1
|
Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Grübner O, Rinaldi F, von Wyl V. Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLOS DIGITAL HEALTH 2023; 2:e0000347. [PMID: 37819910 PMCID: PMC10566734 DOI: 10.1371/journal.pdig.0000347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/14/2023] [Indexed: 10/13/2023]
Abstract
Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.
Collapse
Affiliation(s)
- Jana Sedlakova
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
| | - Andrea Horn Wintsch
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Center for Gerontology, University of Zurich, Zurich, Switzerland
- CoupleSense: Health and Interpersonal Emotion Regulation Group, University Research Priority Program (URPP) Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Markus Wolf
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Mina Stanikic
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christina Haag
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Chloé Sieber
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Gerold Schneider
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Kaspar Staub
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland
| | - Dominik Alois Ettlin
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Oliver Grübner
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Geography, University of Zurich, Zurich, Switzerland
| | - Fabio Rinaldi
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Dalle Molle Institute for Artificial Intelligence (IDSIA), Switzerland
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Fondazione Bruno Kessler, Trento, Italy
- Swiss Institute of Bioinformatics, Switzerland
| | - Viktor von Wyl
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | | |
Collapse
|
2
|
Elvas LB, Nunes M, Ferreira JC, Dias MS, Rosário LB. AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia. J Pers Med 2023; 13:1421. [PMID: 37763188 PMCID: PMC10533089 DOI: 10.3390/jpm13091421] [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: 08/30/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.
Collapse
Affiliation(s)
- Luís B. Elvas
- ISTAR, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal; (M.N.); (J.C.F.); (M.S.D.)
- Inov Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal
| | - Miguel Nunes
- ISTAR, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal; (M.N.); (J.C.F.); (M.S.D.)
| | - Joao C. Ferreira
- ISTAR, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal; (M.N.); (J.C.F.); (M.S.D.)
- Inov Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal
| | - Miguel Sales Dias
- ISTAR, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal; (M.N.); (J.C.F.); (M.S.D.)
| | - Luís Brás Rosário
- Faculty of Medicine, Lisbon University, Hospital Santa Maria/CHULN, CCUL, 1649-028 Lisbon, Portugal;
| |
Collapse
|
3
|
Saputra J, Lawrencya C, Saini JM, Suharjito S. Hyperparameter optimization for cardiovascular disease data-driven prognostic system. Vis Comput Ind Biomed Art 2023; 6:16. [PMID: 37524951 PMCID: PMC10390457 DOI: 10.1186/s42492-023-00143-6] [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: 03/15/2023] [Accepted: 07/04/2023] [Indexed: 08/02/2023] Open
Abstract
Prediction and diagnosis of cardiovascular diseases (CVDs) based, among other things, on medical examinations and patient symptoms are the biggest challenges in medicine. About 17.9 million people die from CVDs annually, accounting for 31% of all deaths worldwide. With a timely prognosis and thorough consideration of the patient's medical history and lifestyle, it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening disease. In this study, we used various patient datasets from a major hospital in the United States as prognostic factors for CVD. The data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years old. In this study, we present a data mining modeling approach to analyze the performance, classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning (ML) using the Orange data mining software. Various techniques are then used to classify the model parameters, such as k-nearest neighbors, support vector machine, random forest, artificial neural network (ANN), naïve bayes, logistic regression, stochastic gradient descent (SGD), and AdaBoost. To determine the number of clusters, various unsupervised ML clustering methods were used, such as k-means, hierarchical, and density-based spatial clustering of applications with noise clustering. The results showed that the best model performance analysis and classification accuracy were SGD and ANN, both of which had a high score of 0.900 on Cardiovascular Disease Prognostic datasets. Based on the results of most clustering methods, such as k-means and hierarchical clustering, Cardiovascular Disease Prognostic datasets can be divided into two clusters. The prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic model. The more accurate the model, the better it can predict which patients are at risk for CVD.
