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Garcés-Jiménez A, Polo-Luque ML, Gómez-Pulido JA, Rodríguez-Puyol D, Gómez-Pulido JM. Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis. Comput Biol Med 2024; 174:108469. [PMID: 38636331 DOI: 10.1016/j.compbiomed.2024.108469] [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: 08/30/2023] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/20/2024]
Abstract
This research addresses the problem of detecting acute respiratory, urinary tract, and other infectious diseases in elderly nursing home residents using machine learning algorithms. The study analyzes data extracted from multiple vital signs and other contextual information for diagnostic purposes. The daily data collection process encounters sampling constraints due to weekends, holidays, shift changes, staff turnover, and equipment breakdowns, resulting in numerous nulls, repeated readings, outliers, and meaningless values. The short time series generated also pose a challenge to analysis, preventing the extraction of seasonal information or consistent trends. Blind data collection results in most of the data coming from periods when residents are healthy, resulting in excessively imbalanced data. This study proposes a data cleaning process and then builds a mechanism that reproduces the basal activity of the residents to improve the classification of the disease. The results show that the proposed basal module-assisted machine learning techniques allow anticipating diagnostics 2, 3 or 4 days before doctors decide to start treatment with antibiotics, achieving a performance measured by the area-under-the-curve metric of 0.857. The contributions of this work are: (1) a new data cleaning process; (2) the analysis of contextual information to improve data quality; (3) the generation of a baseline measure for relative comparison; and (4) the use of either binary (disease/no disease) or multiclass classification, differentiating among types of infections and showing the advantages of multiclass versus binary classification. From a medical point of view, the anticipated detection of infectious diseases in institutionalized individuals is brand new.
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Affiliation(s)
- Alberto Garcés-Jiménez
- Department of Computer Science, Universidad de Alcalá, Politechnic School, Alcala de Henares, 28805, Spain
| | - María-Luz Polo-Luque
- Department of Nursing and Physiotherapy, Universidad de Alcalá, Faculty of Medicine and Health Sciences, Alcala de Henares, 28805, Spain
| | - Juan A Gómez-Pulido
- Department of Technologies of Computers and Communications, Universidad de Extremadura, School of Technology, Cáceres, 10003, Spain.
| | - Diego Rodríguez-Puyol
- Department of Medicine and Medical Specialties, Research Foundation of the University Hospital Príncipe de Asturias, Campus Científico Tecnológico, Alcala de Henares, 28805, Spain
| | - José M Gómez-Pulido
- Department of Computer Science, Universidad de Alcalá, Politechnic School, Alcala de Henares, 28805, Spain
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2
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Xie J, Wang Y, Sheng Q, Liu X, Li J, Sun F, Wang Y, Li S, Li Y, Yu Y, Yu G. Identification of mycoplasma pneumonia in children based on fusion of multi-modal clinical free-text description and structured test data. Health Informatics J 2024; 30:14604582241255818. [PMID: 38779978 DOI: 10.1177/14604582241255818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Mycoplasma pneumonia may lead to hospitalizations and pose life-threatening risks in children. The automated identification of mycoplasma pneumonia from electronic medical records holds significant potential for improving the efficiency of hospital resource allocation. In this study, we proposed a novel method for identifying mycoplasma pneumonia by integrating multi-modal features derived from both free-text descriptions and structured test data in electronic medical records. Our approach begins with the extraction of free-text and structured data from clinical records through a systematic preprocessing pipeline. Subsequently, we employ a pre-trained transformer language model to extract features from the free-text, while multiple additive regression trees are used to transform features from the structured data. An attention-based fusion mechanism is then applied to integrate these multi-modal features for effective classification. We validated our method using clinic records of 7157 patients, retrospectively collected for training and testing purposes. The experimental results demonstrate that our proposed multi-modal fusion approach achieves significant improvements over other methods across four key performance metrics.
