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Garza-Frias E, Kaviani P, Karout L, Fahimi R, Hosseini S, Putha P, Tadepalli M, Kiran S, Arora C, Robert D, Bizzo B, Dreyer KJ, Kalra MK, Digumarthy SR. Early Detection of Heart Failure with Autonomous AI-Based Model Using Chest Radiographs: A Multicenter Study. Diagnostics (Basel) 2024; 14:1635. [PMID: 39125511 PMCID: PMC11311468 DOI: 10.3390/diagnostics14151635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 06/18/2024] [Indexed: 08/12/2024] Open
Abstract
The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77-0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (<3 months: 0.85; 4-6 months: 0.82; 7-9 months: 0.75; 10-12 months: 0.71), accuracy (0.68-0.72), and specificity (0.68) remained stable. Our results support the ongoing investigation efforts for opportunistic screening in radiology.
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Affiliation(s)
- Emiliano Garza-Frias
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (P.K.)
- Mass General Brigham Data Science Office, Boston, MA 02114, USA
| | - Parisa Kaviani
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (P.K.)
- Mass General Brigham Data Science Office, Boston, MA 02114, USA
| | - Lina Karout
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (P.K.)
- Mass General Brigham Data Science Office, Boston, MA 02114, USA
| | - Roshan Fahimi
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (P.K.)
- Mass General Brigham Data Science Office, Boston, MA 02114, USA
| | - Seyedehelaheh Hosseini
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (P.K.)
- Mass General Brigham Data Science Office, Boston, MA 02114, USA
| | | | | | - Sai Kiran
- Qure AI, Mumbai 400063, India (C.A.)
| | | | | | - Bernardo Bizzo
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (P.K.)
- Mass General Brigham Data Science Office, Boston, MA 02114, USA
| | - Keith J. Dreyer
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (P.K.)
- Mass General Brigham Data Science Office, Boston, MA 02114, USA
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (P.K.)
- Mass General Brigham Data Science Office, Boston, MA 02114, USA
| | - Subba R. Digumarthy
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (P.K.)
- Mass General Brigham Data Science Office, Boston, MA 02114, USA
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Jahangiri S, Abdollahi M, Rashedi E, Azadeh-Fard N. A machine learning model to predict heart failure readmission: toward optimal feature set. Front Artif Intell 2024; 7:1363226. [PMID: 38449791 PMCID: PMC10915081 DOI: 10.3389/frai.2024.1363226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Background Hospital readmissions for heart failure patients remain high despite efforts to reduce them. Predictive modeling using big data provides opportunities to identify high-risk patients and inform care management. However, large datasets can constrain performance. Objective This study aimed to develop a machine learning based prediction model leveraging a nationwide hospitalization database to predict 30-day heart failure readmissions. Another objective of this study is to find the optimal feature set that leads to the highest AUC value in the prediction model. Material and methods Heart failure patient data was extracted from the 2020 Nationwide Readmissions Database. A heuristic feature selection process incrementally incorporated predictors into logistic regression and random forest models, which yields a maximum increase in the AUC metric. Discrimination was evaluated through accuracy, sensitivity, specificity and AUC. Results A total of 566,019 discharges with heart failure diagnosis were recognized. Readmission rate was 8.9% for same-cause and 20.6% for all-cause diagnoses. Random forest outperformed logistic regression, achieving AUCs of 0.607 and 0.576 for same-cause and all-cause readmissions respectively. Heuristic feature selection resulted in the identification of optimal feature sets including 20 and 22 variables from a pool of 30 and 31 features for the same-cause and all-cause datasets. Key predictors included age, payment method, chronic kidney disease, disposition status, number of ICD-10-CM diagnoses, and post-care encounters. Conclusion The proposed model attained discrimination comparable to prior analyses that used smaller datasets. However, reducing the sample enhanced performance, indicating big data complexity. Improved techniques like heuristic feature selection enabled effective leveraging of the nationwide data. This study provides meaningful insights into predictive modeling methodologies and influential features for forecasting heart failure readmissions.
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Affiliation(s)
- Sonia Jahangiri
- Industrial and Systems Engineering Department, Rochester Institute of Technology, Rochester, NY, United States
| | - Masoud Abdollahi
- Industrial and Systems Engineering Department, Rochester Institute of Technology, Rochester, NY, United States
| | - Ehsan Rashedi
- Industrial and Systems Engineering Department, Rochester Institute of Technology, Rochester, NY, United States
| | - Nasibeh Azadeh-Fard
- Industrial and Systems Engineering Department, Rochester Institute of Technology, Rochester, NY, United States
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Yoon M, Park JJ, Hur T, Hua CH, Hussain M, Lee S, Choi DJ. Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future. INTERNATIONAL JOURNAL OF HEART FAILURE 2024; 6:11-19. [PMID: 38303917 PMCID: PMC10827704 DOI: 10.36628/ijhf.2023.0050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 02/03/2024]
Abstract
The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.
