639620 PMCID: PMC10654865 DOI: 10.1093/jamia/ocad175] [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: 06/29/2023] [Revised: 08/03/2023] [Accepted: 08/15/2023] [Indexed: 08/31/2023] Open
Abstract
OBJECTIVE Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to creating robust pediatric prediction models. The primary objective was to determine whether a self-supervised model trained in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients, for pediatric inpatient clinical prediction tasks. MATERIALS AND METHODS This retrospective cohort study used EHR data and included patients with at least one admission to an inpatient unit. One admission per patient was randomly selected. Adult inpatients were 18 years or older while pediatric inpatients were more than 28 days and less than 18 years. Admissions were temporally split into training (January 1, 2008 to December 31, 2019), validation (January 1, 2020 to December 31, 2020), and test (January 1, 2021 to August 1, 2022) sets. Primary comparison was a self-supervised model trained in adult inpatients versus count-based logistic regression models trained in pediatric inpatients. Primary outcome was mean area-under-the-receiver-operating-characteristic-curve (AUROC) for 11 distinct clinical outcomes. Models were evaluated in pediatric inpatients. RESULTS When evaluated in pediatric inpatients, mean AUROC of self-supervised model trained in adult inpatients (0.902) was noninferior to count-based logistic regression models trained in pediatric inpatients (0.868) (mean difference = 0.034, 95% CI=0.014-0.057; P < .001 for noninferiority and P = .006 for superiority). CONCLUSIONS Self-supervised learning in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients. This finding suggests transferability of self-supervised models trained in adult patients to pediatric patients, without requiring costly model retraining.
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Affiliation(s)
- Joshua Lemmon
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON M5G1X8, Canada
| | - Lin Lawrence Guo
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON M5G1X8, Canada
| | - Ethan Steinberg
- Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA 94305, United States
| | - Keith E Morse
- Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University, Palo Alto, CA 94304, United States
| | - Scott Lanyon Fleming
- Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA 94305, United States
| | - Catherine Aftandilian
- Division of Hematology/Oncology, Department of Pediatrics, Stanford University, Palo Alto, CA 94304, United States
| | | | - Jose D Posada
- Universidad del Norte, Barranquilla 081007, Colombia
| | - Nigam Shah
- Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA 94305, United States
| | - Jason Fries
- Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA 94305, United States
| | - Lillian Sung
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON M5G1X8, Canada
- Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON M5G1X8, Canada
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Ma X, Mo C, Li Y, Chen X, Gui C. Prediction of the development of contrast‑induced nephropathy following percutaneous coronary artery intervention by machine learning. Acta Cardiol 2023; 78:912-921. [PMID: 37052397 DOI: 10.1080/00015385.2023.2198937] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 03/30/2023] [Indexed: 04/14/2023]
Abstract
Contrast-induced nephropathy (CIN) is associated with increased mortality and morbidity in patients with coronary artery disease undergoing elective percutaneous coronary intervention(PCI). We developed a machine learning-based risk stratification model to predict contrast-induced nephropathy after PCI. A study retrospectively enrolling 240 patients eligible for PCI from December 2017 to May 2020 was performed. CIN was defined as a rise in serum creatinine levels ≥0.5 mg/dL or ≥25% from baseline within 72 h after surgery. Eight machine learning methods were performed based on clinical variables. Shapley Additive exPlanation values were also used to interpret the best-performing prediction models. Development of CIN was found in 37 patients(16.5%) after PCI. There were 11 significant predictors of CIN, including uric acid, peripheral vascular disease, cystatin C, creatine kinase-MB, haemoglobin, N-terminal pro-brain natriuretic peptide, age, diabetes, systemic immune-inflammatory index, total protein, and low-density lipoprotein. Regarding the efficacy of the machine learning model that accurately predicted CIN, SVM exhibited the most outstanding AUC value of 0.784. The SHAP and radar plots were used to illustrate the positive and negative effects of the 11 features attributed to the SVM. Machine learning models have the potential to identify the risk of CIN for elective PCI patients.
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Affiliation(s)
- Xiao Ma
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China
- Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China
- Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. China
| | - Changhua Mo
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China
- Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China
- Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. China
| | - Yujuan Li
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China
- Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China
- Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. China
| | - Xinyuan Chen
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China
| | - Chun Gui
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China
- Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China
- Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. China
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Bäckel N, Hort S, Kis T, Nettleton DF, Egan JR, Jacobs JJL, Grunert D, Schmitt RH. Elaborating the potential of Artificial Intelligence in automated CAR-T cell manufacturing. FRONTIERS IN MOLECULAR MEDICINE 2023; 3:1250508. [PMID: 39086671 PMCID: PMC11285580 DOI: 10.3389/fmmed.2023.1250508] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 08/28/2023] [Indexed: 08/02/2024]
Abstract
This paper discusses the challenges of producing CAR-T cells for cancer treatment and the potential for Artificial Intelligence (AI) for its improvement. CAR-T cell therapy was approved in 2018 as the first Advanced Therapy Medicinal Product (ATMP) for treating acute leukemia and lymphoma. ATMPs are cell- and gene-based therapies that show great promise for treating various cancers and hereditary diseases. While some new ATMPs have been approved, ongoing clinical trials are expected to lead to the approval of many more. However, the production of CAR-T cells presents a significant challenge due to the high costs associated with the manufacturing process, making the therapy very expensive (approx. $400,000). Furthermore, autologous CAR-T therapy is limited to a make-to-order approach, which makes scaling economical production difficult. First attempts are being made to automate this multi-step manufacturing process, which will not only directly reduce the high manufacturing costs but will also enable comprehensive data collection. AI technologies have the ability to analyze this data and convert it into knowledge and insights. In order to exploit these opportunities, this paper analyses the data potential in the automated CAR-T production process and creates a mapping to the capabilities of AI applications. The paper explores the possible use of AI in analyzing the data generated during the automated process and its capabilities to further improve the efficiency and cost-effectiveness of CAR-T cell production.
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Affiliation(s)
- Niklas Bäckel
- Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Simon Hort
- Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Tamás Kis
- Institute for Computer Science and Control, Hungarian Research Network, Budapest, Hungary
| | | | - Joseph R. Egan
- Department of Biochemical Engineering, Mathematical Modelling of Cell and Gene Therapies, University College London, London, United Kingdom
| | | | - Dennis Grunert
- Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Robert H. Schmitt
- Fraunhofer Institute for Production Technology IPT, Aachen, Germany
- Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany
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Ferreira LD, McCants D, Velamuri S. Using machine learning for process improvement in sepsis management. J Healthc Qual Res 2023; 38:304-311. [PMID: 36319584 DOI: 10.1016/j.jhqr.2022.09.006] [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] [Received: 06/07/2022] [Revised: 08/18/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
INTRODUCTION In the U.S., sepsis afflicts 1.7 million adults, causing 270,000 deaths each year. Early detection of sepsis could decrease the number of deaths by 92,000 annually and decrease hospital expenditures by 1.5 billion USD. Few prior studies and reviews have presented a holistic understanding of the relationship between machine learning and existing process improvement measures. This study, in addition to discussing machine learning and existing process improvements measures, elaborates on the disadvantages and the barriers to integrating machine learning into the clinic. This article synthesizes previous studies to educate healthcare professionals on effectively managing sepsis by leveraging the benefits of machine learning. METHODS This study used the PubMed database. Search terms include sepsis antibiotics, sepsis process improvement, sepsis machine learning. Our search criteria included previous studies published between January 1, 2017, and February 1, 2022. RESULTS/DISCUSSION Although machine learning algorithms have better predictive capabilities, their effectiveness in the clinical setting is limited as studies show mixed results because the medical staff often fails to intervene. To overcome poor interventional response, clinicians need to work with the facility's IT department to ensure integration into clinical workflow and minimize alert-fatigue. Algorithms should enhance the productivity of clinical teams, not attempt to replace them entirely. CONCLUSION Hospitals can employ process improvement measures that effectively utilize machine learning algorithms to ensure integration into clinical workflows. Healthcare professionals can utilize workflow tools in addition to the predictive capabilities of machine learning to enhance clinical decisions in sepsis.
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Affiliation(s)
- L D Ferreira
- Department of Student Affairs, Baylor College of Medicine, United States.
| | - D McCants
- Department of Internal Medicine, Baylor College of Medicine, United States
| | - S Velamuri
- Department of Internal Medicine, Baylor College of Medicine, United States; Luminare, Inc. United States
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11
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Malorey D, Lorton F, Chalumeau M, Bourgoin P, Boussicault G, Chantreuil J, Gaillot T, Roué JM, Martinot A, Assathiany R, Saulnier JP, Caillon J, Grain A, Gras-Le Guen C, Launay E. Distribution, Consequences, and Determinants of Time to Antibiotics in Children With Community-Onset Severe Bacterial Infection: A Secondary Analysis of a Prospective Population-Based Study. Pediatr Crit Care Med 2023; 24:e441-e451. [PMID: 37260312 DOI: 10.1097/pcc.0000000000003306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
OBJECTIVES To describe the distribution, consequences and potential determinants of time to antibiotics administration in children with community-onset severe bacterial infections (COSBIs). DESIGN Secondary analysis of the available data from a prospective population-based study from 2009 to 2014. SETTING An administrative area in western France accounting for 13% of the national pediatric population. PATIENTS All children from 1 month to 16 years old admitted to a PICU or who died before admission and had a COSBI. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS The time to antibiotics was divided into patient interval (from first signs of COSBI to the first medical consultation) and medical interval (from the first consultation to appropriate antibiotics administration). The association between the medical interval and child outcome was studied by a multinomial logistic regression model and the potential determinants of the patient and medical intervals were by a Cox proportional-hazards model. Of the 227 children included (median age 2.1 yr), 22 died (9.7%), and 21 (9.3%) had severe sequelae at PICU discharge. Median patient and medical intervals were 7.0 hours (interquartile range [IQR], 2.0-16.5 hr) and 3.3 hours (IQR, 1.1-12.2 hr), respectively. The last quartile of medical interval was not associated with death (adjusted odds ratio [aOR], 3.7; 95% CI, 0.8-17.5) or survival with severe sequelae (aOR, 1.3; 95% CI, 0.4-4.0) versus survival without severe sequelae. Patient interval was shorter in younger children (adjusted hazard ratio [aHR], 0.95; 95% CI, 0.92-0.99), and medical interval was reduced when the first consultation was conducted in a hospital (aHR, 1.5; 95% CI, 1.1-2.0) versus outpatient medicine. CONCLUSIONS For children with COSBI, we found no significant association between medical interval and mortality or severe sequelae. An initial hospital referral could help reduce the time to antibiotics in COSBIs.