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Affiliation(s)
- Jingna Xie
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yingshuo Wang
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiuyang Sheng
- Deepwise Healthcare Artificial Intelligence Laboratory, Beijing, China
| | - Xiaoqing Liu
- Deepwise Healthcare Artificial Intelligence Laboratory, Beijing, China
| | - Jing Li
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China; Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China
| | - Fenglei Sun
- Deepwise Healthcare Artificial Intelligence Laboratory, Beijing, China
| | - Yuqi Wang
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuxian Li
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yiming Li
- Deepwise Healthcare Artificial Intelligence Laboratory, Beijing, China
| | - Yizhou Yu
- Deepwise Healthcare Artificial Intelligence Laboratory, Beijing, China; Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Gang Yu
- The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China; Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China; Polytechnic Institute, Zhejiang University, Hangzhou, China
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Wang M, Yang B, Liu Y, Yang Y, Ji H, Yang C. Emerging infectious disease surveillance using a hierarchical diagnosis model and the Knox algorithm. Sci Rep 2023; 13:19836. [PMID: 37963966 PMCID: PMC10645817 DOI: 10.1038/s41598-023-47010-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 11/07/2023] [Indexed: 11/16/2023] Open
Abstract
Emerging infectious diseases are a critical public health challenge in the twenty-first century. The recent proliferation of such diseases has raised major social and economic concerns. Therefore, early detection of emerging infectious diseases is essential. Subjects from five medical institutions in Beijing, China, which met the spatial-specific requirements, were analyzed. A quality control process was used to select 37,422 medical records of infectious diseases and 56,133 cases of non-infectious diseases. An emerging infectious disease detection model (EIDDM), a two-layer model that divides the problem into two sub-problems, i.e., whether a case is an infectious disease, and if so, whether it is a known infectious disease, was proposed. The first layer model adopts the binary classification model TextCNN-Attention. The second layer is a multi-classification model of LightGBM based on the one-vs-rest strategy. Based on the experimental results, a threshold of 0.5 is selected. The model results were compared with those of other models such as XGBoost and Random Forest using the following evaluation indicators: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The prediction performance of the first-layer TextCNN is better than that of other comparison models. Its average specificity for non-infectious diseases is 97.57%, with an average negative predictive value of 82.63%, indicating a low risk of misdiagnosing non-infectious diseases as infectious (i.e., a low false positive rate). Its average positive predictive value for eight selected infectious diseases is 95.07%, demonstrating the model's ability to avoid misdiagnoses. The overall average accuracy of the model is 86.11%. The average prediction accuracy of the second-layer LightGBM model for emerging infectious diseases reaches 90.44%. Furthermore, the response time of a single online reasoning using the LightGBM model is approximately 27 ms, which makes it suitable for analyzing clinical records in real time. Using the Knox method, we found that all the infectious diseases were within 2000 m in our case, and a clustering feature of spatiotemporal interactions (P < 0.05) was observed as well. Performance testing and model comparison results indicated that the EIDDM is fast and accurate and can be used to monitor the onset/outbreak of emerging infectious diseases in real-world hospitals.
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Affiliation(s)
- Mengying Wang
- State Key Laboratory of Media Convergence and Communication, Communication University of China, No. 1, Dingfuzhuang East Street, Chaoyang District, Beijing, China
- Information Management and Big Data Center, Peking University Third Hospital, No. 49, Huayuan North Road, Beijing, China
| | - Bingqing Yang
- Goodwill Hessian Health Technology Co. Ltd, Beijing, China
| | - Yunpeng Liu
- Goodwill Hessian Health Technology Co. Ltd, Beijing, China
| | - Yingyun Yang
- State Key Laboratory of Media Convergence and Communication, Communication University of China, No. 1, Dingfuzhuang East Street, Chaoyang District, Beijing, China
| | - Hong Ji
- Information Management and Big Data Center, Peking University Third Hospital, No. 49, Huayuan North Road, Beijing, China.
| | - Cheng Yang
- State Key Laboratory of Media Convergence and Communication, Communication University of China, No. 1, Dingfuzhuang East Street, Chaoyang District, Beijing, China.
