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Guo X, Zheng Z, Cheong KH, Zou Q, Tiwari P, Ding Y. Sequence homology score-based deep fuzzy network for identifying therapeutic peptides. Neural Netw 2024; 178:106458. [PMID: 38901093 DOI: 10.1016/j.neunet.2024.106458] [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: 12/18/2023] [Revised: 05/29/2024] [Accepted: 06/09/2024] [Indexed: 06/22/2024]
Abstract
The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923).
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Affiliation(s)
- Xiaoyi Guo
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; Quzhou People's Hospital, Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000, PR China; Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, S637371, Singapore.
| | - Ziyu Zheng
- Department of Mathematical Sciences, University of Nottingham Ningbo, Ningbo, 315100, PR China.
| | - Kang Hao Cheong
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, S637371, Singapore; College of Computing and Data Science, Nanyang Technological University, S639798, Singapore.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, PR China.
| | - Prayag Tiwari
- School of Information Technology, Halmstad University, Sweden.
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, PR China.
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Chee ML, Chee ML, Huang H, Mazzochi K, Taylor K, Wang H, Feng M, Ho AFW, Siddiqui FJ, Ong MEH, Liu N. Artificial intelligence and machine learning in prehospital emergency care: A scoping review. iScience 2023; 26:107407. [PMID: 37609632 PMCID: PMC10440716 DOI: 10.1016/j.isci.2023.107407] [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] [Indexed: 08/24/2023] Open
Abstract
Our scoping review provides a comprehensive analysis of the landscape of artificial intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field by highlighting the most studied AI applications and identifying the most common methodological approaches across 106 included studies. The findings indicate a promising future for AI in PEC, with many unique use cases, such as prognostication, demand prediction, resource optimization, and the Internet of Things continuous monitoring systems. Comparisons with other approaches showed AI outperforming clinicians and non-AI algorithms in most cases. However, most studies were internally validated and retrospective, highlighting the need for rigorous prospective validation of AI applications before implementation in clinical settings. We identified knowledge and methodological gaps using an evidence map, offering a roadmap for future investigators. We also discussed the significance of explainable AI for establishing trust in AI systems among clinicians and facilitating real-world validation of AI models.
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Affiliation(s)
- Marcel Lucas Chee
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Mark Leonard Chee
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Haotian Huang
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Katelyn Mazzochi
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Kieran Taylor
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Han Wang
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Andrew Fu Wah Ho
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Fahad Javaid Siddiqui
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Marcus Eng Hock Ong
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Nan Liu
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
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Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K, Ahmady S, Koohestani HR. The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2023; 11:e38. [PMID: 37215232 PMCID: PMC10197918 DOI: 10.22037/aaem.v11i1.1974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Introduction Artificial Inteligence (AI) application in emergency medicine is subject to ethical and legal inconsistencies. The purposes of this study were to map the extent of AI applications in emergency medicine, to identify ethical issues related to the use of AI, and to propose an ethical framework for its use. Methods A comprehensive literature collection was compiled through electronic databases/internet search engines (PubMed, Web of Science Platform, MEDLINE, Scopus, Google Scholar/Academia, and ERIC) and reference lists. We considered studies published between 1 January 2014 and 6 October 2022. Articles that did not self-classify as studies of an AI intervention, those that were not relevant to Emergency Departments (EDs), and articles that did not report outcomes or evaluations were excluded. Descriptive and thematic analyses of data extracted from the included articles were conducted. Results A total of 137 out of the 2175 citations in the original database were eligible for full-text evaluation. Of these articles, 47 were included in the scoping review and considered for theme extraction. This review covers seven main areas of AI techniques in emergency medicine: Machine Learning (ML) Algorithms (10.64%), prehospital emergency management (12.76%), triage, patient acuity and disposition of patients (19.15%), disease and condition prediction (23.40%), emergency department management (17.03%), the future impact of AI on Emergency Medical Services (EMS) (8.51%), and ethical issues (8.51%). Conclusion There has been a rapid increase in AI research in emergency medicine in recent years. Several studies have demonstrated the potential of AI in diverse contexts, particularly when improving patient outcomes through predictive modelling. According to the synthesis of studies in our review, AI-based decision-making lacks transparency. This feature makes AI decision-making opaque.