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Affiliation(s)
- David Malorey
- Department of Pediatrics and Pediatric Emergency, Hôpital Femme Enfant Adolescent, CHU de Nantes, Nantes, France
- Inserm UMR 1153, Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Centre of Research in Epidemiology and StatisticS (CRESS), Paris Descartes University, Paris, France
| | - Fleur Lorton
- Department of Pediatrics and Pediatric Emergency, Hôpital Femme Enfant Adolescent, CHU de Nantes, Nantes, France
- Inserm UMR 1153, Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Centre of Research in Epidemiology and StatisticS (CRESS), Paris Descartes University, Paris, France
- Inserm 1413 CIC FEA, Nantes University Hospital, Nantes, France
- Department of General Pediatrics and Pediatric Infectious Diseases, Necker Hospital for Sick Children, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris, France
- Department of Pediatric and Neonatal Critical Care, Femme Enfant Adolescent, CHU de Nantes, Nantes, France
- Department of Pediatric Critical Care, CHU d'Angers, Angers, France
- Department of Pediatric Critical Care, Hôpital Clocheville, CHU de Tours, Tours, France
- Department of Pediatric Critical Care, Hôpital Sud, CHU de Rennes, Rennes, France
- Department of Pediatric and Neonatal Critical Care, Hôpital Morvan, CHU de Brest, Brest, France
- University Lille, ULR 2694-METRICS: Evaluation des technologies de Santé et des pratiques médicales, CHU Lille, Lille, France
- Association pour la Recherche et l'Enseignement en Pédiatrie Générale (AREPEGE); Association Française de Pédiatrie Ambulatoire (AFPA), Cabinet de Pédiatrie, Issy-les-Moulineaux, France
- Department of Pediatric and Neonatal Critical Care, Tour Jean Bernard, CHU de Poitiers, Poitiers, France
- Department of Microbiology, Hôtel Dieu, CHU de Nantes, Nantes, France
- Pediatric Haematology and Oncology Department, University Hospital of Nantes, Nantes, France
- Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, Nantes, France
| | - Martin Chalumeau
- Inserm UMR 1153, Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Centre of Research in Epidemiology and StatisticS (CRESS), Paris Descartes University, Paris, France
- Department of General Pediatrics and Pediatric Infectious Diseases, Necker Hospital for Sick Children, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris, France
| | - Pierre Bourgoin
- Department of Pediatric and Neonatal Critical Care, Femme Enfant Adolescent, CHU de Nantes, Nantes, France
| | | | - Julie Chantreuil
- Department of Pediatric Critical Care, Hôpital Clocheville, CHU de Tours, Tours, France
| | - Théophile Gaillot
- Department of Pediatric Critical Care, Hôpital Sud, CHU de Rennes, Rennes, France
| | - Jean-Michel Roué
- Department of Pediatric and Neonatal Critical Care, Hôpital Morvan, CHU de Brest, Brest, France
| | - Alain Martinot
- University Lille, ULR 2694-METRICS: Evaluation des technologies de Santé et des pratiques médicales, CHU Lille, Lille, France
| | - Rémy Assathiany
- Association pour la Recherche et l'Enseignement en Pédiatrie Générale (AREPEGE); Association Française de Pédiatrie Ambulatoire (AFPA), Cabinet de Pédiatrie, Issy-les-Moulineaux, France
| | - Jean-Pascal Saulnier
- Department of Pediatric and Neonatal Critical Care, Tour Jean Bernard, CHU de Poitiers, Poitiers, France
| | - Jocelyne Caillon
- Department of Microbiology, Hôtel Dieu, CHU de Nantes, Nantes, France
| | - Audrey Grain
- Pediatric Haematology and Oncology Department, University Hospital of Nantes, Nantes, France
- Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, Nantes, France
| | - Christèle Gras-Le Guen
- Department of Pediatrics and Pediatric Emergency, Hôpital Femme Enfant Adolescent, CHU de Nantes, Nantes, France
- Inserm UMR 1153, Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Centre of Research in Epidemiology and StatisticS (CRESS), Paris Descartes University, Paris, France
- Inserm 1413 CIC FEA, Nantes University Hospital, Nantes, France
- Department of General Pediatrics and Pediatric Infectious Diseases, Necker Hospital for Sick Children, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris, France
- Department of Pediatric and Neonatal Critical Care, Femme Enfant Adolescent, CHU de Nantes, Nantes, France
- Department of Pediatric Critical Care, CHU d'Angers, Angers, France
- Department of Pediatric Critical Care, Hôpital Clocheville, CHU de Tours, Tours, France
- Department of Pediatric Critical Care, Hôpital Sud, CHU de Rennes, Rennes, France
- Department of Pediatric and Neonatal Critical Care, Hôpital Morvan, CHU de Brest, Brest, France
- University Lille, ULR 2694-METRICS: Evaluation des technologies de Santé et des pratiques médicales, CHU Lille, Lille, France
- Association pour la Recherche et l'Enseignement en Pédiatrie Générale (AREPEGE); Association Française de Pédiatrie Ambulatoire (AFPA), Cabinet de Pédiatrie, Issy-les-Moulineaux, France
- Department of Pediatric and Neonatal Critical Care, Tour Jean Bernard, CHU de Poitiers, Poitiers, France
- Department of Microbiology, Hôtel Dieu, CHU de Nantes, Nantes, France
- Pediatric Haematology and Oncology Department, University Hospital of Nantes, Nantes, France
- Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, Nantes, France
| | - Elise Launay
- Department of Pediatrics and Pediatric Emergency, Hôpital Femme Enfant Adolescent, CHU de Nantes, Nantes, France
- Inserm UMR 1153, Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Centre of Research in Epidemiology and StatisticS (CRESS), Paris Descartes University, Paris, France
- Inserm 1413 CIC FEA, Nantes University Hospital, Nantes, France
- Department of General Pediatrics and Pediatric Infectious Diseases, Necker Hospital for Sick Children, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris, France
- Department of Pediatric and Neonatal Critical Care, Femme Enfant Adolescent, CHU de Nantes, Nantes, France
- Department of Pediatric Critical Care, CHU d'Angers, Angers, France
- Department of Pediatric Critical Care, Hôpital Clocheville, CHU de Tours, Tours, France
- Department of Pediatric Critical Care, Hôpital Sud, CHU de Rennes, Rennes, France
- Department of Pediatric and Neonatal Critical Care, Hôpital Morvan, CHU de Brest, Brest, France
- University Lille, ULR 2694-METRICS: Evaluation des technologies de Santé et des pratiques médicales, CHU Lille, Lille, France
- Association pour la Recherche et l'Enseignement en Pédiatrie Générale (AREPEGE); Association Française de Pédiatrie Ambulatoire (AFPA), Cabinet de Pédiatrie, Issy-les-Moulineaux, France
- Department of Pediatric and Neonatal Critical Care, Tour Jean Bernard, CHU de Poitiers, Poitiers, France
- Department of Microbiology, Hôtel Dieu, CHU de Nantes, Nantes, France
- Pediatric Haematology and Oncology Department, University Hospital of Nantes, Nantes, France
- Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, Nantes, France
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12
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Tabaie A, Orenstein EW, Kandaswamy S, Kamaleswaran R. Integrating structured and unstructured data for timely prediction of bloodstream infection among children. Pediatr Res 2023; 93:969-975. [PMID: 35854085 DOI: 10.1038/s41390-022-02116-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/08/2022] [Accepted: 05/08/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Hospitalized children with central venous lines (CVLs) are at higher risk of hospital-acquired infections. Information in electronic health records (EHRs) can be employed in training deep learning models to predict the onset of these infections. We incorporated clinical notes in addition to structured EHR data to predict serious bloodstream infections, defined as positive blood culture followed by at least 4 days of new antimicrobial agent administration, among hospitalized children with CVLs. METHODS Structured EHR information and clinical notes were extracted for a retrospective cohort including all hospitalized patients with CVLs at a single tertiary care pediatric health system from 2013 to 2018. Deep learning models were trained to determine the added benefit of incorporating the information embedded in clinical notes in predicting serious bloodstream infection. RESULTS A total of 24,351 patient encounters met inclusion criteria. The best-performing model restricted to structured EHR data had a specificity of 0.951 and positive predictive value (PPV) of 0.056 when the sensitivity was set to 0.85. The addition of contextualized word embeddings improved the specificity to 0.981 and PPV to 0.113. CONCLUSIONS Integrating clinical notes with structured EHR data improved the prediction of serious bloodstream infections among pediatric patients with CVLs. IMPACT Developed an advanced infection prediction model in pediatrics that integrates the structured and unstructured EHRs. Extracted information from clinical notes to do timely prediction in a clinical setting. Developed a deep learning model framework that can be employed in predicting rare events in a complex and dynamic environment.
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Affiliation(s)
- Azade Tabaie
- Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA.
| | - Evan W Orenstein
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | | | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, USA
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13
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Aylward BS, Abbas H, Taraman S, Salomon C, Gal-Szabo D, Kraft C, Ehwerhemuepha L, Chang A, Wall DP. An Introduction to Artificial Intelligence in Developmental and Behavioral Pediatrics. J Dev Behav Pediatr 2023; 44:e126-e134. [PMID: 36730317 PMCID: PMC9907689 DOI: 10.1097/dbp.0000000000001149] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 09/12/2022] [Indexed: 02/03/2023]
Abstract
ABSTRACT Technological breakthroughs, together with the rapid growth of medical information and improved data connectivity, are creating dramatic shifts in the health care landscape, including the field of developmental and behavioral pediatrics. While medical information took an estimated 50 years to double in 1950, by 2020, it was projected to double every 73 days. Artificial intelligence (AI)-powered health technologies, once considered theoretical or research-exclusive concepts, are increasingly being granted regulatory approval and integrated into clinical care. In the United States, the Food and Drug Administration has cleared or approved over 160 health-related AI-based devices to date. These trends are only likely to accelerate as economic investment in AI health care outstrips investment in other sectors. The exponential increase in peer-reviewed AI-focused health care publications year over year highlights the speed of growth in this sector. As health care moves toward an era of intelligent technology powered by rich medical information, pediatricians will increasingly be asked to engage with tools and systems underpinned by AI. However, medical students and practicing clinicians receive insufficient training and lack preparedness for transitioning into a more AI-informed future. This article provides a brief primer on AI in health care. Underlying AI principles and key performance metrics are described, and the clinical potential of AI-driven technology together with potential pitfalls is explored within the developmental and behavioral pediatric health context.