Collapse
Affiliation(s)
- Jayson Saputra
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia.
| | - Cindy Lawrencya
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
| | - Jecky Mitra Saini
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
| | - Suharjito Suharjito
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
| |
Collapse
|
4
|
Ahmed U, Lin JCW, Srivastava G. Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases. SUSTAINABLE COMPUTING : INFORMATICS AND SYSTEMS 2023; 38:100868. [PMID: 37168459 PMCID: PMC10076073 DOI: 10.1016/j.suscom.2023.100868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 11/27/2022] [Accepted: 04/02/2023] [Indexed: 05/13/2023]
Abstract
Approximately 19 million people die each year from cardiovascular and chronic respiratory diseases. As a result of the recent Covid-19 epidemic, blood pressure, cholesterol, and blood sugar levels have risen. Not only do healthcare institutions benefit from studying physiological vital signs, but individuals also benefit from being alerted to health problems in a timely manner. This study uses machine learning to categorize and predict cardiovascular and chronic respiratory diseases. By predicting a patient's health status, caregivers and medical professionals can be alerted when needed. We predicted vital signs for 180 seconds using real-world vital sign data. A person's life can be saved if caregivers react quickly and anticipate emergencies. The tree-based pipeline optimization method (TPOT) is used instead of manually adjusting machine learning classifiers. This paper focuses on optimizing classification accuracy by combining feature pre-processors and machine learning models with TPOT genetic programming making use of linear and Prophet models to predict important indicators. The TPOT tuning parameter combines predicted values with classical classification models such as Naïve Bayes, Support Vector Machines, and Random Forests. As a result of this study, we show the importance of categorizing and increasing the accuracy of predictions. The proposed model achieves its adaptive behavior by conceptually incorporating different machine learning classifiers. We compare the proposed model with several state-of-the-art algorithms using a large amount of training data. Test results at the University of Queensland using 32 patient's data showed that the proposed model outperformed existing algorithms, improving the classification of cardiovascular disease from 0.58 to 0.71 and chronic respiratory disease from 0.49 to 0.70, respectively, while minimizing the mean percent error in vital signs. Our results suggest that the Facebook Prophet prediction model in conjunction with the TPOT classification model can correctly diagnose a patient's health status based on abnormal vital signs and enables patients to receive prompt medical attention.
Collapse
Affiliation(s)
- Usman Ahmed
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5063, Bergen, Norway
| | - Jerry Chun-Wei Lin
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5063, Bergen, Norway
| | - Gautam Srivastava
- Department of Mathematics & Computer Science, Brandon University, Brandon, Canada
- Research Centre of Interneural Computing, Taichung, Taiwan
- Department of Computer Science & Math, Lebanese American University, Beirut, Lebanon
| |
Collapse
|
5
|
Huang Y, Zhang R, Li H, Xia Y, Yu X, Liu S, Yang Y. A multi-label learning prediction model for heart failure in patients with atrial fibrillation based on expert knowledge of disease duration. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04487-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
|
6
|
Zhou B, Rao X, Xing H, Ma Y, Wang F, Rong L. A convolutional neural network-based system for detecting early gastric cancer in white-light endoscopy. Scand J Gastroenterol 2023; 58:157-162. [PMID: 36000979 DOI: 10.1080/00365521.2022.2113427] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND White-light endoscopy (WLE) is a main and standard modality for detection of early gastric cancer (EGC). The detection rate of EGC is not satisfactory so far. In this single-center retrospective study we developed a convolutional neural network (CNN)-based system to automatically detect EGC in WLE images. METHODS An EGC detecting system was constructed based on the CNN architecture EfficientDet. We trained our system with a data set including 4527 images from 130 cases (cancerous images, 1737; noncancerous images, 2790). Then we tested its performance with a data set including 1243 images from 64 cases (cancerous images, 445; noncancerous images, 798). RESULTS For case-based analysis, our system successfully detected EGC in 63 of 64 cases and the sensitivity was 98.4%. For image-based analysis, the accuracy was 88.3%. The sensitivity, specificity, positive predictive value and negative predictive value were 84.5%, 90.5%, 83.2% and 91.3%, respectively. The most common cause for false positives was gastritis (57.9%). The most common cause for false negatives was that the lesion was too small with a diameter of 10 mm or less (44.9%). CONCLUSION Our CNN-based EGC detecting system was able to achieve satisfactory sensitivity for detecting EGC in WLE images and shows great potential in assisting endoscopists with the detection of EGC.