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Affiliation(s)
- Minjae Yoon
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jin Joo Park
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Taeho Hur
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Cam-Hao Hua
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Musarrat Hussain
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Sungyoung Lee
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Dong-Ju Choi
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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Zhang Q, Xu D. Applications of deep learning models in precision prediction of survival rates for heart failure patients. Technol Health Care 2024; 32:329-337. [PMID: 38759059 PMCID: PMC11191484 DOI: 10.3233/thc-248029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
Abstract
BACKGROUND Heart failure poses a significant challenge in the global health domain, and accurate prediction of mortality is crucial for devising effective treatment plans. In this study, we employed a Seq2Seq model from deep learning, integrating 12 patient features. By finely modeling continuous medical records, we successfully enhanced the accuracy of mortality prediction. OBJECTIVE The objective of this research was to leverage the Seq2Seq model in conjunction with patient features for precise mortality prediction in heart failure cases, surpassing the performance of traditional machine learning methods. METHODS The study utilized a Seq2Seq model in deep learning, incorporating 12 patient features, to intricately model continuous medical records. The experimental design aimed to compare the performance of Seq2Seq with traditional machine learning methods in predicting mortality rates. RESULTS The experimental results demonstrated that the Seq2Seq model outperformed conventional machine learning methods in terms of predictive accuracy. Feature importance analysis provided critical patient risk factors, offering robust support for formulating personalized treatment plans. CONCLUSIONS This research sheds light on the significant applications of deep learning, specifically the Seq2Seq model, in enhancing the precision of mortality prediction in heart failure cases. The findings present a valuable direction for the application of deep learning in the medical field and provide crucial insights for future research and clinical practices.
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Affiliation(s)
- Qiaohui Zhang
- Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian, China
| | - Demin Xu
- Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian, China
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Rizinde T, Ngaruye I, Cahill ND. Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda. J Pers Med 2023; 13:1393. [PMID: 37763160 PMCID: PMC10532623 DOI: 10.3390/jpm13091393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/22/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
High rates of hospital readmission and the cost of treating heart failure (HF) are significant public health issues globally and in Rwanda. Using machine learning (ML) to predict which patients are at high risk for HF hospital readmission 20 days after their discharge has the potential to improve HF management by enabling early interventions and individualized treatment approaches. In this paper, we compared six different ML models for this task, including multi-layer perceptron (MLP), K-nearest neighbors (KNN), logistic regression (LR), decision trees (DT), random forests (RF), and support vector machines (SVM) with both linear and radial basis kernels. The outputs of the classifiers are compared using performance metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We found that RF outperforms all the remaining models with an AUC of 94% while SVM, MLP, and KNN all yield 88% AUC. In contrast, DT performs poorly, with an AUC value of 57%. Hence, hospitals in Rwanda can benefit from using the RF classifier to determine which HF patients are at high risk of hospital readmission.
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Affiliation(s)
- Theogene Rizinde
- College of Business and Economics, University of Rwanda, Kigali 4285, Rwanda
| | - Innocent Ngaruye
- College of Science and Technology, University of Rwanda, Kigali 4285, Rwanda;
| | - Nathan D. Cahill
- School of Mathematics and Statistics, Rochester Institute of Technology, Rochester, NY 14623, USA;
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Moreno-Sánchez PA. Improvement of a prediction model for heart failure survival through explainable artificial intelligence. Front Cardiovasc Med 2023; 10:1219586. [PMID: 37600061 PMCID: PMC10434534 DOI: 10.3389/fcvm.2023.1219586] [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: 05/09/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
Cardiovascular diseases and their associated disorder of heart failure (HF) are major causes of death globally, making it a priority for doctors to detect and predict their onset and medical consequences. Artificial Intelligence (AI) allows doctors to discover clinical indicators and enhance their diagnoses and treatments. Specifically, "eXplainable AI" (XAI) offers tools to improve the clinical prediction models that experience poor interpretability of their results. This work presents an explainability analysis and evaluation of two HF survival prediction models using a dataset that includes 299 patients who have experienced HF. The first model utilizes survival analysis, considering death events and time as target features, while the second model approaches the problem as a classification task to predict death. The model employs an optimization data workflow pipeline capable of selecting the best machine learning algorithm as well as the optimal collection of features. Moreover, different post hoc techniques have been used for the explainability analysis of the model. The main contribution of this paper is an explainability-driven approach to select the best HF survival prediction model balancing prediction performance and explainability. Therefore, the most balanced explainable prediction models are Survival Gradient Boosting model for the survival analysis and Random Forest for the classification approach with a c-index of 0.714 and balanced accuracy of 0.74 (std 0.03) respectively. The selection of features by the SCI-XAI in the two models is similar where "serum_creatinine", "ejection_fraction", and "sex" are selected in both approaches, with the addition of "diabetes" for the survival analysis model. Moreover, the application of post hoc XAI techniques also confirm common findings from both approaches by placing the "serum_creatinine" as the most relevant feature for the predicted outcome, followed by "ejection_fraction". The explainable prediction models for HF survival presented in this paper would improve the further adoption of clinical prediction models by providing doctors with insights to better understand the reasoning behind usually "black-box" AI clinical solutions and make more reasonable and data-driven decisions.