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Affiliation(s)
| | | | - Sharief Taraman
- Cognoa, Inc, Palo Alto, CA
- CHOC (Children's Health of Orange County), Orange, CA
- University of California Irvine, Irvine, CA
- Chapman University, Orange, CA
- Medical Intelligence and Innovation Institute (M13), CHOC, Orange, CA
| | | | | | - Colleen Kraft
- Cognoa, Inc, Palo Alto, CA
- University of Southern California, Los Angeles, CA
- Children's Hospital of Los Angeles, Los Angeles, CA; and
| | - Louis Ehwerhemuepha
- CHOC (Children's Health of Orange County), Orange, CA
- Chapman University, Orange, CA
- Medical Intelligence and Innovation Institute (M13), CHOC, Orange, CA
| | - Anthony Chang
- CHOC (Children's Health of Orange County), Orange, CA
- University of California Irvine, Irvine, CA
- Medical Intelligence and Innovation Institute (M13), CHOC, Orange, CA
| | - Dennis P. Wall
- Cognoa, Inc, Palo Alto, CA
- Stanford Medical School, Palo Alto, CA
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14
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Data harnessing to nurture the human mind for a tailored approach to the child. Pediatr Res 2023; 93:357-365. [PMID: 36180585 DOI: 10.1038/s41390-022-02320-4] [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] [Received: 03/14/2022] [Revised: 07/06/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022]
Abstract
Big data in pediatrics is an ocean of structured and unstructured data. Big data analysis helps to dive into the ocean of data to filter out information that can guide pediatricians in their decision making, precision diagnosis, and targeted therapy. In addition, big data and its analysis have helped in the surveillance, prevention, and performance of the health system. There has been a considerable amount of work in pediatrics that we have tried to highlight in this review and some of it has been already incorporated into the health system. Work in specialties of pediatrics is still forthcoming with the creation of a common data model and amalgamation of the huge "omics" database. The physicians entrusted with the care of children must be aware of the outcome so that they can play a role to ensure that big data algorithms have a clinically relevant effect in improving the health of their patients. They will apply the outcome of big data and its analysis in patient care through clinical algorithms or with the help of embedded clinical support alerts from the electronic medical records. IMPACT: Big data in pediatrics include structured, unstructured data, waveform data, biological, and social data. Big data analytics has unraveled significant information from these databases. This is changing how pediatricians will look at the body of available evidence and translate it into their clinical practice. Data harnessed so far is implemented in certain fields while in others it is in the process of development to become a clinical adjunct to the physician. Common databases are being prepared for future work. Diagnostic and prediction models when incorporated into the health system will guide the pediatrician to a targeted approach to diagnosis and therapy.
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15
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Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TA. Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res 2023; 93:334-341. [PMID: 35906317 PMCID: PMC9668209 DOI: 10.1038/s41390-022-02226-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/29/2022] [Accepted: 07/18/2022] [Indexed: 11/24/2022]
Abstract
Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical deterioration to provide evidence-based recommendations. This use of artificial intelligence models in clinical decision support (AI-CDS) may have several advantages over traditional "rule-based" CDS models in pediatric care through increased model accuracy, with fewer false alerts and missed patients. AI-CDS tools must be appropriately developed, provide insight into the rationale behind decisions, be seamlessly integrated into care pathways, be intuitive to use, answer clinically relevant questions, respect the content expertise of the healthcare provider, and be scientifically sound. While numerous machine learning models have been reported in pediatric care, their integration into AI-CDS remains incompletely realized to date. Important challenges in the application of AI models in pediatric care include the relatively lower rates of clinically significant outcomes compared to adults, and the lack of sufficiently large datasets available necessary for the development of machine learning models. In this review article, we summarize key concepts related to AI-CDS, its current application to pediatric care, and its potential benefits and risks. IMPACT: The performance of clinical decision support may be enhanced by the utilization of machine learning-based algorithms to improve the predictive performance of underlying models. Artificial intelligence-based clinical decision support (AI-CDS) uses models that are experientially improved through training and are particularly well suited toward high-dimensional data. The application of AI-CDS toward pediatric care remains limited currently but represents an important area of future research.
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Affiliation(s)
- Sriram Ramgopal
- Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - L. Nelson Sanchez-Pinto
- grid.16753.360000 0001 2299 3507Division of Critical Care Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA ,grid.16753.360000 0001 2299 3507Department of Preventive Medicine (Health and Biomedical Informatics), Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Christopher M. Horvat
- grid.21925.3d0000 0004 1936 9000Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
| | - Michael S. Carroll
- grid.16753.360000 0001 2299 3507Data Analytics and Reporting, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Yuan Luo
- grid.16753.360000 0001 2299 3507Department of Preventive Medicine (Health and Biomedical Informatics), Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Todd A. Florin
- grid.16753.360000 0001 2299 3507Division of Emergency Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA
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McCahill C, Laycock HC, Guris RJD, Chigaru L. State-of-the-art management of the acutely unwell child. Anaesthesia 2022; 77:1288-1298. [PMID: 36089884 PMCID: PMC9826095 DOI: 10.1111/anae.15816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2022] [Indexed: 01/11/2023]
Abstract
Children make up around one-fifth of all emergency department visits in the USA and UK, with an increasing trend of emergency admissions requiring intensive care. Anaesthetists play a vital role in the management of paediatric emergencies contributing to stabilisation, emergency anaesthesia, transfers and non-technical skills that optimise team performance. From neonates to adolescents, paediatric patients have diverse physiology and present with a range of congenital and acquired pathologies that often differ from the adult population. With increasing centralisation of paediatric services, staff outside these centres have less exposure to caring for children, yet are often the first responders in managing these high stakes situations. Staying abreast of the latest evidence for managing complex low frequency emergencies is a challenge. This review focuses on recent evidence and pertinent clinical updates within the field. The challenges of maintaining skills and training are explored as well as novel advancements in care.
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Affiliation(s)
- C. McCahill
- Department of AnaesthesiaGreat Ormond Street HospitalLondonUK
| | - H. C. Laycock
- Department of AnaesthesiaGreat Ormond Street HospitalLondonUK,Department of Surgery and CancerImperial CollegeLondonUK
| | - R. J. Daly Guris
- Department of Anesthesiology and Critical Care MedicineChildren's Hospital of PhiladelphiaPhiladelphiaPAUSA,Department of Anesthesiology and Critical CareUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - L. Chigaru
- Department of AnaesthesiaGreat Ormond Street HospitalLondonUK,Children's Acute Transport ServiceLondonUK
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Khan M, Khurshid M, Vatsa M, Singh R, Duggal M, Singh K. On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review. Front Public Health 2022; 10:880034. [PMID: 36249249 PMCID: PMC9562034 DOI: 10.3389/fpubh.2022.880034] [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: 02/20/2022] [Accepted: 05/30/2022] [Indexed: 01/21/2023] Open
Abstract
A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.
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Affiliation(s)
- Misaal Khan
- Department of Smart Healthcare, Indian Institute of Technology Jodhpur, Karwar, India,All India Institute of Medical Sciences Jodhpur, Jodhpur, India
| | - Mahapara Khurshid
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India
| | - Mayank Vatsa
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India,*Correspondence: Mayank Vatsa
| | - Richa Singh
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India
| | - Mona Duggal
- Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Kuldeep Singh
- Department of Pediatrics, All India Institute of Medical Sciences Jodhpur, Jodhpur, India
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Kiser AC, Eilbeck K, Ferraro JP, Skarda DE, Samore MH, Bucher B. Standard Vocabularies to Improve Machine Learning Model Transferability With Electronic Health Record Data: Retrospective Cohort Study Using Health Care-Associated Infection. JMIR Med Inform 2022; 10:e39057. [PMID: 36040784 PMCID: PMC9472055 DOI: 10.2196/39057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 08/09/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND With the widespread adoption of electronic healthcare records (EHRs) by US hospitals, there is an opportunity to leverage this data for the development of predictive algorithms to improve clinical care. A key barrier in model development and implementation includes the external validation of model discrimination, which is rare and often results in worse performance. One reason why machine learning models are not externally generalizable is data heterogeneity. A potential solution to address the substantial data heterogeneity between health care systems is to use standard vocabularies to map EHR data elements. The advantage of these vocabularies is a hierarchical relationship between elements, which allows the aggregation of specific clinical features to more general grouped concepts. OBJECTIVE This study aimed to evaluate grouping EHR data using standard vocabularies to improve the transferability of machine learning models for the detection of postoperative health care-associated infections across institutions with different EHR systems. METHODS Patients who underwent surgery from the University of Utah Health and Intermountain Healthcare from July 2014 to August 2017 with complete follow-up data were included. The primary outcome was a health care-associated infection within 30 days of the procedure. EHR data from 0-30 days after the operation were mapped to standard vocabularies and grouped using the hierarchical relationships of the vocabularies. Model performance was measured using the area under the receiver operating characteristic curve (AUC) and F1-score in internal and external validations. To evaluate model transferability, a difference-in-difference metric was defined as the difference in performance drop between internal and external validations for the baseline and grouped models. RESULTS A total of 5775 patients from the University of Utah and 15,434 patients from Intermountain Healthcare were included. The prevalence of selected outcomes was from 4.9% (761/15,434) to 5% (291/5775) for surgical site infections, from 0.8% (44/5775) to 1.1% (171/15,434) for pneumonia, from 2.6% (400/15,434) to 3% (175/5775) for sepsis, and from 0.8% (125/15,434) to 0.9% (50/5775) for urinary tract infections. In all outcomes, the grouping of data using standard vocabularies resulted in a reduced drop in AUC and F1-score in external validation compared to baseline features (all P<.001, except urinary tract infection AUC: P=.002). The difference-in-difference metrics ranged from 0.005 to 0.248 for AUC and from 0.075 to 0.216 for F1-score. CONCLUSIONS We demonstrated that grouping machine learning model features based on standard vocabularies improved model transferability between data sets across 2 institutions. Improving model transferability using standard vocabularies has the potential to improve the generalization of clinical prediction models across the health care system.
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Affiliation(s)
- Amber C Kiser
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Karen Eilbeck
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Jeffrey P Ferraro
- Department of Medicine, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - David E Skarda
- Center for Value-Based Surgery, Intermountain Healthcare, Salt Lake City, UT, United States.,Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Matthew H Samore
- Department of Medicine, School of Medicine, University of Utah, Salt Lake City, UT, United States.,Informatics, Decision-Enhancement and Analytic Sciences Center 2.0, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, United States
| | - Brian Bucher
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States.,Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT, United States
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19
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Sveen W, Dewan M, Dexheimer JW. The Risk of Coding Racism into Pediatric Sepsis Care: The Necessity of Antiracism in Machine Learning. J Pediatr 2022; 247:129-132. [PMID: 35469891 DOI: 10.1016/j.jpeds.2022.04.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 03/16/2022] [Accepted: 04/15/2022] [Indexed: 11/27/2022]
Abstract
Machine learning holds the possibility of improving racial health inequalities by compensating for human bias and structural racism. However, unanticipated racial biases may enter during model design, training, or implementation and perpetuate or worsen racial inequalities if ignored. Pre-existing racial health inequalities could be codified into medical care by machine learning without clinicians being aware. To illustrate the importance of a commitment to antiracism at all stages of machine learning, we examine machine learning in predicting severe sepsis in Black children, focusing on the impacts of structural racism that may be perpetuated by machine learning and difficult to discover. To move toward antiracist machine learning, we recommend partnering with ethicists and experts in model development, enrolling representative samples for training, including socioeconomic inputs with proximate causal associations to racial inequalities, reporting outcomes by race, and committing to equitable models that narrow inequality gaps or at least have equal benefit.
