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Kim MN, Lee YS, Park Y, Jung A, So H, Park J, Park JJ, Choi DJ, Kim SR, Park SM. Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation. ESC Heart Fail 2024. [PMID: 38981003 DOI: 10.1002/ehf2.14918] [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: 02/09/2023] [Revised: 05/18/2024] [Accepted: 06/07/2024] [Indexed: 07/11/2024] Open
Abstract
AIMS Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data. This study aimed to develop a deep learning-based prediction model for HF rehospitalization within 30, 90, and 365 days after acute HF (AHF) discharge. METHODS AND RESULTS We analysed the data of patients admitted due to AHF between January 2014 and January 2019 in a tertiary hospital. In performing deep learning-based predictive algorithms for HF rehospitalization, we use hyperbolic tangent activation layers followed by recurrent layers with gated recurrent units. To assess the readmission prediction, we used the AUC, precision, recall, specificity, and F1 measure. We applied the Shapley value to identify which features contributed to HF readmission. Twenty-two prognostic features exhibiting statistically significant associations with HF rehospitalization were identified, consisting of 6 time-independent and 16 time-dependent features. The AUC value shows moderate discrimination for predicting readmission within 30, 90, and 365 days of follow-up (FU) (AUC:0.63, 0.74, and 0.76, respectively). The features during the FU have a relatively higher contribution to HF rehospitalization than features from other time points. CONCLUSIONS Our deep learning-based model using real-world data could provide valid predictions of HF rehospitalization in 1 year follow-up. It can be easily utilized to guide appropriate interventions or care strategies for patients with HF. The closed monitoring and blood test in daily clinics are important for assessing the risk of HF rehospitalization.
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Affiliation(s)
- Mi-Na Kim
- Department of Internal Medicine, Division of Cardiology, Anam Hospital, Korea University Medicine, Seoul, Korea
| | | | | | - Ayoung Jung
- Data Analytics Group, Samsung SDS, Seoul, Korea
| | - Hanjee So
- Data Analytics Group, Samsung SDS, Seoul, Korea
| | | | - Jin-Joo Park
- Department of Internal Medicine, Division of Cardiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Dong-Joo Choi
- Department of Internal Medicine, Division of Cardiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - So-Ree Kim
- Department of Internal Medicine, Division of Cardiology, Anam Hospital, Korea University Medicine, Seoul, Korea
| | - Seong-Mi Park
- Department of Internal Medicine, Division of Cardiology, Anam Hospital, Korea University Medicine, Seoul, Korea
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Saqib M, Perswani P, Muneem A, Mumtaz H, Neha F, Ali S, Tabassum S. Machine learning in heart failure diagnosis, prediction, and prognosis: review. Ann Med Surg (Lond) 2024; 86:3615-3623. [PMID: 38846887 PMCID: PMC11152866 DOI: 10.1097/ms9.0000000000002138] [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: 02/09/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024] Open
Abstract
Globally, cardiovascular diseases take the lives of over 17 million people each year, mostly through myocardial infarction, or MI, and heart failure (HF). This comprehensive literature review examines various aspects related to the diagnosis, prediction, and prognosis of HF in the context of machine learning (ML). The review covers an array of topics, including the diagnosis of HF with preserved ejection fraction (HFpEF) and the identification of high-risk patients with HF with reduced ejection fraction (HFrEF). The prediction of mortality in different HF populations using different ML approaches is explored, encompassing patients in the ICU, and HFpEF patients using biomarkers and gene expression. The review also delves into the prediction of mortality and hospitalization rates in HF patients with mid-range ejection fraction (HFmrEF) using ML methods. The findings highlight the significance of a multidimensional approach that encompasses clinical evaluation, laboratory assessments, and comprehensive research to improve our understanding and management of HF. Promising predictive models incorporating biomarkers, gene expression, and consideration of epigenetics demonstrate potential in estimating mortality and identifying high-risk HFpEF patients. This literature review serves as a valuable resource for researchers, clinicians, and healthcare professionals seeking a comprehensive and updated understanding of the role of ML diagnosis, prediction, and prognosis of HF across different subtypes and patient populations.