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Affiliation(s)
| | | | - Karim Qayumi
- Centre of Excellence for Simulation Education and Innovation, Department of Surgery, University of British Columbia, Vancouver, BC, Canada
| | - Soleiman Ahmady
- Department of Medical Education, Virtual School of Medical Education & Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Reza Koohestani
- Department of Nursing, Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran
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Huang Y, Xu T, Yang Q, Pan C, Zhan L, Chen H, Zhang X, Chen C. Demand prediction of medical services in home and community-based services for older adults in China using machine learning. Front Public Health 2023; 11:1142794. [PMID: 37006569 PMCID: PMC10060662 DOI: 10.3389/fpubh.2023.1142794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/28/2023] [Indexed: 03/18/2023] Open
Abstract
BackgroundHome and community-based services are considered an appropriate and crucial caring method for older adults in China. However, the research examining demand for medical services in HCBS through machine learning techniques and national representative data has not yet been carried out. This study aimed to address the absence of a complete and unified demand assessment system for home and community-based services.MethodsThis was a cross-sectional study conducted on 15,312 older adults based on the Chinese Longitudinal Healthy Longevity Survey 2018. Models predicting demand were constructed using five machine-learning methods: Logistic regression, Logistic regression with LASSO regularization, Support Vector Machine, Random Forest, and Extreme Gradient Boosting (XGboost), and based on Andersen's behavioral model of health services use. Methods utilized 60% of older adults to develop the model, 20% of the samples to examine the performance of models, and the remaining 20% of cases to evaluate the robustness of the models. To investigate demand for medical services in HCBS, individual characteristics such as predisposing, enabling, need, and behavior factors constituted four combinations to determine the best model.ResultsRandom Forest and XGboost models produced the best results, in which both models were over 80% at specificity and produced robust results in the validation set. Andersen's behavioral model allowed for combining odds ratio and estimating the contribution of each variable of Random Forest and XGboost models. The three most critical features that affected older adults required medical services in HCBS were self-rated health, exercise, and education.ConclusionAndersen's behavioral model combined with machine learning techniques successfully constructed a model with reasonable predictors to predict older adults who may have a higher demand for medical services in HCBS. Furthermore, the model captured their critical characteristics. This method predicting demands could be valuable for the community and managers in arranging limited primary medical resources to promote healthy aging.
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Affiliation(s)
- Yucheng Huang
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Tingke Xu
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Qingren Yang
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Chengxi Pan
- The State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, China
| | - Lu Zhan
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Huajian Chen
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiangyang Zhang
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Xiangyang Zhang
| | - Chun Chen
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Center for Healthy China Research, Wenzhou Medical University, Wenzhou, Zhejiang, China
- *Correspondence: Chun Chen
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Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:2053-2061. [PMID: 35989154 DOI: 10.1016/j.jval.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/10/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.
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Affiliation(s)
- Woojung Lee
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
| | - Naomi Schwartz
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Aasthaa Bansal
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Sara Khor
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Noah Hammarlund
- Department of Health Services Research, Management & Policy, University of Florida, Gainesville, FL, USA
| | - Anirban Basu
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Beth Devine
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
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Gianquintieri L, Brovelli MA, Pagliosa A, Dassi G, Brambilla PM, Bonora R, Sechi GM, Caiani EG. Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9012. [PMID: 35897382 PMCID: PMC9330211 DOI: 10.3390/ijerph19159012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/13/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
Abstract
The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.
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Affiliation(s)
- Lorenzo Gianquintieri
- Electronics, Information and Biomedical Engineering Department, Politecnico di Milano, 20133 Milan, Italy;
| | - Maria Antonia Brovelli
- Civil and Environmental Engineering Department, Politecnico di Milano, 20133 Milan, Italy;
- Istituto per il Rilevamento Elettromagnetico dell’Ambiente, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy
| | - Andrea Pagliosa
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Gabriele Dassi
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Piero Maria Brambilla
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Rodolfo Bonora
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Giuseppe Maria Sechi
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Enrico Gianluca Caiani
- Electronics, Information and Biomedical Engineering Department, Politecnico di Milano, 20133 Milan, Italy;
- Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy
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Hybrid Machine Learning Models for Forecasting Surgical Case Volumes at a Hospital. AI 2021. [DOI: 10.3390/ai2040032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Recent developments in machine learning and deep learning have led to the use of multiple algorithms to make better predictions. Surgical units in hospitals allocate their resources for day surgeries based on the number of elective patients, which is mostly disrupted by emergency surgeries. Sixteen different models were constructed for this comparative study, including four simple and twelve hybrid models for predicting the demand for endocrinology, gastroenterology, vascular, urology, and pediatric surgical units. The four simple models used were seasonal autoregressive integrated moving average (SARIMA), support vector regression (SVR), multilayer perceptron (MLP), and long short-term memory (LSTM). The twelve hybrid models used were a combination of any two of the above-mentioned simple models, namely, SARIMA–SVR, SVR–SARIMA, SARIMA–MLP, MLP–SARIMA, SARIMA–LSTM, LSTM–SARIMA, SVR–MLP, MLP–SVR, SVR–LSTM, LSTM–SVR, MLP–LSTM, and LSTM–MLP. Data from the period 2012–2018 were used to build and test the models for each surgical unit. The results indicated that, in some cases, the simple LSTM model outperformed the others while, in other cases, there was a need for hybrid models. This shows that surgical units are unique in nature and need separate models for predicting their corresponding surgical volumes.