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Affiliation(s)
- William Sveen
- Department of Pediatrics, University of Minnesota, Minneapolis, MN.
| | - Maya Dewan
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati, Cincinnati, OH
| | - Judith W Dexheimer
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati, Cincinnati, OH
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20
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Matsushita FY, Krebs VLJ, Carvalho WBD. Artificial intelligence and machine learning in pediatrics and neonatology healthcare. Rev Assoc Med Bras (1992) 2022; 68:745-750. [PMID: 35766685 PMCID: PMC9575899 DOI: 10.1590/1806-9282.20220177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 02/09/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Felipe Yu Matsushita
- Universidade de São Paulo, Faculty of Medicine, Department of Pediatrics - São Paulo (SP), Brazil
| | - Vera Lucia Jornada Krebs
- Universidade de São Paulo, Faculty of Medicine, Department of Pediatrics - São Paulo (SP), Brazil
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21
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Rajmohan R, Kumar TA, Julie EG, Robinson YH, Vimal S, Kadry S, Crespo RG. G-Sep: A Deep Learning Algorithm for Detection of Long-Term Sepsis Using Bidirectional Gated Recurrent Unit. INT J UNCERTAIN FUZZ 2022. [DOI: 10.1142/s0218488522400013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Sepsis is a common and deadly condition that must be treated eloquently within 19 hours. Numerous deep learning techniques, including Recurrent Neural Networks, Convolution Neural Networks, Long Short-Term Memory, and Gated Recurrent Units, have been suggested for diagnosing long-term sepsis. Regardless, a sizable portion of them are computationally risky and have precision problems. The primary issue described is that output will degrade, and resource utilization will expand proportionately as the volume of dependencies grows. To overcome these issues, we propose a G-Sep technique utilizing Bidirectional Gated Recurrent Unit Algorithm, which consumes much less resource to detect the disease and in a short time with better accuracy than the existing methods to diagnose the sepsis. AI models could assist with distinguishing potential clinical factors and give better than existing conventional low-execution models. The proposed model is implemented utilizing Conda and Tensorflow Framework using the California Inpatient Severe Sepsis (CISS) Patient Dataset. The comparative simulation of the various existing models and the proposed G-Sep model is done using Conda and Tensor frameworks. The simulation results revealed that the proposed model had outperformed other frameworks in terms of mean average precision (mAP), receiver operating characteristic curve (ROC), and Area under the ROC Curve (AUROC) metrics linearly.
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Affiliation(s)
- R. Rajmohan
- Department of Computer Science and Engineering, IFET College of Engineering, Villupuram, Tamil Nadu, India
| | - T. Ananth Kumar
- Department of Computer Science and Engineering, IFET College of Engineering, Villupuram, Tamil Nadu, India
| | - E. Golden Julie
- Department of Computer Science and Engineering, Anna University Regional Campus, Tirunelveli, Tamil Nadu, India
| | - Y. Harold Robinson
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - S. Vimal
- Department of Computer Science and Engineering, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India
| | - Seifidine Kadry
- Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway
| | - Ruben Gonzalez Crespo
- Department of Engineering, School of Engineering and Technology, Universidad Internacional de la Rioja (UNIR), Logroño, Spain
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22
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Guo F, Zhu X, Wu Z, Zhu L, Wu J, Zhang F. Clinical applications of machine learning in the survival prediction and classification of sepsis: coagulation and heparin usage matter. J Transl Med 2022; 20:265. [PMID: 35690822 PMCID: PMC9187899 DOI: 10.1186/s12967-022-03469-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/30/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Sepsis is a life-threatening syndrome eliciting highly heterogeneous host responses. Current prognostic evaluation methods used in clinical practice are characterized by an inadequate effectiveness in predicting sepsis mortality. Rapid identification of patients with high mortality risk is urgently needed. The phenotyping of patients will assistant invaluably in tailoring treatments. METHODS Machine learning and deep learning technology are used to characterize the patients' phenotype and determine the sepsis severity. The database used in this study is MIMIC-III and MIMIC-IV ('Medical information Mart for intensive care') which is a large, public, and freely available database. The K-means clustering is used to classify the sepsis phenotype. Convolutional neural network (CNN) was used to predict the 28-day survival rate based on 35 blood test variables of the sepsis patients, whereas a double coefficient quadratic multivariate fitting function (DCQMFF) is utilized to predict the 28-day survival rate with only 11 features of sepsis patients. RESULTS The patients were grouped into four clusters with a clear survival nomogram. The first cluster (C_1) was characterized by low white blood cell count, low neutrophil, and the highest lymphocyte proportion. C_2 obtained the lowest Sequential Organ Failure Assessment (SOFA) score and the highest survival rate. C_3 was characterized by significantly prolonged PTT, high SIC, and a higher proportion of patients using heparin than the patients in other clusters. The early mortality rate of patients in C_3 was high but with a better long-term survival rate than that in C_4. C_4 contained septic coagulation patients with the worst prognosis, characterized by slightly prolonged partial thromboplastin time (PTT), significantly prolonged prothrombin time (PT), and high septic coagulation disease score (SIC). The survival rate prediction accuracy of CNN and DCQMFF models reached 92% and 82%, respectively. The models were tested on an external dataset (MIMIC-IV) and achieved good performance. A DCQMFF-based application platform was established for fast prediction of the 28-day survival rate. CONCLUSION CNN and DCQMFF accurately predicted the sepsis patients' survival, while K-means successfully identified the phenotype groups. The distinct phenotypes associated with survival, and significant features correlated with mortality were identified. The findings suggest that sepsis patients with abnormal coagulation had poor outcomes, abnormal coagulation increase mortality during sepsis. The anticoagulation effects of appropriate heparin sodium treatment may improve extensive micro thrombosis-caused organ failure.
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Affiliation(s)
- Fei Guo
- Ningbo Institute for Medicine & Biomedical Engineering Combined Innovation, Ningbo Medical Treatment Centre Lihuili Hospital, Ningbo University, Ningbo, 315040, Zhejiang, China
| | - Xishun Zhu
- School of Mechatronics Engineering, Nanchang University, Nanchang, 330031, Jiangxi, China
| | - Zhiheng Wu
- School of Information Engineering, Nanchang University, Nanchang, 330031, Jiangxi, China
| | - Li Zhu
- School of Information Engineering, Nanchang University, Nanchang, 330031, Jiangxi, China
| | - Jianhua Wu
- School of Information Engineering, Nanchang University, Nanchang, 330031, Jiangxi, China.
| | - Fan Zhang
- Department of Critical Care Medicine, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
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23
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Martin B, DeWitt PE, Scott HF, Parker S, Bennett TD. Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission. Hosp Pediatr 2022; 12:590-603. [PMID: 35634885 DOI: 10.1542/hpeds.2021-005998] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND OBJECTIVES Serious bacterial infection (SBI) is common in the PICU. Antibiotics can mitigate associated morbidity and mortality but have associated adverse effects. Our objective is to develop machine learning models able to identify SBI-negative children and reduce unnecessary antibiotics. METHODS We developed models to predict SBI-negative status at PICU admission using vital sign, laboratory, and demographic variables. Children 3-months to 18-years-old admitted to our PICU, between 2011 and 2020, were included if evaluated for infection within 24-hours, stratified by documented antibiotic exposure in the 48-hours prior. Area under the receiver operating characteristic curve (AUROC) was the primary model accuracy measure; secondarily, we calculated the number of SBI-negative children subsequently provided antibiotics in the PICU identified as low-risk by each model. RESULTS A total of 15 074 children met inclusion criteria; 4788 (32%) received antibiotics before PICU admission. Of these antibiotic-exposed patients, 2325 of 4788 (49%) had an SBI. Of the 10 286 antibiotic-unexposed patients, 2356 of 10 286 (23%) had an SBI. In antibiotic-exposed children, a radial support vector machine model had the highest AUROC (0.80) for evaluating SBI, identifying 48 of 442 (11%) SBI-negative children provided antibiotics in the PICU who could have been spared a median 3.7 (interquartile range 0.9-9.0) antibiotic-days per patient. In antibiotic-unexposed children, a random forest model performed best, but was less accurate overall (AUROC 0.76), identifying 33 of 469 (7%) SBI-negative children provided antibiotics in the PICU who could have been spared 1.1 (interquartile range 0.9-3.7) antibiotic-days per patient. CONCLUSIONS Among children who received antibiotics before PICU admission, machine learning models can identify children at low risk of SBI and potentially reduce antibiotic exposure.
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Affiliation(s)
- Blake Martin
- Department of Pediatrics, Sections of Critical Care
- Children's Hospital Colorado, Aurora, Colorado
| | | | - Halden F Scott
- Emergency Medicine
- Children's Hospital Colorado, Aurora, Colorado
| | - Sarah Parker
- Infectious Diseases, University of Colorado School of Medicine, Aurora, Colorado
- Children's Hospital Colorado, Aurora, Colorado
| | - Tellen D Bennett
- Department of Pediatrics, Sections of Critical Care
- Informatics and Data Science
- Children's Hospital Colorado, Aurora, Colorado
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24
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Lee B, Chung HJ, Kang HM, Kim DK, Kwak YH. Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments. PLoS One 2022; 17:e0265500. [PMID: 35333881 PMCID: PMC8956167 DOI: 10.1371/journal.pone.0265500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/02/2022] [Indexed: 12/03/2022] Open
Abstract
Serious bacterial infection (SBI) in children, such as bacterial meningitis or sepsis, is an important condition that can lead to fatal outcomes. Therefore, since it is very important to accurately diagnose SBI, SBI prediction tools such as 'Refined Lab-score' or 'clinical prediction rule' have been developed and used. However, these tools can predict SBI only when there are values of all factors used in the tool, and if even one of them is missing, the tools become useless. Therefore, the purpose of this study was to develop and validate a machine learning-driven model to predict SBIs among febrile children, even with missing values. This was a multicenter retrospective observational study including febrile children <6 years of age who visited Emergency departments (EDs) of 3 different tertiary hospitals from 2016 to 2018. The SBI prediction model was trained with a derivation cohort (data from two hospitals) and externally tested with a validation cohort (data from a third hospital). A total of 11,973 and 2,858 patient records were included in the derivation and validation cohorts, respectively. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) of the RF model was 0.964 (95% confidence interval [CI], 0.943-0.986), and the area under the precision-recall curve (AUPRC) was 0.753 (95% CI, 0.681-0.824). The conventional LR (CLR) model showed corresponding values of 0.902 (95% CI, 0.894-0.910) and 0.573 (95% CI, 0.560-0.586), respectively. In the validation cohort, the AUROC (95% CI) of the RF model was 0.950 (95% CI, 0.945-0.956), the AUPRC was 0.605 (95% CI, 0.593-0.616), and the CLR presented corresponding values of 0.815 (95% CI, 0.789-0.841) and 0.586 (95% CI, 0.553-0.619), respectively. We developed a machine learning-driven prediction model for SBI among febrile children, which works robustly despite missing values. And it showed superior performance compared to CLR in both internal validation and external validation.