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Affiliation(s)
| | | | - Abraar Muneem
- College of Medicine, The Pennsylvania State University, Hershey, United States
| | | | - Fnu Neha
- Jinnah Sindh Medical University, Karachi
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Bali V, Turzhitsky V, Schelfhout J, Paudel M, Hulbert E, Peterson-Brandt J, Hertzberg J, Kelly NR, Patel RH. Machine learning to identify chronic cough from administrative claims data. Sci Rep 2024; 14:2449. [PMID: 38291064 PMCID: PMC10828499 DOI: 10.1038/s41598-024-51522-9] [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: 02/02/2023] [Accepted: 01/06/2024] [Indexed: 02/01/2024] Open
Abstract
Accurate identification of patient populations is an essential component of clinical research, especially for medical conditions such as chronic cough that are inconsistently defined and diagnosed. We aimed to develop and compare machine learning models to identify chronic cough from medical and pharmacy claims data. In this retrospective observational study, we compared 3 machine learning algorithms based on XG Boost, logistic regression, and neural network approaches using a large claims and electronic health record database. Of the 327,423 patients who met the study criteria, 4,818 had chronic cough based on linked claims-electronic health record data. The XG Boost model showed the best performance, achieving a Receiver-Operator Characteristic Area Under the Curve (ROC-AUC) of 0.916. We selected a cutoff that favors a high positive predictive value (PPV) to minimize false positives, resulting in a sensitivity, specificity, PPV, and negative predictive value of 18.0%, 99.6%, 38.7%, and 98.8%, respectively on the held-out testing set (n = 82,262). Logistic regression and neural network models achieved slightly lower ROC-AUCs of 0.907 and 0.838, respectively. The XG Boost and logistic regression models maintained their robust performance in subgroups of individuals with higher rates of chronic cough. Machine learning algorithms are one way of identifying conditions that are not coded in medical records, and can help identify individuals with chronic cough from claims data with a high degree of classification value.
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Affiliation(s)
- Vishal Bali
- Center for Observational and Real-World Evidence (CORE), Merck & Co, Rahway, NJ, USA.
| | - Vladimir Turzhitsky
- Center for Observational and Real-World Evidence (CORE), Merck & Co, Rahway, NJ, USA
| | - Jonathan Schelfhout
- Center for Observational and Real-World Evidence (CORE), Merck & Co, Rahway, NJ, USA
| | - Misti Paudel
- Health Economics and Outcomes Research (HEOR), Optum Insight, Eden Prairie, MN, USA
| | - Erin Hulbert
- Health Economics and Outcomes Research (HEOR), Optum Insight, Eden Prairie, MN, USA
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Lashen H, St John TL, Almallah YZ, Sasidhar M, Shamout FE. Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates. JMIR AI 2023; 2:e45257. [PMID: 38875543 PMCID: PMC11041421 DOI: 10.2196/45257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/19/2023] [Accepted: 08/01/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Early warning score systems are widely used for identifying patients who are at the highest risk of deterioration to assist clinical decision-making. This could facilitate early intervention and consequently improve patient outcomes; for example, the National Early Warning Score (NEWS) system, which is recommended by the Royal College of Physicians in the United Kingdom, uses predefined alerting thresholds to assign scores to patients based on their vital signs. However, there is limited evidence of the reliability of such scores across patient cohorts in the United Arab Emirates. OBJECTIVE Our aim in this study was to propose a data-driven model that accurately predicts in-hospital deterioration in an inpatient cohort in the United Arab Emirates. METHODS We conducted a retrospective cohort study using a real-world data set that consisted of 16,901 unique patients associated with 26,073 inpatient emergency encounters and 951,591 observation sets collected between April 2015 and August 2021 at a large multispecialty hospital in Abu Dhabi, United Arab Emirates. The observation sets included routine measurements of heart rate, respiratory rate, systolic blood pressure, level of consciousness, temperature, and oxygen saturation, as well as whether the patient was receiving supplementary oxygen. We divided the data set of 16,901 unique patients into training, validation, and test sets consisting of 11,830 (70%; 18,319/26,073, 70.26% emergency encounters), 3397 (20.1%; 5206/26,073, 19.97% emergency encounters), and 1674 (9.9%; 2548/26,073, 9.77% emergency encounters) patients, respectively. We defined an adverse event as the occurrence of admission to the intensive care unit, mortality, or both if the patient was admitted to the intensive care unit first. On the basis of 7 routine vital signs measurements, we assessed the performance of the NEWS system in detecting deterioration within 24 hours using the area under the receiver operating characteristic curve (AUROC). We also developed and evaluated several machine learning models, including logistic regression, a gradient-boosting model, and a feed-forward neural network. RESULTS In a holdout test set of 2548 encounters with 95,755 observation sets, the NEWS system achieved an overall AUROC value of 0.682 (95% CI 0.673-0.690). In comparison, the best-performing machine learning models, which were the gradient-boosting model and the neural network, achieved AUROC values of 0.778 (95% CI 0.770-0.785) and 0.756 (95% CI 0.749-0.764), respectively. Our interpretability results highlight the importance of temperature and respiratory rate in predicting patient deterioration. CONCLUSIONS Although traditional early warning score systems are the dominant form of deterioration prediction models in clinical practice today, we strongly recommend the development and use of cohort-specific machine learning models as an alternative. This is especially important in external patient cohorts that were unseen during model development.