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Rabaan AA, Bakhrebah MA, Alotaibi J, Natto ZS, Alkhaibari RS, Alawad E, Alshammari HM, Alwarthan S, Alhajri M, Almogbel MS, Aljohani MH, Alofi FS, Alharbi N, Al-Adsani W, Alsulaiman AM, Aldali J, Ibrahim FA, Almaghrabi RS, Al-Omari A, Garout M. Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: A comprehensive review. J Infect Public Health 2023; 16:1837-1847. [PMID: 37769584 DOI: 10.1016/j.jiph.2023.08.021] [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: 04/11/2023] [Revised: 07/19/2023] [Accepted: 08/27/2023] [Indexed: 10/03/2023] Open
Abstract
Infectious diseases present a global challenge, requiring accurate diagnosis, effective treatments, and preventive measures. Artificial intelligence (AI) has emerged as a promising tool for analysing complex molecular data and improving the diagnosis, treatment, and prevention of infectious diseases. Computer-aided detection (CAD) using convolutional neural networks (CNN) has gained prominence for diagnosing tuberculosis (TB) and other infectious diseases such as COVID-19, HIV, and viral pneumonia. The review discusses the challenges and limitations associated with AI in this field and explores various machine-learning models and AI-based approaches. Artificial neural networks (ANN), recurrent neural networks (RNN), support vector machines (SVM), multilayer neural networks (MLNN), CNN, long short-term memory (LSTM), and random forests (RF) are among the models discussed. The review emphasizes the potential of AI to enhance the accuracy and efficiency of diagnosis, treatment, and prevention of infectious diseases, highlighting the need for further research and development in this area.
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Affiliation(s)
- Ali A Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran 31311, Saudi Arabia; College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; Department of Public Health and Nutrition, The University of Haripur, Haripur 22610, Pakistan.
| | - Muhammed A Bakhrebah
- Life Science and Environment Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Jawaher Alotaibi
- Infectious Diseases Unit, Department of Medicine, King Faisal Specialist Hospital and Research Center, Riyadh 11564, Saudi Arabia
| | - Zuhair S Natto
- Department of Dental Public Health, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Rahaf S Alkhaibari
- Molecular Diagnostic Laboratory, Dammam Regional Laboratory and Blood Bank, Dammam 31411, Saudi Arabia
| | - Eman Alawad
- Adult Infectious Diseases Department, Prince Mohammed Bin Abdulaziz Hospital, Riyadh 11474, Saudi Arabia
| | - Huda M Alshammari
- Clinical Pharmacy Department, College of Pharmacy, Northern Border University, Arar 9280, Saudi Arabia
| | - Sara Alwarthan
- Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
| | - Mashael Alhajri
- Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
| | - Mohammed S Almogbel
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 4030, Saudi Arabia
| | - Maha H Aljohani
- Department of Infectious Diseases, King Fahad Hospital, Madinah 42351, Saudi Arabia
| | - Fadwa S Alofi
- Department of Infectious Diseases, King Fahad Hospital, Madinah 42351, Saudi Arabia
| | - Nada Alharbi
- Department of Basic Medical Sciences, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
| | - Wasl Al-Adsani
- Department of Medicine, Infectious Diseases Hospital, Kuwait City 63537, Kuwait; Department of Infectious Diseases, Hampton Veterans Administration Medical Center, Hampton, VA 23667, USA
| | | | - Jehad Aldali
- Department of Pathology, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh 13317, Saudi Arabia
| | - Fatimah Al Ibrahim
- Infectious Disease Division, Department of Internal Medicine, Dammam Medical Complex, Dammam 32245, Saudi Arabia
| | - Reem S Almaghrabi