Collapse
Affiliation(s)
- Bin Zhou
- Department of Endoscopy Center, Peking University First Hospital, Beijing, China
| | - Xiaolong Rao
- Department of Endoscopy Center, Peking University First Hospital, Beijing, China
| | - Haoqiang Xing
- Thunder Software Technology Co., Ltd, Beijing, China
| | - Yongchen Ma
- Department of Endoscopy Center, Peking University First Hospital, Beijing, China
| | - Feng Wang
- Department of Endoscopy Center, Peking University First Hospital, Beijing, China
| | - Long Rong
- Department of Endoscopy Center, Peking University First Hospital, Beijing, China
| |
Collapse
|
7
|
Sayadi M, Varadarajan V, Sadoughi F, Chopannejad S, Langarizadeh M. A Machine Learning Model for Detection of Coronary Artery Disease Using Noninvasive Clinical Parameters. LIFE (BASEL, SWITZERLAND) 2022; 12:life12111933. [PMID: 36431068 PMCID: PMC9698583 DOI: 10.3390/life12111933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022]
Abstract
Background and Objective: Coronary artery disease (CAD) is one of the most prevalent causes of death worldwide. The early diagnosis and timely medical care of cardiovascular patients can greatly prevent death and reduce the cost of treatments associated with CAD. In this study, we attempt to prepare a new model for early CAD diagnosis. The proposed model can diagnose CAD based on clinical data and without the use of an invasive procedure. Methods: In this paper, machine-learning (ML) techniques were used for the early detection of CAD, which were applied to a CAD dataset known as Z-Alizadeh Sani. Since this dataset has 54 features, the Pearson correlation feature selection method was conducted to identify the most effective features. Then, six machine learning techniques including decision tree, deep learning, logistic regression, random forest, support vector machine (SVM), and Xgboost were employed based on a semi-random-partitioning framework. Result: Applying Pearson feature selection to the dataset demonstrated that only eight features were the most effective for CAD diagnosis. The results of running the six machine-learning models on the selected features showed that logistic regression and SVM had the same performance with 95.45% accuracy, 95.91% sensitivity, 91.66% specificity, and a 96.90% F1 score. In addition, the ROC curve indicates a similar result regarding the AUC (0.98). Conclusions: Prediction is an important component of medical decision support systems. The results of the present study showed that feature selection has a high impact on machine-learning performance and, regardless of the evaluation metrics of the machine-learning models, determining the effective features is very important. However, SVM and Logistic Regression were designated as the best models according to our selected features.
Collapse
Affiliation(s)
- Mohammadjavad Sayadi
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
- Department of Computer Engineering, Technical and Vocational University (TVU), Tehran 14357-61137, Iran
| | - Vijayakumar Varadarajan
- School of Computer Science and Engineering, The University of New South Wales, Sydney 2052, Australia
- Dean International, Ajeenkya D Y Patil University, Pune 412105, India
- Swiss School of Business and Management, 1213 Geneva, Switzerland
- Correspondence: (V.V.); (M.L.)
| | - Farahnaz Sadoughi
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Sara Chopannejad
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Mostafa Langarizadeh
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
- Correspondence: (V.V.); (M.L.)
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Zhang Y, Razbek J, Li D, Yang L, Bao L, Xia W, Mao H, Daken M, Zhang X, Cao M. Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models. BMC Public Health 2022; 22:251. [PMID: 35135534 PMCID: PMC8822755 DOI: 10.1186/s12889-022-12617-y] [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: 06/08/2021] [Accepted: 01/17/2022] [Indexed: 12/03/2022] Open
Abstract
Background We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS. Methods This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model. Results Of the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise. Conclusions Both DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.