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Affiliation(s)
- Hazem Lashen
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | | | | | - Madhu Sasidhar
- Cleveland Clinic Tradition Hospital, Port St. Lucie, FL, United States
| | - Farah E Shamout
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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Han S, Sohn TJ, Ng BP, Park C. Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach. Sci Rep 2023; 13:13491. [PMID: 37596346 PMCID: PMC10439193 DOI: 10.1038/s41598-023-40552-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 08/12/2023] [Indexed: 08/20/2023] Open
Abstract
Cardiovascular disease (CVD) in cancer patients can affect the risk of unplanned readmissions, which have been reported to be costly and associated with worse mortality and prognosis. We aimed to demonstrate the feasibility of using machine learning techniques in predicting the risk of unplanned 180-day readmission attributable to CVD among hospitalized cancer patients using the 2017-2018 Nationwide Readmissions Database. We included hospitalized cancer patients, and the outcome was unplanned hospital readmission due to any CVD within 180 days after discharge. CVD included atrial fibrillation, coronary artery disease, heart failure, stroke, peripheral artery disease, cardiomegaly, and cardiomyopathy. Decision tree (DT), random forest, extreme gradient boost (XGBoost), and AdaBoost were implemented. Accuracy, precision, recall, F2 score, and receiver operating characteristic curve (AUC) were used to assess the model's performance. Among 358,629 hospitalized patients with cancer, 5.86% (n = 21,021) experienced unplanned readmission due to any CVD. The three ensemble algorithms outperformed the DT, with the XGBoost displaying the best performance. We found length of stay, age, and cancer surgery were important predictors of CVD-related unplanned hospitalization in cancer patients. Machine learning models can predict the risk of unplanned readmission due to CVD among hospitalized cancer patients.
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Affiliation(s)
- Sola Han
- Health Outcomes Division, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Ted J Sohn
- Health Outcomes Division, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Boon Peng Ng
- College of Nursing, University of Central Florida, Orlando, FL, USA
- Disability, Aging, and Technology Cluster, University of Central Florida, Orlando, FL, USA
| | - Chanhyun Park
- Health Outcomes Division, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA.
