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Luo M, Wang YT, Wang XK, Hou WH, Huang RL, Liu Y, Wang JQ. A multi-granularity convolutional neural network model with temporal information and attention mechanism for efficient diabetes medical cost prediction. Comput Biol Med 2022; 151:106246. [PMID: 36343403 DOI: 10.1016/j.compbiomed.2022.106246] [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: 06/06/2022] [Revised: 09/30/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
As the cost of diabetes treatment continues to grow, it is critical to accurately predict the medical costs of diabetes. Most medical cost studies based on convolutional neural networks (CNNs) ignore the importance of multi-granularity information of medical concepts and time interval characteristics of patients' multiple visit sequences, which reflect the frequency of patient visits and the severity of the disease. Therefore, this paper proposes a new end-to-end deep neural network structure, MST-CNN, for medical cost prediction. The MST-CNN model improves the representation quality of medical concepts by constructing a multi-granularity embedding model of medical concepts and incorporates a time interval vector to accurately measure the frequency of patient visits and form an accurate representation of medical events. Moreover, the MST-CNN model integrates a channel attention mechanism to adaptively adjust the channel weights to focus on significant medical features. The MST-CNN model systematically addresses the problem of deep learning models for temporal data representation. A case study and three comparative experiments are conducted using data collected from Pingjiang County. Through experiments, the methods used in the proposed model are analyzed, and the super contribution of the model performance is demonstrated.
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Affiliation(s)
- Min Luo
- School of Business, Central South University, Changsha, 410083, PR China
| | - Yi-Ting Wang
- School of Business, Central South University, Changsha, 410083, PR China
| | - Xiao-Kang Wang
- School of Business, Central South University, Changsha, 410083, PR China
| | - Wen-Hui Hou
- School of Business, Central South University, Changsha, 410083, PR China
| | - Rui-Lu Huang
- School of Business, Central South University, Changsha, 410083, PR China
| | - Ye Liu
- School of Business, Central South University, Changsha, 410083, PR China
| | - Jian-Qiang Wang
- School of Business, Central South University, Changsha, 410083, PR China.
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Xie F, Yuan H, Ning Y, Ong MEH, Feng M, Hsu W, Chakraborty B, Liu N. Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. J Biomed Inform 2021; 126:103980. [PMID: 34974189 DOI: 10.1016/j.jbi.2021.103980] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/07/2021] [Accepted: 12/20/2021] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Temporal electronic health records (EHRs) contain a wealth of information for secondary uses, such as clinical events prediction and chronic disease management. However, challenges exist for temporal data representation. We therefore sought to identify these challenges and evaluate novel methodologies for addressing them through a systematic examination of deep learning solutions. METHODS We searched five databases (PubMed, Embase, the Institute of Electrical and Electronics Engineers [IEEE] Xplore Digital Library, the Association for Computing Machinery [ACM] Digital Library, and Web of Science) complemented with hand-searching in several prestigious computer science conference proceedings. We sought articles that reported deep learning methodologies on temporal data representation in structured EHR data from January 1, 2010, to August 30, 2020. We summarized and analyzed the selected articles from three perspectives: nature of time series, methodology, and model implementation. RESULTS We included 98 articles related to temporal data representation using deep learning. Four major challenges were identified, including data irregularity, heterogeneity, sparsity, and model opacity. We then studied how deep learning techniques were applied to address these challenges. Finally, we discuss some open challenges arising from deep learning. CONCLUSION Temporal EHR data present several major challenges for clinical prediction modeling and data utilization. To some extent, current deep learning solutions can address these challenges. Future studies may consider designing comprehensive and integrated solutions. Moreover, researchers should incorporate clinical domain knowledge into study designs and enhance model interpretability to facilitate clinical implementation.
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Affiliation(s)
- Feng Xie
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Han Yuan
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Yilin Ning
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Marcus Eng Hock Ong
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Wynne Hsu
- School of Computing, National University of Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore
| | - Bibhas Chakraborty
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore; Department of Statistics and Data Science, National University of Singapore, Singapore; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States
| | - Nan Liu
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore; Institute of Data Science, National University of Singapore, Singapore; SingHealth AI Health Program, Singapore Health Services, Singapore.