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Affiliation(s)
- Bongjin Lee
- Department of Pediatrics, Seoul National University Hospital, Seoul, Korea
| | - Hyun Jung Chung
- Department of Emergency Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Korea
| | - Hyun Mi Kang
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Do Kyun Kim
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
| | - Young Ho Kwak
- Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea
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25
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Bishara A, Maze EH, Maze M. Considerations for the implementation of machine learning into acute care settings. Br Med Bull 2022; 141:15-32. [PMID: 35107127 DOI: 10.1093/bmb/ldac001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/01/2022] [Indexed: 11/14/2022]
Abstract
INTRODUCTION Management of patients in the acute care setting requires accurate diagnosis and rapid initiation of validated treatments; therefore, this setting is likely to be an environment in which cognitive augmentation of the clinician's provision of care with technology rooted in artificial intelligence, such as machine learning (ML), is likely to eventuate. SOURCES OF DATA PubMed and Google Scholar with search terms that included ML, intensive/critical care unit, electronic health records (EHR), anesthesia information management systems and clinical decision support were the primary sources for this report. AREAS OF AGREEMENT Different categories of learning of large clinical datasets, often contained in EHRs, are used for training in ML. Supervised learning uses algorithm-based models, including support vector machines, to pair patients' attributes with an expected outcome. Unsupervised learning uses clustering algorithms to define to which disease grouping a patient's attributes most closely approximates. Reinforcement learning algorithms use ongoing environmental feedback to deterministically pursue likely patient outcome. AREAS OF CONTROVERSY Application of ML can result in undesirable outcomes over concerns related to fairness, transparency, privacy and accountability. Whether these ML technologies irrevocably change the healthcare workforce remains unresolved. GROWING POINTS Well-resourced Learning Health Systems are likely to exploit ML technology to gain the fullest benefits for their patients. How these clinical advantages can be extended to patients in health systems that are neither well-endowed, nor have the necessary data gathering technologies, needs to be urgently addressed to avoid further disparities in healthcare.
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Affiliation(s)
- Andrew Bishara
- Department of Anesthesia and Perioperative Care, University of California San Francisco, 1001 Potrero Avenue San Francisco, CA 94110, USA.,Bakar Computational Health Sciences Institute, University of California San Francisco, 490 Illinois Street, San Francisco, CA 94143, USA
| | - Elijah H Maze
- Departments of Computer Science and Mathematics, University of Michigan, Bob and Betty Beyster Building, 2260 Hayward Street Ann Arbor, MI 48109, USA
| | - Mervyn Maze
- Department of Anesthesia and Perioperative Care, University of California San Francisco, 1001 Potrero Avenue San Francisco, CA 94110, USA.,Center for Cerebrovascular Research, Building 10, Zuckerberg San Francisco General, 1001 Potrero Avenue, San Francisco, CA 94110, USA
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26
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Boch S, Hussain SA, Bambach S, DeShetler C, Chisolm D, Linwood S. Locating Youth Exposed to Parental Justice Involvement in the Electronic Health Record: Development of a Natural Language Processing Model. JMIR Pediatr Parent 2022; 5:e33614. [PMID: 35311681 PMCID: PMC8981008 DOI: 10.2196/33614] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/16/2022] [Accepted: 01/25/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Parental justice involvement (eg, prison, jail, parole, or probation) is an unfortunately common and disruptive household adversity for many US youths, disproportionately affecting families of color and rural families. Data on this adversity has not been captured routinely in pediatric health care settings, and if it is, it is not discrete nor able to be readily analyzed for purposes of research. OBJECTIVE In this study, we outline our process training a state-of-the-art natural language processing model using unstructured clinician notes of one large pediatric health system to identify patients who have experienced a justice-involved parent. METHODS Using the electronic health record database of a large Midwestern pediatric hospital-based institution from 2011-2019, we located clinician notes (of any type and written by any type of provider) that were likely to contain such evidence of family justice involvement via a justice-keyword search (eg, prison and jail). To train and validate the model, we used a labeled data set of 7500 clinician notes identifying whether the patient was ever exposed to parental justice involvement. We calculated the precision and recall of the model and compared those rates to the keyword search. RESULTS The development of the machine learning model increased the precision (positive predictive value) of locating children affected by parental justice involvement in the electronic health record from 61% (a simple keyword search) to 92%. CONCLUSIONS The use of machine learning may be a feasible approach to addressing the gaps in our understanding of the health and health services of underrepresented youth who encounter childhood adversities not routinely captured-particularly for children of justice-involved parents.
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Affiliation(s)
- Samantha Boch
- College of Nursing, University of Cincinnati, Cincinnati, OH, United States.,James M Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Syed-Amad Hussain
- IT Research and Innovation, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States
| | - Sven Bambach
- IT Research and Innovation, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States
| | - Cameron DeShetler
- Biomedical Engineering Undergraduate Department, Notre Dame University, Notre Dame, IN, United States
| | - Deena Chisolm
- IT Research and Innovation, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States.,College of Medicine and Public Health, College of Nursing, The Ohio State University, Columbus, OH, United States
| | - Simon Linwood
- Nationwide Children's Hospital, Columbus, OH, United States.,School of Medicine, University of California, Riverside, CA, United States
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27
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Bazoukis G, Hall J, Loscalzo J, Antman EM, Fuster V, Armoundas AA. The inclusion of augmented intelligence in medicine: A framework for successful implementation. Cell Rep Med 2022; 3:100485. [PMID: 35106506 PMCID: PMC8784713 DOI: 10.1016/j.xcrm.2021.100485] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) algorithms are being applied across a large spectrum of everyday life activities. The implementation of AI algorithms in clinical practice has been met with some skepticism and concern, mainly because of the uneasiness that stems, in part, from a lack of understanding of how AI operates, together with the role of physicians and patients in the decision-making process; uncertainties regarding the reliability of the data and the outcomes; as well as concerns regarding the transparency, accountability, liability, handling of personal data, and monitoring and system upgrades. In this viewpoint, we take these issues into consideration and offer an integrated regulatory framework to AI developers, clinicians, researchers, and regulators, aiming to facilitate the adoption of AI that rests within the FDA’s pathway, in research, development, and clinical medicine. Concerns hamper the implementation of augmented intelligence in clinical practice Transparency and external validation increase trust in developed algorithms A regulatory framework is needed for adoption of augmented intelligence in medicine
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Affiliation(s)
| | - Jennifer Hall
- American Heart Association, National Center, Dallas, TX.,Division of Cardiology, Department of Medicine, University of Minnesota, MN
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Boston, MA
| | | | - Valentín Fuster
- Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Antonis A Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology Cambridge, MA
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28
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Eisenberg MA, Balamuth F. Pediatric sepsis screening in US hospitals. Pediatr Res 2022; 91:351-358. [PMID: 34417563 PMCID: PMC8378117 DOI: 10.1038/s41390-021-01708-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/28/2021] [Accepted: 08/04/2021] [Indexed: 11/09/2022]
Abstract
Sepsis is a major cause of morbidity and mortality in children. While adverse outcomes can be reduced through prompt initiation of sepsis protocols including fluid resuscitation and antibiotics, provision of these therapies relies on clinician recognition of sepsis. Recognition is challenging in children because early signs of shock such as tachycardia and tachypnea have low specificity while hypotension often does not occur until late in the clinical course. This narrative review highlights the important context that has led to the rapid growth of pediatric sepsis screening in the United States. In this review, we (1) describe different screening tools used in US emergency department, inpatient, and intensive care unit settings; (2) highlight details of the design, implementation, and evaluation of specific tools; (3) review the available data on the process of integrating sepsis screening into an overall sepsis quality improvement program and on the effect of these screening tools on patient outcomes; (4) discuss potential harms of sepsis screening including alarm fatigue; and (5) highlight several future directions in sepsis screening, such as novel tools that incorporate artificial intelligence and machine learning methods to augment sepsis identification with the ultimate goal of precision-based approaches to sepsis recognition and treatment. IMPACT: This narrative review highlights the context that has led to the rapid growth of pediatric sepsis screening nationally. Screening tools used in US emergency department, inpatient, and intensive care unit settings are described in terms of their design, implementation, and clinical performance. Limitations and potential harms of these tools are highlighted, as well as future directions that may lead to a more precision-based approach to sepsis recognition and treatment.
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Affiliation(s)
- Matthew A. Eisenberg
- grid.38142.3c000000041936754XDepartments of Pediatrics and Emergency Medicine, Harvard Medical School, Boston, MA USA ,grid.2515.30000 0004 0378 8438Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA USA
| | - Fran Balamuth
- grid.25879.310000 0004 1936 8972Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA ,grid.239552.a0000 0001 0680 8770Division of Emergency Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA USA ,grid.239552.a0000 0001 0680 8770Pediatric Sepsis Program, Children’s Hospital of Philadelphia, Philadelphia, PA USA ,grid.239552.a0000 0001 0680 8770Center for Pediatric Clinical Effectiveness, Children’s Hospital of Philadelphia, Philadelphia, PA USA
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29
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Popa IV, Burlacu A, Gavrilescu O, Dranga M, Prelipcean CC, Mihai C. A new approach to predict ulcerative colitis activity through standard clinical–biological parameters using a robust neural network model. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06055-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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30
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Tabaie A, Orenstein EW, Nemati S, Basu RK, Clifford GD, Kamaleswaran R. Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines. Front Pediatr 2021; 9:726870. [PMID: 34604142 PMCID: PMC8480258 DOI: 10.3389/fped.2021.726870] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 08/06/2021] [Indexed: 12/23/2022] Open
Abstract
Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI*), among pediatric patients with Central Venous Lines (CVLs). Design: Retrospective cohort study. Setting: Single academic children's hospital. Patients: All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h. Measurements and Main Results: Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI* during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI* by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence. Conclusion: A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection.