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Ru B, Tan X, Liu Y, Kannapur K, Ramanan D, Kessler G, Lautsch D, Fonarow G. Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study. JMIR Form Res 2023; 7:e41775. [PMID: 37067873 PMCID: PMC10152335 DOI: 10.2196/41775] [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: 09/19/2022] [Revised: 02/16/2023] [Accepted: 02/19/2023] [Indexed: 04/18/2023] Open
Abstract
BACKGROUND Heart failure (HF) is highly prevalent in the United States. Approximately one-third to one-half of HF cases are categorized as HF with reduced ejection fraction (HFrEF). Patients with HFrEF are at risk of worsening HF, have a high risk of adverse outcomes, and experience higher health care use and costs. Therefore, it is crucial to identify patients with HFrEF who are at high risk of subsequent events after HF hospitalization. OBJECTIVE Machine learning (ML) has been used to predict HF-related outcomes. The objective of this study was to compare different ML prediction models and feature construction methods to predict 30-, 90-, and 365-day hospital readmissions and worsening HF events (WHFEs). METHODS We used the Veradigm PINNACLE outpatient registry linked to Symphony Health's Integrated Dataverse data from July 1, 2013, to September 30, 2017. Adults with a confirmed diagnosis of HFrEF and HF-related hospitalization were included. WHFEs were defined as HF-related hospitalizations or outpatient intravenous diuretic use within 1 year of the first HF hospitalization. We used different approaches to construct ML features from clinical codes, including frequencies of clinical classification software (CCS) categories, Bidirectional Encoder Representations From Transformers (BERT) trained with CCS sequences (BERT + CCS), BERT trained on raw clinical codes (BERT + raw), and prespecified features based on clinical knowledge. A multilayer perceptron neural network, extreme gradient boosting (XGBoost), random forest, and logistic regression prediction models were applied and compared. RESULTS A total of 30,687 adult patients with HFrEF were included in the analysis; 11.41% (3184/27,917) of adults experienced a hospital readmission within 30 days of their first HF hospitalization, and nearly half (9231/21,562, 42.81%) of the patients experienced at least 1 WHFE within 1 year after HF hospitalization. The prediction models and feature combinations with the best area under the receiver operating characteristic curve (AUC) for each outcome were XGBoost with CCS frequency (AUC=0.595) for 30-day readmission, random forest with CCS frequency (AUC=0.630) for 90-day readmission, XGBoost with CCS frequency (AUC=0.649) for 365-day readmission, and XGBoost with CCS frequency (AUC=0.640) for WHFEs. Our ML models could discriminate between readmission and WHFE among patients with HFrEF. Our model performance was mediocre, especially for the 30-day readmission events, most likely owing to limitations of the data, including an imbalance between positive and negative cases and high missing rates of many clinical variables and outcome definitions. CONCLUSIONS We predicted readmissions and WHFEs after HF hospitalizations in patients with HFrEF. Features identified by data-driven approaches may be comparable with those identified by clinical domain knowledge. Future work may be warranted to validate and improve the models using more longitudinal electronic health records that are complete, are comprehensive, and have a longer follow-up time.
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Affiliation(s)
- Boshu Ru
- Merck & Co, Inc, Rahway, NJ, United States
| | - Xi Tan
- Merck & Co, Inc, Rahway, NJ, United States
| | - Yu Liu
- Merck & Co, Inc, Rahway, NJ, United States
| | | | | | - Garin Kessler
- Amazon Web Services Inc, Seattle, WA, United States
- School of Continuing Studies, Georgetown University, Washington, DC, United States
| | | | - Gregg Fonarow
- Ahmanson-UCLA Cardiomyopathy Center, University of California, Los Angeles, Los Angeles, CA, United States
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Mostafa FA, Elrefaei LA, Fouda MM, Hossam A. A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images. Diagnostics (Basel) 2022; 12:diagnostics12123034. [PMID: 36553041 PMCID: PMC9777249 DOI: 10.3390/diagnostics12123034] [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: 10/10/2022] [Revised: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Thoracic diseases refer to disorders that affect the lungs, heart, and other parts of the rib cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis, cardiomegaly, and fracture. Millions of people die every year from thoracic diseases. Therefore, early detection of these diseases is essential and can save many lives. Earlier, only highly experienced radiologists examined thoracic diseases, but recent developments in image processing and deep learning techniques are opening the door for the automated detection of these diseases. In this paper, we present a comprehensive review including: types of thoracic diseases; examination types of thoracic images; image pre-processing; models of deep learning applied to the detection of thoracic diseases (e.g., pneumonia, COVID-19, edema, fibrosis, tuberculosis, chronic obstructive pulmonary disease (COPD), and lung cancer); transfer learning background knowledge; ensemble learning; and future initiatives for improving the efficacy of deep learning models in applications that detect thoracic diseases. Through this survey paper, researchers may be able to gain an overall and systematic knowledge of deep learning applications in medical thoracic images. The review investigates a performance comparison of various models and a comparison of various datasets.