- Organ Transplant Center of Excellence, King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia
| | - Awad Al-Omari
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; Research Center, Dr. Sulaiman Al Habib Medical Group, Riyadh 11372, Saudi Arabia
| | - Mohammed Garout
- Department of Community Medicine and Health Care for Pilgrims, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
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5
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Tao W, Liu Y, Lin X, Song B, Zeng X. Prediction of multi-relational drug-gene interaction via Dynamic hyperGraph Contrastive Learning. Brief Bioinform 2023; 24:bbad371. [PMID: 37864294 DOI: 10.1093/bib/bbad371] [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: 06/28/2023] [Revised: 09/11/2023] [Accepted: 09/29/2023] [Indexed: 10/22/2023] Open
Abstract
Drug-gene interaction prediction occupies a crucial position in various areas of drug discovery, such as drug repurposing, lead discovery and off-target detection. Previous studies show good performance, but they are limited to exploring the binding interactions and ignoring the other interaction relationships. Graph neural networks have emerged as promising approaches owing to their powerful capability of modeling correlations under drug-gene bipartite graphs. Despite the widespread adoption of graph neural network-based methods, many of them experience performance degradation in situations where high-quality and sufficient training data are unavailable. Unfortunately, in practical drug discovery scenarios, interaction data are often sparse and noisy, which may lead to unsatisfactory results. To undertake the above challenges, we propose a novel Dynamic hyperGraph Contrastive Learning (DGCL) framework that exploits local and global relationships between drugs and genes. Specifically, graph convolutions are adopted to extract explicit local relations among drugs and genes. Meanwhile, the cooperation of dynamic hypergraph structure learning and hypergraph message passing enables the model to aggregate information in a global region. With flexible global-level messages, a self-augmented contrastive learning component is designed to constrain hypergraph structure learning and enhance the discrimination of drug/gene representations. Experiments conducted on three datasets show that DGCL is superior to eight state-of-the-art methods and notably gains a 7.6% performance improvement on the DGIdb dataset. Further analyses verify the robustness of DGCL for alleviating data sparsity and over-smoothing issues.
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Affiliation(s)
- Wen Tao
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 Hunan, China
| | - Yuansheng Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 Hunan, China
| | - Xuan Lin
- School of Computer Science, Xiangtan University, Xiangtan, 411105 Hunan, China
- Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education (Xiangtan University), Xiangtan, 411105 Hunan, China
| | - Bosheng Song
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 Hunan, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 Hunan, China
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6
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Albuquerque G, Fernandes F, Barbalho IMP, Barros DMS, Morais PSG, Morais AHF, Santos MM, Galvão-Lima LJ, Sales-Moioli AIL, Santos JPQ, Gil P, Henriques J, Teixeira C, Lima TS, Coutinho KD, Pinto TKB, Valentim RAM. Computational methods applied to syphilis: where are we, and where are we going? Front Public Health 2023; 11:1201725. [PMID: 37680278 PMCID: PMC10481400 DOI: 10.3389/fpubh.2023.1201725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 08/07/2023] [Indexed: 09/09/2023] Open
Abstract
Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions.