Collapse
Affiliation(s)
- Yan Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Jaina Razbek
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Deyang Li
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Lei Yang
- Xinjiang De Kang Ci Hui Health Services Group, Urumqi, Xinjiang, China
| | - Liangliang Bao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Wenjun Xia
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Hongkai Mao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Mayisha Daken
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiaoxu Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Mingqin Cao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China.
| |
Collapse
|
10
|
Shah W, Aleem M, Iqbal MA, Islam MA, Ahmed U, Srivastava G, Lin JCW. A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:2621655. [PMID: 34760140 PMCID: PMC8575608 DOI: 10.1155/2021/2621655] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 08/24/2021] [Accepted: 10/04/2021] [Indexed: 11/17/2022]
Abstract
Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This high mortality rate can be reduced with the use of technological advancements in medical science that can facilitate continuous monitoring of physiological parameters-blood pressure, cholesterol levels, blood glucose, etc. The futuristic values of these critical physiological or vital sign parameters not only enable in-time assistance from medical experts and caregivers but also help patients manage their health status by receiving relevant regular alerts/advice from healthcare practitioners. In this study, we propose a machine-learning-based prediction and classification system to determine futuristic values of related vital signs for both cardiovascular and chronic respiratory diseases. Based on the prediction of futuristic values, the proposed system can classify patients' health status to alarm the caregivers and medical experts. In this machine-learning-based prediction and classification model, we have used a real vital sign dataset. To predict the next 1-3 minutes of vital sign values, several regression techniques (i.e., linear regression and polynomial regression of degrees 2, 3, and 4) have been tested. For caregivers, a 60-second prediction and to facilitate emergency medical assistance, a 3-minute prediction of vital signs is used. Based on the predicted vital signs values, the patient's overall health is assessed using three machine learning classifiers, i.e., Support Vector Machine (SVM), Naive Bayes, and Decision Tree. Our results show that the Decision Tree can correctly classify a patient's health status based on abnormal vital sign values and is helpful in timely medical care to the patients.
Collapse
Affiliation(s)
- Wajid Shah
- Capital University of Science and Technology, Islamabad 44000, Pakistan
| | - Muhammad Aleem
- National University of Computer and Emerging Sciences (NUCES), Islamabad 44000, Pakistan
| | - Muhammad Azhar Iqbal
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
| | - Muhammad Arshad Islam
- National University of Computer and Emerging Sciences (NUCES), Islamabad 44000, Pakistan
| | - Usman Ahmed
- Department of Computer Science,Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen 5063, Norway
| | - Gautam Srivastava
- Department of Mathematics and Computer Science, Brandon University, Brandon, Canada
- Research Centre for Interneural Computing, China Medical University, Taichung 40402, Taiwan
| | - Jerry Chun-Wei Lin
- Department of Computer Science,Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen 5063, Norway
| |
Collapse
|
11
|
Prediction of Bladder Cancer Treatment Side Effects Using an Ontology-Based Reasoning for Enhanced Patient Health Safety. INFORMATICS 2021. [DOI: 10.3390/informatics8030055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Predicting potential cancer treatment side effects at time of prescription could decrease potential health risks and achieve better patient satisfaction. This paper presents a new approach, founded on evidence-based medical knowledge, using as much information and proof as possible to help a computer program to predict bladder cancer treatment side effects and support the oncologist’s decision. This will help in deciding treatment options for patients with bladder malignancies. Bladder cancer knowledge is complex and requires simplification before any attempt to represent it in a formal or computerized manner. In this work we rely on the capabilities of OWL ontologies to seamlessly capture and conceptualize the required knowledge about this type of cancer and the underlying patient treatment process. Our ontology allows case-based reasoning to effectively predict treatment side effects for a given set of contextual information related to a specific medical case. The ontology is enriched with proofs and evidence collected from online biomedical research databases using “web crawlers”. We have exclusively designed the crawler algorithm to search for the required knowledge based on a set of specified keywords. Results from the study presented 80.3% of real reported bladder cancer treatment side-effects prediction and were close to really occurring adverse events recorded within the collected test samples when applying the approach. Evidence-based medicine combined with semantic knowledge-based models is prominent in generating predictions related to possible health concerns. The integration of a diversity of knowledge and evidence into one single integrated knowledge-base could dramatically enhance the process of predicting treatment risks and side effects applied to bladder cancer oncotherapy.