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Affiliation(s)
- Fatma A. Mostafa
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
| | - Lamiaa A. Elrefaei
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA
- Correspondence:
| | - Aya Hossam
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
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Li Y, Dong W, Ru B, Black A, Zhang X, Guan Y. Generic Medical Concept Embedding and Time Decay for Diverse Patient Outcome Prediction Tasks. iScience 2022; 25:104880. [PMID: 36039302 PMCID: PMC9418804 DOI: 10.1016/j.isci.2022.104880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/10/2022] [Accepted: 08/02/2022] [Indexed: 11/18/2022] Open
Abstract
Many fields, including Natural Language Processing (NLP), have recently witnessed the benefit of pre-training with large generic datasets to improve the accuracy of prediction tasks. However, there exist key differences between the longitudinal healthcare data (e.g., claims) and NLP tasks, which make the direct application of NLP pre-training methods to healthcare data inappropriate. In this article, we developed a pre-training scheme for longitudinal healthcare data that leverages the pairing of medical history and a future event. We then conducted systematic evaluations of various methods on ten patient-level prediction tasks encompassing adverse events, misdiagnosis, disease risks, and readmission. In addition to substantially reducing model size, our results show that a universal medical concept embedding pretrained with generic big data as well as carefully designed time decay modeling improves the accuracy of different downstream prediction tasks. This work develops a pre-training scheme for longitudinal healthcare data The method leverages the pairing of medical history and a future event We created a universal medical concept embedding pretrained with generic data We designed a time-decay method for medical concept data
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Abstract
In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.
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Chen YC, Chung JH, Yeh YJ, Lou SJ, Lin HF, Lin CH, Hsien HH, Hung KW, Yeh SCJ, Shi HY. Predicting 30-Day Readmission for Stroke Using Machine Learning Algorithms: A Prospective Cohort Study. Front Neurol 2022; 13:875491. [PMID: 35860493 PMCID: PMC9289395 DOI: 10.3389/fneur.2022.875491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundMachine learning algorithms for predicting 30-day stroke readmission are rarely discussed. The aims of this study were to identify significant predictors of 30-day readmission after stroke and to compare prediction accuracy and area under the receiver operating characteristic (AUROC) curve in five models: artificial neural network (ANN), K nearest neighbor (KNN), random forest (RF), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models.MethodsThe subjects of this prospective cohort study were 1,476 patients with a history of admission for stroke to one of six hospitals between March, 2014, and September, 2019. A training dataset (n = 1,033) was used for model development, and a testing dataset (n = 443) was used for internal validation. Another 167 patients with stroke recruited from October, to December, 2019, were enrolled in the dataset for external validation. A feature importance analysis was also performed to identify the significance of the selected input variables.ResultsFor predicting 30-day readmission after stroke, the ANN model had significantly (P < 0.001) higher performance indices compared to the other models. According to the ANN model results, the best predictor of 30-day readmission was PAC followed by nasogastric tube insertion and stroke type (P < 0.05). Using a machine learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients.ConclusionUsing a machine-learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients. For stroke patients who are candidates for PAC rehabilitation, these predictors have practical applications in educating patients in the expected course of recovery and health outcomes.