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Affiliation(s)
- Gabriela Albuquerque
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Felipe Fernandes
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Ingridy M. P. Barbalho
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Daniele M. S. Barros
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Philippi S. G. Morais
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Antônio H. F. Morais
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Marquiony M. Santos
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Leonardo J. Galvão-Lima
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Ana Isabela L. Sales-Moioli
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - João Paulo Q. Santos
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Paulo Gil
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal
| | - Jorge Henriques
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal
| | - César Teixeira
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal
| | - Thaisa Santos Lima
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
- Ministry of Health, Esplanada dos Ministérios, Brasília, Brazil
| | - Karilany D. Coutinho
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Talita K. B. Pinto
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Ricardo A. M. Valentim
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
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Wang M, Jia M, Wei Z, Wang W, Shang Y, Ji H. Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system. Sci Rep 2023; 13:13202. [PMID: 37580359 PMCID: PMC10425425 DOI: 10.1038/s41598-023-39931-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 08/02/2023] [Indexed: 08/16/2023] Open
Abstract
To improve the hospital's ability to proactively detect infectious diseases, a knowledge-based infectious disease monitoring and decision support system was established based on real medical records and knowledge rules. The effectiveness of the system was evaluated using interrupted time series analysis. In the system, a monitoring and alert rule library for infectious diseases was generated by combining infectious disease diagnosis guidelines with literature and a real medical record knowledge map. The system was integrated with the electronic medical record system, and doctors were provided with various types of real-time warning prompts when writing medical records. The effectiveness of the system's alerts was analyzed from the perspectives of false positive rates, rule accuracy, alert effectiveness, and missed case rates using interrupted time series analysis. Over a period of 12 months, the system analyzed 4,497,091 medical records, triggering a total of 12,027 monitoring alerts. Of these, 98.43% were clinically effective, while 1.56% were invalid alerts, mainly owing to the relatively rough rules generated by the guidelines leading to several false alarms. In addition, the effectiveness of the system's alerts, distribution of diagnosis times, and reporting efficiency of doctors were analyzed. 89.26% of infectious disease cases could be confirmed and reported by doctors within 5 min of receiving the alert, and 77.6% of doctors could complete the filling of 33 items of information within 2 min, which is a reduction in time compared to the past. The timely reminders from the system reduced the rate of missed cases by doctors; the analysis using interrupted time series method showed an average reduction of 4.4037% in the missed-case rate. This study proposed a knowledge-based infectious disease decision support system based on real medical records and knowledge rules, and its effectiveness was verified. The system improved the management of infectious diseases, increased the reliability of decision-making, and reduced the rate of underreporting.
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Affiliation(s)
- Mengying Wang
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Mo Jia
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Zhenhao Wei
- Goodwill Hessian Health Technology Co. Ltd, Beijing, China
| | - Wei Wang
- Goodwill Hessian Health Technology Co. Ltd, Beijing, China
| | - Yafei Shang
- Goodwill Hessian Health Technology Co. Ltd, Beijing, China
| | - Hong Ji
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China.
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Wekesa JS, Kimwele M. A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment. Front Genet 2023; 14:1199087. [PMID: 37547471 PMCID: PMC10398577 DOI: 10.3389/fgene.2023.1199087] [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: 04/02/2023] [Accepted: 07/11/2023] [Indexed: 08/08/2023] Open
Abstract
Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.
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9
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Okeibunor JC, Jaca A, Iwu-Jaja CJ, Idemili-Aronu N, Ba H, Zantsi ZP, Ndlambe AM, Mavundza E, Muneene D, Wiysonge CS, Makubalo L. The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review. Front Public Health 2023; 11:1102185. [PMID: 37469694 PMCID: PMC10352788 DOI: 10.3389/fpubh.2023.1102185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 06/19/2023] [Indexed: 07/21/2023] Open
Abstract
Background Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care. Methods We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data. Results Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health. Conclusions Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.