Collapse
|
12
|
Huang X, Zhu H, Wang J. Adoption of Snake Variable Model-Based Method in Segmentation and Quantitative Calculation of Cardiac Ultrasound Medical Images. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:2425482. [PMID: 34354806 PMCID: PMC8331276 DOI: 10.1155/2021/2425482] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 07/06/2021] [Accepted: 07/20/2021] [Indexed: 12/03/2022]
Abstract
This paper intends to explore the effect of the enhanced snake variable model in the segmentation of cardiac ultrasound images and its adoption in quantitative measurement of cardiac cavity. First, the basic principles of the traditional snake model and the gradient vector flow (GVF) snake model are explained. Then, an ellipsoid model is constructed to obtain the initial contour of the heart based on the three-dimensional volume of cardiac ultrasound image, and a discretized triangular mesh model is generated. Finally, the vortical gradient vector flow (VGVF) external force field is introduced and combined with the greedy algorithm to process the deformation of the initial ellipsoid contour of the heart. The segmentation effect is quantitatively evaluated regarding the area overlap rate (AOR) and the mean contour distance (MCD). The results show that the VGVF snake model can segment the deep recessed area of the "U-shaped map" in contrast to the traditional snake model and the GVF snake model. After being applied to ultrasonic image segmentation, the VGVF snake model obtains the segmentation result that is close to the doctor's manual segmentation result, and the average AOR and MCD are 97.4% and 3.2, respectively. The quantitative evaluation of the cardiac cavity is carried out based on the segmentation results, and the measurement of the volume change of the left ventricle within a cardiac cycle is realized. To sum up, VGVF snake model is superior to the traditional snake and GVF snake models in terms of ultrasonic image segmentation, which realizes the three-dimensional segmentation and quantitative calculation of the cardiac cavity.
Collapse
Affiliation(s)
- Xing Huang
- Department of Ultrasound, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong, China
| | - Haozhi Zhu
- Department of Ultrasound, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong, China
| | - Jiexin Wang
- Department of Ultrasound, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong, China
| |
Collapse
|
13
|
Li L, Chen Y, Shen Z, Zhang X, Sang J, Ding Y, Yang X, Li J, Chen M, Jin C, Chen C, Yu C. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer 2020; 23:126-132. [PMID: 31332619 PMCID: PMC6942561 DOI: 10.1007/s10120-019-00992-2] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 07/12/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND Magnifying endoscopy with narrow band imaging (M-NBI) has been applied to examine early gastric cancer by observing microvascular architecture and microsurface structure of gastric mucosal lesions. However, the diagnostic efficacy of non-experts in differentiating early gastric cancer from non-cancerous lesions by M-NBI remained far from satisfactory. In this study, we developed a new system based on convolutional neural network (CNN) to analyze gastric mucosal lesions observed by M-NBI. METHODS A total of 386 images of non-cancerous lesions and 1702 images of early gastric cancer were collected to train and establish a CNN model (Inception-v3). Then a total of 341 endoscopic images (171 non-cancerous lesions and 170 early gastric cancer) were selected to evaluate the diagnostic capabilities of CNN and endoscopists. Primary outcome measures included diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS The sensitivity, specificity, and accuracy of CNN system in the diagnosis of early gastric cancer were 91.18%, 90.64%, and 90.91%, respectively. No significant difference was spotted in the specificity and accuracy of diagnosis between CNN and experts. However, the diagnostic sensitivity of CNN was significantly higher than that of the experts. Furthermore, the diagnostic sensitivity, specificity and accuracy of CNN were significantly higher than those of the non-experts. CONCLUSIONS Our CNN system showed high accuracy, sensitivity and specificity in the diagnosis of early gastric cancer. It is anticipated that more progress will be made in optimization of the CNN diagnostic system and further development of artificial intelligence in the medical field.
Collapse
Affiliation(s)
- Lan Li
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003 China
| | - Yishu Chen
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003 China
| | - Zhe Shen
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003 China
| | - Xuequn Zhang
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003 China
| | - Jianzhong Sang
- Department of Gastroenterology, Yuyao People’s Hospital, Yuyao, China
| | - Yong Ding
- grid.203507.30000 0000 8950 5267Department of Gastroenterology, The Affiliated Hospital of School of Medicine of Ningbo University, Ningbo, China
| | - Xiaoyun Yang
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Jun Li
- grid.13402.340000 0004 1759 700XDepartment of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Ming Chen
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Chaohui Jin
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Chunlei Chen
- grid.13402.340000 0004 1759 700XState Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Chaohui Yu
- grid.13402.340000 0004 1759 700XDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003 China
| |
Collapse
|