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Affiliation(s)
- Yu-Ching Chen
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Public Health, College of Medicine, National Cheng-Kung University, Tainan, Taiwan
| | - Jo-Hsuan Chung
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yu-Jo Yeh
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Shi-Jer Lou
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan
- Graduate Institute of Technological and Vocational Education, National Pingtung University of Science and Technology, Pingtung, Taiwan
| | - Hsiu-Fen Lin
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
- Department of Neurology, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ching-Huang Lin
- Division of Neurology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Hong-Hsi Hsien
- Department of Internal Medicine, St. Joseph Hospital, Kaohsiung, Taiwan
| | - Kuo-Wei Hung
- Division of Neurology, Department of Internal Medicine, Yuan's General Hospital, Kaohsiung, Taiwan
| | - Shu-Chuan Jennifer Yeh
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Business Management, National Sun Yat-Sen University, Kaohsiung, Taiwan
| | - Hon-Yi Shi
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan
- Graduate Institute of Technological and Vocational Education, National Pingtung University of Science and Technology, Pingtung, Taiwan
- Department of Business Management, National Sun Yat-Sen University, Kaohsiung, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
- *Correspondence: Hon-Yi Shi
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Bat-Erdene BI, Zheng H, Son SH, Lee JY. Deep learning-based prediction of heart failure rehospitalization during 6, 12, 24-month follow-ups in patients with acute myocardial infarction. Health Informatics J 2022; 28:14604582221101529. [PMID: 35587458 DOI: 10.1177/14604582221101529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Heart failure is a clinical syndrome that occurs when the heart is too weak or stiff and cannot pump enough blood that our body needs. It is one of the most expensive diseases due to frequent hospitalizations and emergency room visits. Reducing unnecessary rehospitalizations is also an important and challenging task that has the potential of saving healthcare costs, enabling discharge planning, and identifying patients at high risk. Therefore, this paper proposes a deep learning-based prediction model of heart failure rehospitalization during 6, 12, 24-month follow-ups after hospital discharge in patients with acute myocardial infarction (AMI). We used the Korea Acute Myocardial Infarction-National Institutes of Health (KAMIR-NIH) registry which included 13,104 patient records and 551 features. The proposed deep learning-based rehospitalization prediction model outperformed traditional machine learning algorithms such as logistic regression, support vector machine, AdaBoost, gradient boosting machine, and random forest. The performance of the proposed model was accuracy, the area under the curve, precision, recall, specificity, and F1 score of 99.37%, 99.90%, 96.86%, 98.61%, 99.49%, and 97.73%, respectively. This study showed the potential of a deep learning-based model for cardiology, which can be used for decision-making and medical diagnosis tool of heart failure rehospitalization in patients with AMI.
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Affiliation(s)
- Bat-Ireedui Bat-Erdene
- Department of Computer Science, 34933Chungbuk National University, Cheongju, South Korea
| | - Huilin Zheng
- Department of Computer Science, 34933Chungbuk National University, Cheongju, South Korea
| | - Sang Hyeok Son
- Department of Computer Science, 34933Chungbuk National University, Cheongju, South Korea
| | - Jong Yun Lee
- Department of Computer Science, 34933Chungbuk National University, Cheongju, South Korea
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Rabbi F, Dabbagh SR, Angin P, Yetisen AK, Tasoglu S. Deep Learning-Enabled Technologies for Bioimage Analysis. MICROMACHINES 2022; 13:mi13020260. [PMID: 35208385 PMCID: PMC8880650 DOI: 10.3390/mi13020260] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 01/31/2022] [Accepted: 02/03/2022] [Indexed: 02/05/2023]
Abstract
Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.
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Affiliation(s)
- Fazle Rabbi
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
| | - Sajjad Rahmani Dabbagh
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey
- Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkey
| | - Pelin Angin
- Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey;
| | - Ali Kemal Yetisen
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Savas Tasoglu
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey
- Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkey
- Institute of Biomedical Engineering, Boğaziçi University, Çengelköy, Istanbul 34684, Turkey
- Physical Intelligence Department, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany
- Correspondence:
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Zarkowsky DS, Stonko DP. Artificial intelligence's role in vascular surgery decision-making. Semin Vasc Surg 2021; 34:260-267. [PMID: 34911632 DOI: 10.1053/j.semvascsurg.2021.10.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/17/2021] [Accepted: 10/18/2021] [Indexed: 12/28/2022]
Abstract
Artificial intelligence (AI) is the next great advance informing medical science. Several disciplines, including vascular surgery, use AI-based decision-making tools to improve clinical performance. Although applied widely, AI functions best when confronted with voluminous, accurate data. Consistent, predictable analytic technique selection also challenges researchers. This article contextualizes AI analyses within evidence-based medicine, focusing on "big data" and health services research, as well as discussing opportunities to improve data collection and realize AI's promise.
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Affiliation(s)
- Devin S Zarkowsky
- Division of Vascular Surgery and Endovascular Therapy, University of Colorado School of Medicine, 12615 E 17(th) Place, AO1, Aurora, CO, 80045.
| | - David P Stonko
- Department of Surgery, The Johns Hopkins Hospital, Baltimore, MD
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