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Affiliation(s)
- Azade Tabaie
- Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, United States
| | - Evan W. Orenstein
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, United States
| | - Shamim Nemati
- Department of Biomedical Informatics, University of California, San Diego, San Diego, CA, United States
| | - Rajit K. Basu
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, United States
| | - Gari D. Clifford
- Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, United States
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, United States
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, United States
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, United States
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Prolonged hospital length of stay in pediatric trauma: a model for targeted interventions. Pediatr Res 2021; 90:464-471. [PMID: 33184499 DOI: 10.1038/s41390-020-01237-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 09/17/2020] [Accepted: 10/11/2020] [Indexed: 11/08/2022]
Abstract
BACKGROUND In this study, trauma-specific risk factors of prolonged length of stay (LOS) in pediatric trauma were examined. Statistical and machine learning models were used to proffer ways to improve the quality of care of patients at risk of prolonged length of stay and reduce cost. METHODS Data from 27 hospitals were retrieved on 81,929 hospitalizations of pediatric patients with a primary diagnosis of trauma, and for which the LOS was >24 h. Nested mixed effects model was used for simplified statistical inference, while a stochastic gradient boosting model, considering high-order statistical interactions, was built for prediction. RESULTS Over 18.7% of the encounters had LOS >1 week. Burns and corrosion and suspected and confirmed child abuse are the strongest drivers of prolonged LOS. Several other trauma-specific and general pediatric clinical variables were also predictors of prolonged LOS. The stochastic gradient model obtained an area under the receiver operator characteristic curve of 0.912 (0.907, 0.917). CONCLUSIONS The high performance of the machine learning model coupled with statistical inference from the mixed effects model provide an opportunity for targeted interventions to improve quality of care of trauma patients likely to require long length of stay. IMPACT Targeted interventions on high-risk patients would improve the quality of care of pediatric trauma patients and reduce the length of stay. This comprehensive study includes data from multiple hospitals analyzed with advanced statistical and machine learning models. The statistical and machine learning models provide opportunities for targeted interventions and reduction in prolonged length of stay reducing the burden of hospitalization on families.
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Badke CM, Marsillio LE, Carroll MS, Weese-Mayer DE, Sanchez-Pinto LN. Development of a Heart Rate Variability Risk Score to Predict Organ Dysfunction and Death in Critically Ill Children. Pediatr Crit Care Med 2021; 22:e437-e447. [PMID: 33710071 DOI: 10.1097/pcc.0000000000002707] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
OBJECTIVES Determine whether the Heart Rate Variability Dysfunction score, a novel age-normalized measure of autonomic nervous system dysregulation, is associated with the development of new or progressive multiple organ dysfunction syndrome or death in critically ill children. DESIGN, SETTING, AND PATIENTS This was a retrospective, observational cohort study from 2012 to 2018. Patients admitted to the PICU with at least 12 hours of continuous heart rate data available from bedside monitors during the first 24 hours of admission were included in the analysis. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Heart rate variability was measured using the integer heart rate variability, which is the sd of the heart rate sampled every 1 second over 5 consecutive minutes. The Heart Rate Variability Dysfunction score was derived from age-normalized values of integer heart rate variability and transformed, so that higher scores were indicative of lower integer heart rate variability and a proxy for worsening autonomic nervous system dysregulation. Heart Rate Variability Dysfunction score performance as a predictor of new or progressive multiple organ dysfunction syndrome and 28-day mortality were determined using the area under the receiver operating characteristic curve. Of the 7,223 patients who met inclusion criteria, 346 patients (4.8%) developed new or progressive multiple organ dysfunction syndrome, and 103 (1.4%) died by day 28. For every one-point increase in the median Heart Rate Variability Dysfunction score in the first 24 hours of admission, there was a 25% increase in the odds of new or progressive multiple organ dysfunction syndrome and a 51% increase in the odds of mortality. The median Heart Rate Variability Dysfunction score in the first 24 hours had an area under the receiver operating characteristic curve to discriminate new or progressive multiple organ dysfunction syndrome of 0.67 and to discriminate mortality of 0.80. These results were reproducible in a temporal validation cohort. CONCLUSIONS The Heart Rate Variability Dysfunction score, an age-adjusted proxy for autonomic nervous system dysregulation derived from bedside monitor data is independently associated with new or progressive multiple organ dysfunction syndrome and mortality in PICU patients. The Heart Rate Variability Dysfunction score could potentially be used as a single continuous physiologic biomarker or as part of a multivariable prediction model to increase awareness of at-risk patients and augment clinical decision-making.
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Affiliation(s)
- Colleen M Badke
- Division of Critical Care Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL
- Stanley Manne Children's Research Institute, Chicago, IL
- Data Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
- Division of Autonomic Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
| | - Lauren E Marsillio
- Division of Critical Care Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL
- Stanley Manne Children's Research Institute, Chicago, IL
- Data Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
- Division of Autonomic Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
| | - Michael S Carroll
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL
- Data Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
| | - Debra E Weese-Mayer
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL
- Stanley Manne Children's Research Institute, Chicago, IL
- Division of Autonomic Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
| | - L Nelson Sanchez-Pinto
- Division of Critical Care Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL
- Stanley Manne Children's Research Institute, Chicago, IL
- Data Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
- Division of Autonomic Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
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Improvement of 1st-hour bundle compliance and sepsis mortality in pediatrics after the implementation of the surviving sepsis campaign guidelines. J Pediatr (Rio J) 2021; 97:459-467. [PMID: 33121929 PMCID: PMC9432151 DOI: 10.1016/j.jped.2020.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 09/13/2020] [Accepted: 09/14/2020] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVES To study the impact of the implementation of the Pediatric Surviving Sepsis Campaign protocol on early recognition of sepsis, 1-h treatment bundle and mortality. METHODS Retrospective, single-center study, before and after the implementation of the sepsis protocol. OUTCOMES sepsis recognition, compliance with the 1-h bundle (fluid resuscitation, blood culture, antibiotics), time interval to fluid resuscitation and antibiotics administration, and mortality. Patients with febrile neutropenia were excluded. The comparisons between the periods were performed using non-parametric tests and odds ratios or relative risk were calculated. RESULTS We studied 84 patients before and 103 after the protocol implementation. There was an increase in sepsis recognition (OR 21.5 [95% CI: 10.1-45.7]), in the compliance with the 1-h bundle as a whole (62% x 0%), and with its three components: fluid resuscitation (OR 31.1 [95% CI: 3.9-247.2]), blood culture (OR 15.9 [95% CI: 3.9-65.2]), and antibiotics (OR 35.6 [95% CI: 8.9-143.2]). Significant reduction between sepsis recognition to fluid resuscitation (152min×12min, p<0.001) and to antibiotics administration (137min×30min) also occurred. The risk of death before protocol implementation was four times greater (RR 4.1 [95% CI: 1.2-14.4]), and the absolute death risk reduction was 9%. CONCLUSION Even if we considered the low precision of some estimates, the lower limits of the Confidence Intervals show that the implementation of the Pediatric Surviving Sepsis Campaign guidelines alongside a qualitive assurance initiative has led to improvements in sepsis recognition, compliance with the 1-h treatment bundle, reduction in the time interval to fluid resuscitation and antibiotics, and reduction in sepsis mortality.
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Mohammed A, Van Wyk F, Chinthala LK, Khojandi A, Davis RL, Coopersmith CM, Kamaleswaran R. Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults. Shock 2021; 56:58-64. [PMID: 32991797 PMCID: PMC8352046 DOI: 10.1097/shk.0000000000001670] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a minimal set of streaming physiological data in real time. METHODS AND FINDINGS A total of 29,552 adult patients were admitted to the intensive care unit across five regional hospitals in Memphis, Tenn, over 18 months from January 2017 to July 2018. From these, 5,958 patients were selected after filtering for continuous (minute-by-minute) physiological data availability. A total of 617 (10.4%) patients were identified as sepsis cases, using the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteria. Physiomarkers, a set of signal processing features, were derived from five physiological data streams including heart rate, respiratory rate, and blood pressure (systolic, diastolic, and mean), captured every minute from the bedside monitors. A support vector machine classifier was used for classification. The model accurately predicted sepsis up to a mean and 95% confidence interval of 17.4 ± 0.22 h before sepsis onset, with an average test accuracy of 83.0% (average sensitivity, specificity, and area under the receiver operating characteristics curve of 0.757, 0.902, and 0.781, respectively). CONCLUSIONS This study demonstrates that salient physiomarkers derived from continuous bedside monitoring are temporally and differentially expressed in septic patients. Using this information, minimalistic artificial intelligence models can be developed to predict sepsis earlier in critically ill patients.
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Affiliation(s)
- Akram Mohammed
- University of Tennessee Health Science Center, Memphis TN
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Wang Z, Yao B. Multi-Branching Temporal Convolutional Network for Sepsis Prediction. IEEE J Biomed Health Inform 2021; 26:876-887. [PMID: 34181558 DOI: 10.1109/jbhi.2021.3092835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Sepsis is among the leading causes of morbidity and mortality in modern intensive care units. Accurate sepsis prediction is of critical importance to save lives and reduce medical costs. The rapid advancements in sensing and information technology facilitate the effective monitoring of patients health conditions, generating a wealth of medical data, and provide an unprecedented opportunity for data-driven diagnosis of sepsis. However, real-world medical data are often complexly structured with a high level of uncertainty (e.g., missing values, imbalanced data). Realizing the full data potential depends on developing effective analytical models. In this paper, we propose a novel predictive framework with Multi-Branching Temporal Convolutional Network (MB-TCN) to model the complexly structured medical data for robust prediction of sepsis. The MB-TCN framework not only efficiently handles the missing value and imbalanced data issues but also effectively captures the temporal pattern and heterogeneous variable interactions. We evaluate the performance of the proposed MB-TCN in predicting sepsis using real-world medical data from PhysioNet/Computing in Cardiology Challenge 2019. Experimental results show that MB-TCN outperforms existing methods that are commonly used in current practice.