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Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6662420. [PMID: 34055041 PMCID: PMC8149240 DOI: 10.1155/2021/6662420] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 04/10/2021] [Accepted: 04/23/2021] [Indexed: 11/23/2022]
Abstract
A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).
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Jane YN, Nehemiah HK, Kannan A. Classifying unevenly spaced clinical time series data using forecast error approximation based bottom-up (FeAB) segmented time delay neural network. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2021. [DOI: 10.1080/21681163.2020.1817791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Y. Nancy Jane
- Department of Computer Technology, Anna University, Chennai, India
| | | | - Arputharaj Kannan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
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Christopher JJ, Nehemiah HK, Arputharaj K, Moses GL. Computer-assisted Medical Decision-making System for Diagnosis of Urticaria. MDM Policy Pract 2016; 1:2381468316677752. [PMID: 30288410 PMCID: PMC6125052 DOI: 10.1177/2381468316677752] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 09/24/2016] [Indexed: 11/17/2022] Open
Abstract
Background: Urticaria is a common allergic disease that affects all age groups. Allergic disorders are diagnosed at allergy testing centers using skin tests. Though skin tests are the gold standard tests for allergy diagnosis, specialists are required to interpret the observations and test results. Hence, a computer-assisted medical decision-making (CMD) system can be used as an aid for decision support, by junior clinicians, in order to diagnose the presence of urticaria. Methods: The data from intradermal skin test results of 778 patients, who exhibited allergic symptoms, are considered for this study. Based on food habits and the history of a patient, 40 relevant allergens are tested. Allergen extracts are used for skin test. Ten independent runs of 10-fold cross-validation are used to train the system. The performance of the CMD system is evaluated using a set of test samples. The test samples were also presented to the junior clinicians at the allergy testing center to diagnose the presence or absence of urticaria. Results: From a set of 91 features, a subset of 41 relevant features is chosen based on the relevance score of the feature selection algorithm. The Bayes classification approach achieves a classification accuracy of 96.92% over the test samples. The junior clinicians were able to classify the test samples with an average accuracy of 75.68%. Conclusion: A probabilistic classification approach is used for identifying the presence or absence of urticaria based on intradermal skin test results. In the absence of an allergy specialist, the CDM system assists junior clinicians in clinical decision making.
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Affiliation(s)
- Jabez J Christopher
- Ramanujan Computing Centre (JJC, HKN), Anna University, Chennai, Tamil Nadu, India.,Department of Information Science and Technology (KA), Anna University, Chennai, Tamil Nadu, India.,Good Samaritan Lab and Allergy Testing Centre, Kilpauk, Chennai, Tamil Nadu, India (GLM)
| | - Harichandran Khanna Nehemiah
- Ramanujan Computing Centre (JJC, HKN), Anna University, Chennai, Tamil Nadu, India.,Department of Information Science and Technology (KA), Anna University, Chennai, Tamil Nadu, India.,Good Samaritan Lab and Allergy Testing Centre, Kilpauk, Chennai, Tamil Nadu, India (GLM)
| | - Kannan Arputharaj
- Ramanujan Computing Centre (JJC, HKN), Anna University, Chennai, Tamil Nadu, India.,Department of Information Science and Technology (KA), Anna University, Chennai, Tamil Nadu, India.,Good Samaritan Lab and Allergy Testing Centre, Kilpauk, Chennai, Tamil Nadu, India (GLM)
| | - George L Moses
- Ramanujan Computing Centre (JJC, HKN), Anna University, Chennai, Tamil Nadu, India.,Department of Information Science and Technology (KA), Anna University, Chennai, Tamil Nadu, India.,Good Samaritan Lab and Allergy Testing Centre, Kilpauk, Chennai, Tamil Nadu, India (GLM)
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