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Guo S, Zhang H, Gao Y, Wang H, Xu L, Gao Z, Guzzo A, Fortino G. Survival prediction of heart failure patients using motion-based analysis method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107547. [PMID: 37126888 DOI: 10.1016/j.cmpb.2023.107547] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/06/2023] [Accepted: 04/09/2023] [Indexed: 05/03/2023]
Abstract
BACKGROUND AND OBJECTIVE Survival prediction of heart failure patients is critical to improve the prognostic management of the cardiovascular disease. The existing survival prediction methods focus on the clinical information while lacking the cardiac motion information. we propose a motion-based analysis method to predict the survival risk of heart failure patients for aiding clinical diagnosis and treatment. METHODS We propose a motion-based analysis method for survival prediction of heart failure patients. First, our method proposes the hierarchical spatial-temporal structure to capture the myocardial border. It promotes the model discrimination on border features. Second, our method explores the dense optical flow structure to capture motion fields. It improves the tracking capability on cardiac images. The cardiac motion information is obtained by fusing boundary information and motion fields of cardiac images. Finally, our method proposes the multi-modality deep-cox structure to predict the survival risk of heart failure patients. It improves the survival probability of heart failure patients. RESULTS The motion-based analysis method is confirmed to be able to improve the survival prediction of heart failure patients. The precision, recall, F1-score, and C-index are 0.8519, 0.8333, 0.8425, and 0.8478, respectively, which is superior to other state-of-the-art methods. CONCLUSIONS The experimental results show that the proposed model can effectively predict survival risk of heart failure patients. It facilitates the application of robust clinical treatment strategies.
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Affiliation(s)
- Saidi Guo
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
| | - Yifeng Gao
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Hui Wang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Zhifan Gao
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Antonella Guzzo
- Department of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, Rende, Italy
| | - Giancarlo Fortino
- Department of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, Rende, Italy
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Chiu CC, Wu CM, Chien TN, Kao LJ, Li C, Jiang HL. Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure. J Clin Med 2022; 11:6460. [PMID: 36362686 PMCID: PMC9659015 DOI: 10.3390/jcm11216460] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 08/31/2023] Open
Abstract
Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients' conditions by healthcare professionals but allow for a more optimal use of healthcare resources.
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Affiliation(s)
- Chih-Chou Chiu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chung-Min Wu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Te-Nien Chien
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Ling-Jing Kao
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chengcheng Li
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Han-Ling Jiang
- Alliance Manchester Business School, University of Manchester, Manchester M15 6PB, UK
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Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8002. [PMID: 36298352 PMCID: PMC9610988 DOI: 10.3390/s22208002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 06/06/2023]
Abstract
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
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Affiliation(s)
- Jian-Dong Huang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Jinling Wang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Elaine Ramsey
- Department of Global Business & Enterprise, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Gerard Leavey
- School of Psychology, Ulster University at Coleraine, Londonderry BT52 1SA, UK
| | - Timothy J. A. Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK
| | - Joan Condell
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
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Agarwal A, Thirunarayan K, Romine WL, Alambo A, Cajita M, Banerjee T. Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2643-2646. [PMID: 36085789 DOI: 10.1109/embc48229.2022.9871400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Heart failure occurs when the heart is not able to pump blood and oxygen to support other organs in the body as it should. Treatments include medications and sometimes hospitalization. Patients with heart failure can have both cardiovascular as well as non-cardiovascular comorbidities. Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients. In this regard, we apply machine learning techniques, such as topic modeling, to identify the major themes found in the clinical notes specific to the procedures performed on 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling revealed five hidden themes in these clinical notes, including one related to heart disease comorbidities.