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Banerjee R, Shah N, Dicker AP. Next-Generation Implementation of Chimeric Antigen Receptor T-Cell Therapy Using Digital Health. JCO Clin Cancer Inform 2021; 5:668-678. [PMID: 34110929 DOI: 10.1200/cci.21.00023] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Chimeric antigen receptor T-cell (CAR-T) therapy is a paradigm-shifting immunotherapy modality in oncology; however, unique toxicities such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome limit its ability to be implemented more widely in the outpatient setting or at smaller-volume centers. Three operational challenges with CAR-T therapy include the following: (1) the logistics of toxicity monitoring, ie, with frequent vital sign checks and neurologic assessments; (2) the specialized knowledge required for toxicity management, particularly with regard to CRS and immune effector cell-associated neurotoxicity syndrome; and (3) the need for high-quality symptomatic and supportive care during this intensive period. In this review, we explore potential niches for digital innovations that can improve the implementation of CAR-T therapy in each of these domains. These tools include patient-facing technologies and provider-facing platforms: for example, wearable devices and mobile health apps to screen for fevers and encephalopathy, electronic patient-reported outcome assessments-based workflows to assist with symptom management, machine learning algorithms to predict emerging CRS in real time, clinical decision support systems to assist with toxicity management, and digital coaching to help maintain wellness. Televisits, which have grown in prominence since the novel coronavirus pandemic, will continue to play a key role in the monitoring and management of CAR-T-related toxicities as well. Limitations of these strategies include the need to ensure care equity and stakeholder buy-in, both operationally and financially. Nevertheless, once developed and validated, the next-generation implementation of CAR-T therapy using these digital tools may improve both its safety and accessibility.
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Affiliation(s)
- Rahul Banerjee
- Division of Hematology/Oncology, Department of Medicine, University of California San Francisco, San Francisco, CA
| | - Nina Shah
- Division of Hematology/Oncology, Department of Medicine, University of California San Francisco, San Francisco, CA
| | - Adam P Dicker
- Department of Radiation Oncology, Jefferson University, Philadelphia, PA.,Jefferson Center for Digital Health, Jefferson University, Philadelphia, PA
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Huang B, Wang R, Masino AJ, Obstfeld AE. Aiding clinical assessment of neonatal sepsis using hematological analyzer data with machine learning techniques. Int J Lab Hematol 2021; 43:1341-1356. [PMID: 33949115 DOI: 10.1111/ijlh.13549] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/14/2021] [Accepted: 03/29/2021] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Early diagnosis and antibiotic administration are essential for reducing sepsis morbidity and mortality; however, diagnosis remains difficult due to complex pathogenesis and presentation. We created a machine learning model for bacterial sepsis identification in the neonatal intensive care unit (NICU) using hematological analyzer data. METHODS Hematological analyzer data were gathered from NICU patients up to 48 hours prior to clinical evaluation for bacterial sepsis. Five models, Support Vector Machine, K-nearest-neighbors, Logistic Regression, Random Forest (RF), and Extreme Gradient boosting (XGBoost), were trained on 60 hematological and nine clinical variables for 2357 cases (1692 control, 665 septic). Clinical feature only models (nine variables) were additionally trained and compared with models including hematological variables. Feature importance was used to assess relative contributions of parameters to performance. RESULTS The three best performing models were RF, Logistic Regression, and XGBoost. RF achieved an average accuracy of 0.74, AUC-ROC of 0.73, Sensitivity of 0.38, and Specificity of 0.88. Logistic Regression achieved an average accuracy of 0.70, AUC-ROC of 0.74, Sensitivity of 0.62, and Specificity of 0.73. XGBoost achieved an average accuracy of 0.72, AUC-ROC of 0.71, Sensitivity of 0.40, and Specificity of 0.85. All models with hematological variables had significantly stronger performance than models trained on only clinical features. Neutrophil parameters had the highest average feature importance. CONCLUSIONS Machine learning models using hematological analyzer data can classify NICU patients as sepsis positive or negative with stronger performance compared to clinical feature only models. Hematological analyzer variables could augment current sepsis classification machine learning algorithms.
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Affiliation(s)
- Brian Huang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin Wang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aaron J Masino
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Amrom E Obstfeld
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
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Tabaie A, Orenstein EW, Nemati S, Basu RK, Kandaswamy S, Clifford GD, Kamaleswaran R. Predicting presumed serious infection among hospitalized children on central venous lines with machine learning. Comput Biol Med 2021; 132:104289. [PMID: 33667812 PMCID: PMC9207586 DOI: 10.1016/j.compbiomed.2021.104289] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/29/2021] [Accepted: 02/14/2021] [Indexed: 01/28/2023]
Abstract
BACKGROUND Presumed serious infection (PSI) is defined as a blood culture drawn and new antibiotic course of at least 4 days among pediatric patients with Central Venous Lines (CVLs). Early PSI prediction and use of medical interventions can prevent adverse outcomes and improve the quality of care. METHODS Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train machine learning models (XGBoost and ElasticNet) to predict the occurrence of PSI 8 h prior to clinical suspicion. Prediction for PSI was benchmarked against PRISM-III. RESULTS Our model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI = [0.82, 0.85]), sensitivity of 0.73 [0.69, 0.74], and positive predictive value (PPV) of 0.36 [0.34, 0.36]. The PRISM-III conversely achieved a lower sensitivity of 0.19 [0.16, 0.22] and PPV of 0.30 [0.26, 0.34] at a cut-off of ≥ 10. The features with the most impact on the PSI prediction were maximum diastolic blood pressure prior to PSI prediction (mean SHAP = 3.4), height (mean SHAP = 3.2), and maximum temperature prior to PSI prediction (mean SHAP = 2.6). CONCLUSION A machine learning model using common features in the electronic medical records can predict the onset of serious infections in children with central venous lines at least 8 h prior to when a clinical team drew a blood culture.
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Affiliation(s)
- Azade Tabaie
- Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA.
| | - Evan W Orenstein
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Shamim Nemati
- Department of Biomedical Informatics, University of California San Diego, San Diego, CA, USA
| | - Rajit K Basu
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | | | - Gari D Clifford
- Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA; Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA; Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, USA
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Guan Y, Wang X, Chen X, Yi D, Chen L, Jiang X. Assessment of the timeliness and robustness for predicting adult sepsis. iScience 2021; 24:102106. [PMID: 33659874 PMCID: PMC7895752 DOI: 10.1016/j.isci.2021.102106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 01/09/2021] [Accepted: 01/21/2021] [Indexed: 02/07/2023] Open
Abstract
Sepsis is a leading cause of death among inpatients at hospitals. However, with early detection, death rate can drop substantially. In this study, we present the top-performing algorithm for Sepsis II prediction in the DII National Data Science Challenge using the Cerner Health Facts data involving more than 100,000 adult patients. This large sample size allowed us to dissect the predictability by age-groups, race, genders, and care settings and up to 192 hr of sepsis onset. This large data collection also allowed us to conclude that the last six biometric records on average are informative to the prediction of sepsis. We identified biomarkers that are common across the treatment time and novel biomarkers that are uniquely presented for early prediction. The algorithms showed meaningful signals days ahead of sepsis onset, supporting the potential of reducing death rate by focusing on high-risk populations identified from heterogeneous data integration.
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Affiliation(s)
- Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Xueqing Wang
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Xianghao Chen
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Daiyao Yi
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Luyao Chen
- UTHealth School of Biomedical Informatics (SBMI), University of Texas, Houston, TX, USA
| | - Xiaoqian Jiang
- UTHealth School of Biomedical Informatics (SBMI), University of Texas, Houston, TX, USA
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Wulff A, Montag S, Rübsamen N, Dziuba F, Marschollek M, Beerbaum P, Karch A, Jack T. Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children. BMC Med Inform Decis Mak 2021; 21:62. [PMID: 33602206 PMCID: PMC7889709 DOI: 10.1186/s12911-021-01428-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/03/2021] [Indexed: 11/11/2022] Open
Abstract
Background Systemic inflammatory response syndrome (SIRS) is defined as a non-specific inflammatory process in the absence of infection. SIRS increases susceptibility for organ dysfunction, and frequently affects the clinical outcome of affected patients. We evaluated a knowledge-based, interoperable clinical decision-support system (CDSS) for SIRS detection on a pediatric intensive care unit (PICU). Methods The CDSS developed retrieves routine data, previously transformed into an interoperable format, by using model-based queries and guideline- and knowledge-based rules. We evaluated the CDSS in a prospective diagnostic study from 08/2018–03/2019. 168 patients from a pediatric intensive care unit of a tertiary university hospital, aged 0 to 18 years, were assessed for SIRS by the CDSS and by physicians during clinical routine. Sensitivity and specificity (when compared to the reference standard) with 95% Wald confidence intervals (CI) were estimated on the level of patients and patient-days. Results Sensitivity and specificity was 91.7% (95% CI 85.5–95.4%) and 54.1% (95% CI 45.4–62.5%) on patient level, and 97.5% (95% CI 95.1–98.7%) and 91.5% (95% CI 89.3–93.3%) on the level of patient-days. Physicians’ SIRS recognition during clinical routine was considerably less accurate (sensitivity of 62.0% (95% CI 56.8–66.9%)/specificity of 83.3% (95% CI 80.4–85.9%)) when measurd on the level of patient-days. Evaluation revealed valuable insights for the general design of the CDSS as well as specific rule modifications. Despite a lower than expected specificity, diagnostic accuracy was higher than the one in daily routine ratings, thus, demonstrating high potentials of using our CDSS to help to detect SIRS in clinical routine. Conclusions We successfully evaluated an interoperable CDSS for SIRS detection in PICU. Our study demonstrated the general feasibility and potentials of the implemented algorithms but also some limitations. In the next step, the CDSS will be optimized to overcome these limitations and will be evaluated in a multi-center study. Trial registration: NCT03661450 (ClinicalTrials.gov); registered September 7, 2018. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01428-7.
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Affiliation(s)
- Antje Wulff
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625, Hannover, Germany.
| | - Sara Montag
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625, Hannover, Germany.
| | - Nicole Rübsamen
- Institute of Epidemiology and Social Medicine, University of Muenster, Domagkstr. 3, 48149, Muenster, Germany
| | - Friederike Dziuba
- Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Michael Marschollek
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625, Hannover, Germany
| | - Philipp Beerbaum
- Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Muenster, Domagkstr. 3, 48149, Muenster, Germany
| | - Thomas Jack
- Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
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Giacobbe DR, Signori A, Del Puente F, Mora S, Carmisciano L, Briano F, Vena A, Ball L, Robba C, Pelosi P, Giacomini M, Bassetti M. Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective. Front Med (Lausanne) 2021; 8:617486. [PMID: 33644097 PMCID: PMC7906970 DOI: 10.3389/fmed.2021.617486] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/19/2021] [Indexed: 12/15/2022] Open
Abstract
Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Alessio Signori
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Filippo Del Puente
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Sara Mora
- Department of Informatics Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Luca Carmisciano
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Federica Briano
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Antonio Vena
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
| | - Lorenzo Ball
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Chiara Robba
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Paolo Pelosi
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Mauro Giacomini
- Department of Informatics Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
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42
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Harrison WN, Workman JK, Bonafide CP, Lockwood JM. Surviving Sepsis Screening: The Unintended Consequences of Continuous Surveillance. Hosp Pediatr 2020; 10:e14-e17. [PMID: 33184126 DOI: 10.1542/hpeds.2020-002121] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Wade N Harrison
- Pediatric Residency Program and Divisions of Pediatric Inpatient Medicine and .,Division of Hospital Pediatrics, Department of Pediatrics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Jennifer K Workman
- Critical Care Medicine, Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, Utah
| | - Christopher P Bonafide
- Section of Pediatric Hospital Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; and
| | - Justin M Lockwood
- Division of Hospital Medicine, Department of Pediatrics, School of Medicine, University of Colorado, Aurora, Colorado
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Burdick H, Pino E, Gabel-Comeau D, Gu C, Roberts J, Le S, Slote J, Saber N, Pellegrini E, Green-Saxena A, Hoffman J, Das R. Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals. BMC Med Inform Decis Mak 2020; 20:276. [PMID: 33109167 PMCID: PMC7590695 DOI: 10.1186/s12911-020-01284-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 10/08/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset. METHODS Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset. RESULTS 270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset. CONCLUSIONS The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.