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Affiliation(s)
| | - Anelisa Jaca
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | | | - Ngozi Idemili-Aronu
- Department of Sociology/Anthropology, University of Nigeria, Nsukka, Nigeria
| | - Housseynou Ba
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
| | - Zukiswa Pamela Zantsi
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Asiphe Mavis Ndlambe
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Edison Mavundza
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
| | | | - Charles Shey Wiysonge
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
- HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Durban, South Africa
| | - Lindiwe Makubalo
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
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10
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Li N, Wu Z, Jiang C, Sun L, Li B, Guo J, Liu F, Zhou Z, Qin H, Tan W, Tian L. An automatic fresh rib fracture detection and positioning system using deep learning. Br J Radiol 2023; 96:20221006. [PMID: 36972072 PMCID: PMC10230380 DOI: 10.1259/bjr.20221006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 03/29/2023] Open
Abstract
OBJECTIVE To evaluate the performance and robustness of a deep learning-based automatic fresh rib fracture detection and positioning system (FRF-DPS). METHODS CT scans of 18,172 participants admitted to eight hospitals from June 2009 to March 2019 were retrospectively collected. Patients were divided into development set (14,241), multicenter internal test set (1612), and external test set (2319). In internal test set, sensitivity, false positives (FPs) and specificity were used to assess fresh rib fracture detection performance at the lesion- and examination-levels. In external test set, the performance of detecting fresh rib fractures by radiologist and FRF-DPS were evaluated at lesion, rib, and examination levels. Additionally, the accuracy of FRF-DPS in rib positioning was investigated by the ground-truth labeling. RESULTS In multicenter internal test set, FRF-DPS showed excellent performance at the lesion- (sensitivity: 0.933 [95%CI, 0.916-0.949], FPs: 0.50 [95%CI, 0.397-0.583]) and examination-level. In external test set, the sensitivity and FPs at the lesion-level of FRF-DPS (0.909 [95%CI, 0.883-0.926], p < 0.001; 0.379 [95%CI, 0.303-0.422], p = 0.001) were better than the radiologist (0.789 [95%CI, 0.766-0.807]; 0.496 [95%CI, 0.383-0.571]), so were the rib- and patient-levels. In subgroup analysis of CT parameters, FRF-DPS were robust (0.894-0.927). Finally, FRF-DPS(0.997 [95%CI, 0.992-1.000], p < 0.001) is more accurate than radiologist (0.981 [95%CI, 0.969-0.996]) in rib positioning and takes 20 times less time. CONCLUSION FRF-DPS achieved high detection rate of fresh rib fractures with low FP values, and precise positioning of ribs, thus can be used in clinical practice to improve the detection rate and work efficiency. ADVANCES IN KNOWLEDGE We developed the FRF-DPS system which can detect fresh rib fractures and rib position, and evaluated by a large amount of multicenter data.
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Affiliation(s)
- Ning Li
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Zhe Wu
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Chao Jiang
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Lulu Sun
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Bingyao Li
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Jun Guo
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Feng Liu
- Deepwise Artificial Intelligence (AI) Lab, Deepwise Inc., Beijing, China
| | - Zhen Zhou
- Deepwise Artificial Intelligence (AI) Lab, Deepwise Inc., Beijing, China
| | - Haibo Qin
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
| | - Weixiong Tan
- Deepwise Artificial Intelligence (AI) Lab, Deepwise Inc., Beijing, China
| | - Lufeng Tian
- Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China
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Contino G. Reconciling art and science in the era of personalised medicine: the legacy of George Canguilhem. Philos Ethics Humanit Med 2023; 18:5. [PMID: 37221540 DOI: 10.1186/s13010-023-00133-9] [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: 06/14/2022] [Accepted: 04/05/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Biomedicine, i.e. the application of basic sciences to medicine, has become the cornerstone for the study of etiopathogenesis and treatment of diseases. Biomedicine has enormously contributed to the progress of medicine and healthcare and has become the preferred approach to medical problems in the West. The developments in statistical inference and machine learning techniques have provided the foundation for personalised medicine where clinical management can be fully informed by biomedicine. The deployment of precision medicine may impact the autonomy and self-normativity of the patients. Understanding the relationship between biomedicine and medical practice can help navigate the benefits and challenges offered by precision medicine. METHODS Conventional content analysis was applied to "Le Normal and le Pathologique" (Canguilhem G. The Normal and the Pathological. Princeton: Princeton University Press; 1991) and further investigated with respect to its relationship with techne and precision medicine using PubMed and Google Scholar and the Standford Encyclopedia of Philosophy to search for the following keywords singularly or in combination: "Canguilhem", "techne", "episteme", "precision medicine", "machine learning AND medicine". RESULTS The Hippocratic concept of techne accounts for many characteristics of medical knowledge and practice. The advances of biomedicine, experimental medicine and, more recently, machine learning offer, in contrast, the model of a medicine based purely on episteme. I argue that Canguilhem medical epistemology establishes a framework where episteme and data-driven medicine is compatible with the promotion of patient's autonomy and self-normativity. CONCLUSIONS Canguilhem's medical epistemology orders the relationship of applied medicine with experimental sciences, ethics and social sciences. It provides guidance to define the scope of medicine and the boundaries of medicalization of healthy life. Finally, it sets an agenda for a safe implementation of machine learning in medicine.