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Chi S, Guo A, Heard K, Kim S, Foraker R, White P, Moore N. Development and Structure of an Accurate Machine Learning Algorithm to Predict Inpatient Mortality and Hospice Outcomes in the Coronavirus Disease 2019 Era. Med Care 2022; 60:381-386. [PMID: 35230273 PMCID: PMC8989608 DOI: 10.1097/mlr.0000000000001699] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has challenged the accuracy and racial biases present in traditional mortality scores. An accurate prognostic model that can be applied to hospitalized patients irrespective of race or COVID-19 status may benefit patient care. RESEARCH DESIGN This cohort study utilized historical and ongoing electronic health record features to develop and validate a deep-learning model applied on the second day of admission predicting a composite outcome of in-hospital mortality, discharge to hospice, or death within 30 days of admission. Model features included patient demographics, diagnoses, procedures, inpatient medications, laboratory values, vital signs, and substance use history. Conventional performance metrics were assessed, and subgroup analysis was performed based on race, COVID-19 status, and intensive care unit admission. SUBJECTS A total of 35,521 patients hospitalized between April 2020 and October 2020 at a single health care system including a tertiary academic referral center and 9 community hospitals. RESULTS Of 35,521 patients, including 9831 non-White patients and 2020 COVID-19 patients, 2838 (8.0%) met the composite outcome. Patients who experienced the composite outcome were older (73 vs. 61 y old) with similar sex and race distributions between groups. The model achieved an area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.88, 0.91) and an average positive predictive value of 0.46 (0.40, 0.52). Model performance did not differ significantly in White (0.89) and non-White (0.90) subgroups or when grouping by COVID-19 status and intensive care unit admission. CONCLUSION A deep-learning model using large-volume, structured electronic health record data can effectively predict short-term mortality or hospice outcomes on the second day of admission in the general inpatient population without significant racial bias.
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Affiliation(s)
- Stephen Chi
- Division of Pulmonary and Critical Care Medicine
| | - Aixia Guo
- Institute for Informatics, Washington University in St. Louis
| | | | - Seunghwan Kim
- Division of General Medical Sciences, School of Medicine, Washington University in St. Louis
| | - Randi Foraker
- Institute for Informatics, Washington University in St. Louis
| | - Patrick White
- Division of Palliative Medicine, Department of Medicine, Washington University in St. Louis
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Machine Learning Technology-Based Heart Disease Detection Models. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7351061. [PMID: 35265303 PMCID: PMC8898839 DOI: 10.1155/2022/7351061] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/22/2022] [Accepted: 02/04/2022] [Indexed: 11/17/2022]
Abstract
At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared.
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Reducing the Heart Failure Burden in Romania by Predicting Congestive Heart Failure Using Artificial Intelligence: Proof of Concept. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112411728] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Due to population aging, we are currently confronted with an increased number of chronic heart failure patients. The primary purpose of this study was to implement a noncontact system that can predict heart failure exacerbation through vocal analysis. We designed the system to evaluate the voice characteristics of every patient, and we used the identified variations as an input for a machine-learning-based approach. We collected data from a total of 16 patients, 9 men and 7 women, aged 65–91 years old, who agreed to take part in the study, with a detailed signed informed consent. We included hospitalized patients admitted with cardiogenic acute pulmonary edema in the study, regardless of the precipitation cause or other known cardiovascular comorbidities. There were no specific exclusion criteria, except age (which had to be over 18 years old) and patients with speech inabilities. We then recorded each patient’s voice twice a day, using the same smartphone, Lenovo P780, from day one of hospitalization—when their general status was critical—until the day of discharge, when they were clinically stable. We used the New York Heart Association Functional Classification (NYHA) classification system for heart failure to include the patients in stages based on their clinical evolution. Each voice recording has been accordingly equated and subsequently introduced into the machine-learning algorithm. We used multiple machine-learning techniques for classification in order to detect which one turns out to be more appropriate for the given dataset and the one that can be the starting point for future developments. We used algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). After integrating the information from 15 patients, the algorithm correctly classified the 16th patient into the third NYHA stage at hospitalization and second NYHA stage at discharge, based only on his voice recording. The KNN algorithm proved to have the best classification accuracy, with a value of 0.945. Voice is a cheap and easy way to monitor a patient’s health status. The algorithm we have used for analyzing the voice provides highly accurate preliminary results. We aim to obtain larger datasets and compute more complex voice analyzer algorithms to certify the outcomes presented.