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Affiliation(s)
- Hoyt Burdick
- Cabell Huntington Hospital, Huntington, WV, USA
- Marshall University School of Medicine, Huntington, WV, USA
| | - Eduardo Pino
- Cabell Huntington Hospital, Huntington, WV, USA
- Marshall University School of Medicine, Huntington, WV, USA
| | | | - Carol Gu
- Dascena, Inc., P.O. Box 156572, San Francisco, CA, 94115, USA
| | | | - Sidney Le
- Dascena, Inc., P.O. Box 156572, San Francisco, CA, 94115, USA
| | - Joseph Slote
- Dascena, Inc., P.O. Box 156572, San Francisco, CA, 94115, USA
| | - Nicholas Saber
- Dascena, Inc., P.O. Box 156572, San Francisco, CA, 94115, USA
| | | | | | - Jana Hoffman
- Dascena, Inc., P.O. Box 156572, San Francisco, CA, 94115, USA
| | - Ritankar Das
- Dascena, Inc., P.O. Box 156572, San Francisco, CA, 94115, USA
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Chicco D, Jurman G. Survival prediction of patients with sepsis from age, sex, and septic episode number alone. Sci Rep 2020; 10:17156. [PMID: 33051513 PMCID: PMC7555553 DOI: 10.1038/s41598-020-73558-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 09/15/2020] [Indexed: 12/14/2022] Open
Abstract
Sepsis is a life-threatening condition caused by an exaggerated reaction of the body to an infection, that leads to organ failure or even death. Since sepsis can kill a patient even in just one hour, survival prediction is an urgent priority among the medical community: even if laboratory tests and hospital analyses can provide insightful information about the patient, in fact, they might not come in time to allow medical doctors to recognize an immediate death risk and treat it properly. In this context, machine learning can be useful to predict survival of patients within minutes, especially when applied to few medical features easily retrievable. In this study, we show that it is possible to achieve this goal by applying computational intelligence algorithms to three features of patients with sepsis, recorded at hospital admission: sex, age, and septic episode number. We applied several data mining methods to a cohort of 110,204 admissions of patients, and obtained high prediction scores both on this complete dataset (top precision-recall area under the curve PR AUC = 0.966) and on its subset related to the recent Sepsis-3 definition (top PR AUC = 0.860). Additionally, we tested our models on an external validation cohort of 137 patients, and achieved good results in this case too (top PR AUC = 0.863), confirming the generalizability of our approach. Our results can have a huge impact on clinical settings, allowing physicians to forecast the survival of patients by sex, age, and septic episode number alone.
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Qu C, Gao L, Yu XQ, Wei M, Fang GQ, He J, Cao LX, Ke L, Tong ZH, Li WQ. Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients. Gastroenterol Res Pract 2020; 2020:3431290. [PMID: 33061958 PMCID: PMC7542489 DOI: 10.1155/2020/3431290] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/19/2020] [Accepted: 09/06/2020] [Indexed: 12/20/2022] Open
Abstract
Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.
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Affiliation(s)
- Cheng Qu
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Lin Gao
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xian-qiang Yu
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Clinical Medical College of Southeast University, Nanjing, China
| | - Mei Wei
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Guo-quan Fang
- Electrical Engineering School of Southeast University, China
| | - Jianing He
- Institute for Hospital Management of Tsinghua University, Shenzhen, China
| | - Long-xiang Cao
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Lu Ke
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Zhi-hui Tong
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wei-qin Li
- Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
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Shah N, Farhat A, Tweed J, Wang Z, Lee J, McBeth R, Skinner M, Tian F, Thiagarajan R, Raman L. Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation. J Clin Med 2020; 9:jcm9092718. [PMID: 32842683 PMCID: PMC7565544 DOI: 10.3390/jcm9092718] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 08/07/2020] [Accepted: 08/19/2020] [Indexed: 01/03/2023] Open
Abstract
Brain injury is a significant source of morbidity and mortality for pediatric patients treated with Extracorporeal Membrane Oxygenation (ECMO). Our objective was to utilize neural networks to predict radiographic evidence of brain injury in pediatric ECMO-supported patients and identify specific variables that can be explored for future research. Data from 174 ECMO-supported patients were collected up to 24 h prior to, and for the duration of, the ECMO course. Thirty-five variables were collected, including physiological data, markers of end-organ perfusion, acid-base homeostasis, vasoactive infusions, markers of coagulation, and ECMO-machine factors. The primary outcome was the presence of radiologic evidence of moderate to severe brain injury as established by brain CT or MRI. This information was analyzed by a neural network, and results were compared to a logistic regression model as well as clinician judgement. The neural network model was able to predict brain injury with an Area Under the Curve (AUC) of 0.76, 73% sensitivity, and 80% specificity. Logistic regression had 62% sensitivity and 61% specificity. Clinician judgment had 39% sensitivity and 69% specificity. Sequential feature group masking demonstrated a relatively greater contribution of physiological data and minor contribution of coagulation factors to the model's performance. These findings lay the foundation for further areas of research directions.
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Affiliation(s)
- Neel Shah
- Department of Pediatrics, Division of Pediatric Critical Care, Washington University School of Medicine, St. Louis, MO 63110, USA;
| | - Abdelaziz Farhat
- Department of Pediatrics, Pediatrix Medical Group, Orem, UT 84057, USA;
| | | | - Ziheng Wang
- Department of Mechanical Engineering, The University of Texas at Dallas, Dallas, TX 75080, USA;
| | - Jeon Lee
- Department of Bioinformatics, University of Texas Southwestern, Dallas, TX 75390, USA;
| | - Rafe McBeth
- Department of Radiation Oncology, University of Texas Southwestern, Dallas, TX 75390, USA;
| | - Michael Skinner
- Department of Computer Science, The University of Texas at Dallas, Dallas, TX 75080, USA;
| | - Fenghua Tian
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76019, USA;
| | - Ravi Thiagarajan
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA;
| | - Lakshmi Raman
- Children’s Health Dallas, Dallas, TX 75201, USA;
- Department of Pediatrics, Division of Pediatric Critical Care, University of Texas Southwestern, Dallas, TX 75390, USA
- Correspondence:
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Bazoukis G, Stavrakis S, Zhou J, Bollepalli SC, Tse G, Zhang Q, Singh JP, Armoundas AA. Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review. Heart Fail Rev 2020; 26:23-34. [PMID: 32720083 PMCID: PMC7384870 DOI: 10.1007/s10741-020-10007-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Machine learning (ML) algorithms “learn” information directly from data, and their performance improves proportionally with the number of high-quality samples. The aim of our systematic review is to present the state of the art regarding the implementation of ML techniques in the management of heart failure (HF) patients. We manually searched MEDLINE and Cochrane databases as well the reference lists of the relevant review studies and included studies. Our search retrieved 122 relevant studies. These studies mainly refer to (a) the role of ML in the classification of HF patients into distinct categories which may require a different treatment strategy, (b) discrimination of HF patients from the healthy population or other diseases, (c) prediction of HF outcomes, (d) identification of HF patients from electronic records and identification of HF patients with similar characteristics who may benefit form a similar treatment strategy, (e) supporting the extraction of important data from clinical notes, and (f) prediction of outcomes in HF populations with implantable devices (left ventricular assist device, cardiac resynchronization therapy). We concluded that ML techniques may play an important role for the efficient construction of methodologies for diagnosis, management, and prediction of outcomes in HF patients.
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Affiliation(s)
- George Bazoukis
- Second Department of Cardiology, Evangelismos General Hospital of Athens, Athens, Greece
| | - Stavros Stavrakis
- University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Jiandong Zhou
- School of Data Science, City University of Hong Kong, Hong Kong, China
- Shenzhen Research Institute of City University of Hong Kong, Shenzhen, Guangdong, China
| | - Sandeep Chandra Bollepalli
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th Street, Charlestown, Boston, MA, 02129, USA
| | - Gary Tse
- Laboratory of Cardiovascular Physiology, Li Ka Shing Institute of Health Sciences, Hong Kong SAR, People's Republic of China
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong, China
- Shenzhen Research Institute of City University of Hong Kong, Shenzhen, Guangdong, China
| | - Jagmeet P Singh
- Cardiology Division, Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA
| | - Antonis A Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th Street, Charlestown, Boston, MA, 02129, USA.
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology Cambridge, Cambridge, MA, USA.
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Abstract
PURPOSE OF REVIEW Acute care technologies, including novel monitoring devices, big data, increased computing capabilities, machine-learning algorithms and automation, are converging. This enables the application of augmented intelligence for improved outcome predictions, clinical decision-making, and offers unprecedented opportunities to improve patient outcomes, reduce costs, and improve clinician workflow. This article briefly explores recent work in the areas of automation, artificial intelligence and outcome prediction models in pediatric anesthesia and pediatric critical care. RECENT FINDINGS Recent years have yielded little published research into pediatric physiological closed loop control (a type of automation) beyond studies focused on glycemic control for type 1 diabetes. However, there has been a greater range of research in augmented decision-making, leveraging artificial intelligence and machine-learning techniques, in particular, for pediatric ICU outcome prediction. SUMMARY Most studies focusing on artificial intelligence demonstrate good performance on prediction or classification, whether they use traditional statistical tools or novel machine-learning approaches. Yet the challenges of implementation, user acceptance, ethics and regulation cannot be underestimated. Areas in which there is easy access to routinely labeled data and robust outcomes, such as those collected through national networks and quality improvement programs, are likely to be at the forefront of the adoption of these advances.
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