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Affiliation(s)
- Gianmarco Contino
- Von Hügel Institute, University of Cambridge, Cambridge, UK.
- Institute of Cancer and Genomic Sciences, University of Birmingham, College of Medicine, Vincent Drive, Edgbaston, Birmingham, B152TT, UK.
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Wang B, Li L, Nakashima Y, Kawasaki R, Nagahara H. Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory. BMC Med Inform Decis Mak 2023; 23:80. [PMID: 37143041 PMCID: PMC10161556 DOI: 10.1186/s12911-023-02160-0] [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: 04/04/2022] [Accepted: 03/23/2023] [Indexed: 05/06/2023] Open
Abstract
PURPOSE Estimating the surgery length has the potential to be utilized as skill assessment, surgical training, or efficient surgical facility utilization especially if it is done in real-time as a remaining surgery duration (RSD). Surgical length reflects a certain level of efficiency and mastery of the surgeon in a well-standardized surgery such as cataract surgery. In this paper, we design and develop a real-time RSD estimation method for cataract surgery that does not require manual labeling and is transferable with minimum fine-tuning. METHODS A regression method consisting of convolutional neural networks (CNNs) and long short-term memory (LSTM) is designed for RSD estimation. The model is firstly trained and evaluated for the single main surgeon with a large number of surgeries. Then, the fine-tuning strategy is used to transfer the model to the data of the other two surgeons. Mean Absolute Error (MAE in seconds) was used to evaluate the performance of the RSD estimation. The proposed method is compared with the naïve method which is based on the statistic of the historical data. A transferability experiment is also set to demonstrate the generalizability of the method. RESULT The mean surgical time for the sample videos was 318.7 s (s) (standard deviation 83.4 s) for the main surgeon for the initial training. In our experiments, the lowest MAE of 19.4 s (equal to about 6.4% of the mean surgical time) is achieved by our best-trained model for the independent test data of the main target surgeon. It reduces the MAE by 35.5 s (-10.2%) compared to the naïve method. The fine-tuning strategy transfers the model trained for the main target to the data of other surgeons with only a small number of training data (20% of the pre-training). The MAEs for the other two surgeons are 28.3 s and 30.6 s with the fine-tuning model, which decreased by -8.1 s and -7.5 s than the Per-surgeon model (average declining of -7.8 s and 1.3% of video duration). External validation study with Cataract-101 outperformed 3 reported methods of TimeLSTM, RSDNet, and CataNet. CONCLUSION An approach to build a pre-trained model for estimating RSD estimation based on a single surgeon and then transfer to other surgeons demonstrated both low prediction error and good transferability with minimum fine-tuning videos.
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Affiliation(s)
- Bowen Wang
- Institute for Datability Science (IDS), Osaka University, Suita, 565-0871, Japan
| | - Liangzhi Li
- Institute for Datability Science (IDS), Osaka University, Suita, 565-0871, Japan
| | - Yuta Nakashima
- Institute for Datability Science (IDS), Osaka University, Suita, 565-0871, Japan
| | - Ryo Kawasaki
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Suita, 565-0871, Japan.