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Guo A, Foraker RE, MacGregor RM, Masood FM, Cupps BP, Pasque MK. The Use of Synthetic Electronic Health Record Data and Deep Learning to Improve Timing of High-Risk Heart Failure Surgical Intervention by Predicting Proximity to Catastrophic Decompensation. Front Digit Health 2021; 2:576945. [PMID: 34713050 PMCID: PMC8521851 DOI: 10.3389/fdgth.2020.576945] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 11/13/2020] [Indexed: 12/24/2022] Open
Abstract
Objective: Although many clinical metrics are associated with proximity to decompensation in heart failure (HF), none are individually accurate enough to risk-stratify HF patients on a patient-by-patient basis. The dire consequences of this inaccuracy in risk stratification have profoundly lowered the clinical threshold for application of high-risk surgical intervention, such as ventricular assist device placement. Machine learning can detect non-intuitive classifier patterns that allow for innovative combination of patient feature predictive capability. A machine learning-based clinical tool to identify proximity to catastrophic HF deterioration on a patient-specific basis would enable more efficient direction of high-risk surgical intervention to those patients who have the most to gain from it, while sparing others. Synthetic electronic health record (EHR) data are statistically indistinguishable from the original protected health information, and can be analyzed as if they were original data but without any privacy concerns. We demonstrate that synthetic EHR data can be easily accessed and analyzed and are amenable to machine learning analyses. Methods: We developed synthetic data from EHR data of 26,575 HF patients admitted to a single institution during the decade ending on 12/31/2018. Twenty-seven clinically-relevant features were synthesized and utilized in supervised deep learning and machine learning algorithms (i.e., deep neural networks [DNN], random forest [RF], and logistic regression [LR]) to explore their ability to predict 1-year mortality by five-fold cross validation methods. We conducted analyses leveraging features from prior to/at and after/at the time of HF diagnosis. Results: The area under the receiver operating curve (AUC) was used to evaluate the performance of the three models: the mean AUC was 0.80 for DNN, 0.72 for RF, and 0.74 for LR. Age, creatinine, body mass index, and blood pressure levels were especially important features in predicting death within 1-year among HF patients. Conclusions: Machine learning models have considerable potential to improve accuracy in mortality prediction, such that high-risk surgical intervention can be applied only in those patients who stand to benefit from it. Access to EHR-based synthetic data derivatives eliminates risk of exposure of EHR data, speeds time-to-insight, and facilitates data sharing. As more clinical, imaging, and contractile features with proven predictive capability are added to these models, the development of a clinical tool to assist in timing of intervention in surgical candidates may be possible.
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Affiliation(s)
- Aixia Guo
- Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, United States
| | - Randi E Foraker
- Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, United States.,Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Robert M MacGregor
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Faraz M Masood
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Brian P Cupps
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Michael K Pasque
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
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Mei X, Zhong M, Ge W, Zhang L. Mathematical models for shear-induced blood damage based on vortex platform. Int J Artif Organs 2021; 45:397-403. [PMID: 33740880 DOI: 10.1177/03913988211003587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Non-physiological shear stress in Ventricular Assist Device (VAD) is considered to be an important trigger of blood damage, which has become the biggest shackle for clinical application. The researches on blood damage in literature were limited to qualitative but did not make much quantitative analysis. The purpose of this study was to investigate the quantitative influence of two flow-dependent parameters: shear stress (rotational speed) and exposure time on the shear-induced damage of red blood cells and von Willebrand Factor (vWF). A vortex blood-shearing platform was constructed to conduct in vitro experiments. Free hemoglobin assay and vWF molecular weight analysis were then performed on the sheared blood samples. MATLAB was used for regression fitting of original experimental data. The quantitative correlations between the hemolysis index, the degradation of high molecular weight vWF and the two flow-dependent parameters were found both following the power law model. The mathematic models indicated that the sensitivity of blood damage on red blood cells and vWF to exposure time was both greater than that of shear stress. Besides, the damage of vWF was more serious than that of red blood cells at the same flow condition. The models could be used to predict blood damage in blood-contacting medical devices, especially for the slow even stagnant blood flow regions in VAD, thus may provide useful guidance for VAD development and improvement. It also indicated that the vortex platform can be used to study the law of blood damage for the simple structure and easy operation.
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Affiliation(s)
- Xu Mei
- Artificial Organ Laboratory, Bio-manufacturing Research Centre, School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Min Zhong
- Artificial Organ Laboratory, Bio-manufacturing Research Centre, School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Wanning Ge
- Artificial Organ Laboratory, Bio-manufacturing Research Centre, School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Liudi Zhang
- Artificial Organ Laboratory, Bio-manufacturing Research Centre, School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
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