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Kamel Rahimi A, Canfell OJ, Chan W, Sly B, Pole JD, Sullivan C, Shrapnel S. Machine learning models for diabetes management in acute care using electronic medical records: A systematic review. Int J Med Inform 2022; 162:104758. [PMID: 35398812 DOI: 10.1016/j.ijmedinf.2022.104758] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Machine learning (ML) is a subset of Artificial Intelligence (AI) that is used to predict and potentially prevent adverse patient outcomes. There is increasing interest in the application of these models in digital hospitals to improve clinical decision-making and chronic disease management, particularly for patients with diabetes. The potential of ML models using electronic medical records (EMR) to improve the clinical care of hospitalised patients with diabetes is currently unknown. OBJECTIVE The aim was to systematically identify and critically review the published literature examining the development and validation of ML models using EMR data for improving the care of hospitalised adult patients with diabetes. METHODS The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines were followed. Four databases were searched (Embase, PubMed, IEEE and Web of Science) for studies published between January 2010 to January 2022. The reference lists of the eligible articles were manually searched. Articles that examined adults and both developed and validated ML models using EMR data were included. Studies conducted in primary care and community care settings were excluded. Studies were independently screened and data was extracted using Covidence® systematic review software. For data extraction and critical appraisal, the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was followed. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). Quality of reporting was assessed by adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. The IJMEDI checklist was followed to assess quality of ML models and the reproducibility of their outcomes. The external validation methodology of the studies was appraised. RESULTS Of the 1317 studies screened, twelve met inclusion criteria. Eight studies developed ML models to predict disglycaemic episodes for hospitalized patients with diabetes, one study developed a ML model to predict total insulin dosage, two studies predicted risk of readmission, and one study improved the prediction of hospital readmission for inpatients with diabetes. All included studies were heterogeneous with regard to ML types, cohort, input predictors, sample size, performance and validation metrics and clinical outcomes. Two studies adhered to the TRIPOD guideline. The methodological reporting of all the studies was evaluated to be at high risk of bias. The quality of ML models in all studies was assessed as poor. Robust external validation was not performed on any of the studies. No models were implemented or evaluated in routine clinical care. CONCLUSIONS This review identified a limited number of ML models which were developed to improve inpatient management of diabetes. No ML models were implemented in real hospital settings. Future research needs to enhance the development, reporting and validation steps to enable ML models for integration into routine clinical care.
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Affiliation(s)
- Amir Kamel Rahimi
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; Digital Health Cooperative Research Centre, Australian Government, Sydney, New South Wales, Australia.
| | - Oliver J Canfell
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; Digital Health Cooperative Research Centre, Australian Government, Sydney, New South Wales, Australia; UQ Business School, The University of Queensland, St Lucia 4072, Brisbane, Australia
| | - Wilkin Chan
- The School of Clinical Medicine, The University of Queensland, Herston 4006, Brisbane, Australia
| | - Benjamin Sly
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; Princess Alexandra Hospital, 199 Ipswich Road, Woolloongabba 4102, Brisbane, Australia
| | - Jason D Pole
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; Dalla Lana School of Public Health, The University of Toronto, Toronto, Canada; ICES, Toronto, Canada
| | - Clair Sullivan
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; Metro North Hospital and Health Service, Department of Health, Queensland Government, Herston 4006, Brisbane, Australia
| | - Sally Shrapnel
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; The School of Mathematics and Physics, The University of Queensland, St Lucia 4072, Brisbane, Australia