- Department of Vision Informatics, Graduate School of Medicine, Osaka University, Suita, 565-0871, Japan.
| | - Hajime Nagahara
- Institute for Datability Science (IDS), Osaka University, Suita, 565-0871, Japan
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Deconstruction of Risk Prediction of Ischemic Cardiovascular and Cerebrovascular Diseases Based on Deep Learning. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:8478835. [PMID: 36263000 PMCID: PMC9546720 DOI: 10.1155/2022/8478835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/24/2022] [Accepted: 09/07/2022] [Indexed: 01/26/2023]
Abstract
Over the years, with the widespread use of computer technology and the dramatic increase in electronic medical data, data-driven approaches to medical data analysis have emerged. However, the analysis of medical data remains challenging due to the mixed nature of the data, the incompleteness of many records, and the high level of noise. This paper proposes an improved neural network DBN-LSTM that combines a deep belief network (DBN) with a long short-term memory (LSTM) network. The subset of feature attributes processed by CFS-EGA is used for training, and the optimal selection test of the number of hidden layers is performed on the upper DBN in the process of training DBN-LSTM. At the same time, the validation set is combined to determine the hyperparameters of the LSTM. Construct the DNN, CNN, and long short-term memory (LSTM) network for comparative analysis with DBN-LSTM. Use the classification method to compare the average of the final results of the two experiments. The results show that the prediction accuracy of DBN-LSTM for cardiovascular and cerebrovascular diseases reaches 95.61%, which is higher than the three traditional neural networks.
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Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm. ELECTRONICS 2022. [DOI: 10.3390/electronics11091495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Coronary artery disease (CAD) is a disease with high mortality and disability. By 2019, there were 197 million CAD patients in the world. Additionally, the number of disability-adjusted life years (DALYs) owing to CAD reached 182 million. It is widely known that the early and accurate diagnosis of CAD is the most efficient method to reduce the damage of CAD. In medical practice, coronary angiography is considered to be the most reliable basis for CAD diagnosis. However, unfortunately, due to the limitation of inspection equipment and expert resources, many low- and middle-income countries do not have the ability to perform coronary angiography. This has led to a large loss of life and medical burden. Therefore, many researchers expect to realize the accurate diagnosis of CAD based on conventional medical examination data with the help of machine learning and data mining technology. The goal of this study is to propose a model for early, accurate and rapid detection of CAD based on common medical test data. This model took the classical logistic regression algorithm, which is the most commonly used in medical model research as the classifier. The advantages of feature selection and feature combination of tree models were used to solve the problem of manual feature engineering in logical regression. At the same time, in order to solve the class imbalance problem in Z-Alizadeh Sani dataset, five different class balancing methods were applied to balance the dataset. In addition, according to the characteristics of the dataset, we also adopted appropriate preprocessing methods. These methods significantly improved the classification performance of logistic regression classifier in terms of accuracy, recall, precision, F1 score, specificity and AUC when used for CAD detection. The best accuracy, recall, F1 score, precision, specificity and AUC were 94.7%, 94.8%, 94.8%, 95.3%, 94.5% and 0.98, respectively. Experiments and results have confirmed that, according to common medical examination data, our proposed model can accurately identify CAD patients in the early stage of CAD. Our proposed model can be used to help clinicians make diagnostic decisions in clinical practice.
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Ensemble Machine Learning Model to Predict the Waterborne Syndrome. ALGORITHMS 2022. [DOI: 10.3390/a15030093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The COVID-19 epidemic has highlighted the significance of sanitization and maintaining hygienic access to clean water to reduce mortality and morbidity cases worldwide. Diarrhea is one of the prevalent waterborne diseases caused due to contaminated water in many low-income countries with similar living conditions. According to the latest statistics from the World Health Organization (WHO), diarrhea is among the top five primary causes of death worldwide in low-income nations. The condition affects people in every age group due to a lack of proper water used for daily living. In this study, a stacking ensemble machine learning model was employed against traditional models to extract clinical knowledge for better understanding patients’ characteristics; disease prevalence; hygienic conditions; quality of water used for cooking, bathing, and toiletries; chemicals used; therapist’s medications; and symptoms that are reflected in the field study data. Results revealed that the ensemble model provides higher accuracy with 98.90% as part of training and testing phases when experimented against frequently used J48, Naïve Bayes, SVM, NN, PART, Random Forest, and Logistic Regression models. Managing outcomes of this research in the early stages could assist people in low-income countries to have a better lifestyle, fewer infections, and minimize expensive hospital visits.
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