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Cui S, Wang D, Wang Y, Yu PW, Jin Y. An improved support vector machine-based diabetic readmission prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 166:123-135. [PMID: 30415712 DOI: 10.1016/j.cmpb.2018.10.012] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 10/07/2018] [Accepted: 10/12/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early identification of unplanned readmission risks will improve the quality of care during hospitalization and reduce the occurrence of readmission. In clinical practice, medical workers generally use LACE score method to evaluate patient readmission risks, but this method usually performs poorly. With this in mind, this study presents a novel method combining support vector machine and genetic algorithm to build the risk prediction model, which simultaneously involves feature selection and the processing of imbalanced data. This model aims to provide decision support for clinicians during the discharge management of patients with diabetes. METHOD The experiments were conducted from a set of 8756 medical records with 50 different features about diabetic readmission. After preprocessing the data, an effective SMOTE-based method was proposed to solve the imbalance data problem. Further, in order to improve prediction performance, a hybrid feature selection mechanism was devised to select the important features. Subsequently, an improved support vector machine-based (SVM-based) method was developed and the genetic algorithm was used to tune the sensitive parameter of the algorithm. Finally, the five-fold cross-validation method was applied to compare the performance of proposed method with other methods (LACE score, logistic regression, naïve bayes, decision tree and feed forward neural networks). RESULTS Experimental results indicate that the proposed SVM-based method achieves an accuracy of 81.02%, a sensitivity of 82.89%, a specificity of 79.23%, and outperforms other popular algorithms in identifying diabetic patients who may be readmitted. CONCLUSIONS Our research can improve the performance of clinic decision support systems for diabetic readmission, by which the readmission possibility as well as the waste of medical resources can be reduced.
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Affiliation(s)
- Shaoze Cui
- School of Management Science and Engineering, Dalian University of Technology, Dalian 116023, PR China
| | - Dujuan Wang
- Business School of Sichuan University, Chengdu 610064, China.
| | - Yanzhang Wang
- School of Management Science and Engineering, Dalian University of Technology, Dalian 116023, PR China
| | - Pay-Wen Yu
- Department of Physical Education, Fu Jen Catholic University, New Taipei City 24205, Taiwan
| | - Yaochu Jin
- School of Management Science and Engineering, Dalian University of Technology, Dalian 116023, PR China; Department of Computer Science, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom
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Idri A, Benhar H, Fernández-Alemán JL, Kadi I. A systematic map of medical data preprocessing in knowledge discovery. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:69-85. [PMID: 29903496 DOI: 10.1016/j.cmpb.2018.05.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 04/25/2018] [Accepted: 05/03/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Datamining (DM) has, over the last decade, received increased attention in the medical domain and has been widely used to analyze medical datasets in order to extract useful knowledge and previously unknown patterns. However, historical medical data can often comprise inconsistent, noisy, imbalanced, missing and high dimensional data. These challenges lead to a serious bias in predictive modeling and reduce the performance of DM techniques. Data preprocessing is, therefore, an essential step in knowledge discovery as regards improving the quality of data and making it appropriate and suitable for DM techniques. The objective of this paper is to review the use of preprocessing techniques in clinical datasets. METHODS We performed a systematic map of studies regarding the application of data preprocessing to healthcare and published between January 2000 and December 2017. A search string was determined on the basis of the mapping questions and the PICO categories. The search string was then applied in digital databases covering the fields of computer science and medical informatics in order to identify relevant studies. The studies were initially selected by reading their titles, abstracts and keywords. Those that were selected at that stage were then reviewed using a set of inclusion and exclusion criteria in order to eliminate any that were not relevant. This process resulted in 126 primary studies. RESULTS Selected studies were analyzed and classified according to their publication years and channels, research type, empirical type and contribution type. The findings of this mapping study revealed that researchers have paid a considerable amount of attention to preprocessing in medical DM in last decade. A significant number of the selected studies used data reduction and cleaning preprocessing tasks. Moreover, the disciplines in which preprocessing have received most attention are: cardiology, endocrinology and oncology. CONCLUSIONS Researchers should develop and implement standards for an effective integration of multiple medical data types. Moreover, we identified the need to perform literature reviews.
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Affiliation(s)
- A Idri
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - H Benhar
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - J L Fernández-Alemán
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain.
| | - I Kadi
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
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