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Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time. MATHEMATICAL AND COMPUTATIONAL APPLICATIONS 2021. [DOI: 10.3390/mca26030056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is sensitive data because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.
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Cheong KH, Tang KJW, Zhao X, Koh JEW, Faust O, Gururajan R, Ciaccio EJ, Rajinikanth V, Acharya UR. An automated skin melanoma detection system with melanoma-index based on entropy features. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.05.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Monteiro JP, Ramos D, Carneiro D, Duarte F, Fernandes JM, Novais P. Meta‐learning and the new challenges of machine learning. INT J INTELL SYST 2021. [DOI: 10.1002/int.22549] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- José Pedro Monteiro
- Department of Informatics, Algoritmi Centre Universidade do Minho Felgueiras Portugal
| | - Diogo Ramos
- CIICESI, ESTG Politécnico do Porto Felgueiras Portugal
| | - Davide Carneiro
- Department of Informatics, Algoritmi Centre Universidade do Minho Felgueiras Portugal
- CIICESI, ESTG Politécnico do Porto Felgueiras Portugal
| | - Francisco Duarte
- Department of Informatics, Algoritmi Centre Universidade do Minho Felgueiras Portugal
| | - João M. Fernandes
- Department of Informatics, Algoritmi Centre Universidade do Minho Felgueiras Portugal
| | - Paulo Novais
- Department of Informatics, Algoritmi Centre Universidade do Minho Felgueiras Portugal
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Chee ML, Ong MEH, Siddiqui FJ, Zhang Z, Lim SL, Ho AFW, Liu N. Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18094749. [PMID: 33947006 PMCID: PMC8125462 DOI: 10.3390/ijerph18094749] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/20/2021] [Accepted: 04/21/2021] [Indexed: 02/07/2023]
Abstract
Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.
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Affiliation(s)
- Marcel Lucas Chee
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria 3800, Australia;
| | - Marcus Eng Hock Ong
- Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore; (M.E.H.O.); (F.J.S.); (A.F.W.H.)
- Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore
| | - Fahad Javaid Siddiqui
- Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore; (M.E.H.O.); (F.J.S.); (A.F.W.H.)
| | - Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China;
| | - Shir Lynn Lim
- Department of Cardiology, National University Heart Centre, Singapore 119074, Singapore;
| | - Andrew Fu Wah Ho
- Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore; (M.E.H.O.); (F.J.S.); (A.F.W.H.)
- Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore
| | - Nan Liu
- Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore; (M.E.H.O.); (F.J.S.); (A.F.W.H.)
- Health Service Research Centre, Singapore Health Services, Singapore 169856, Singapore
- Institute of Data Science, National University of Singapore, Singapore 117602, Singapore
- Correspondence:
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Zhao X, Ang CKE, Acharya UR, Cheong KH. Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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13
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Himeur Y, Alsalemi A, Bensaali F, Amira A. Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors. INT J INTELL SYST 2021. [DOI: 10.1002/int.22404] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Yassine Himeur
- Department of Electrical Engineering Qatar University Doha Qatar
| | | | - Faycal Bensaali
- Department of Electrical Engineering Qatar University Doha Qatar
| | - Abbes Amira
- Institute of Artificial Intelligence De Montfort University Leicester UK
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Watanabe O, Narita N, Katsuki M, Ishida N, Cai S, Otomo H, Yokota K. Prediction Model of Deep Learning for Ambulance Transports in Kesennuma City by Meteorological Data. Open Access Emerg Med 2021; 13:23-32. [PMID: 33536798 PMCID: PMC7850460 DOI: 10.2147/oaem.s293551] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 01/14/2021] [Indexed: 12/15/2022] Open
Abstract
PURPOSE With the aging population in Japan, the prediction of ambulance transports is needed to save the limited medical resources. Some meteorological factors were risks of ambulance transports, but it is difficult to predict in a classically statistical way because Japan has 4 seasons. We tried to make prediction models for ambulance transports using the deep learning (DL) framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), with the meteorological and calendarial variables. MATERIALS AND METHODS We retrospectively investigated the daily ambulance transports and meteorological data between 2017 and 2019. First, to confirm their association, we performed classically statistical analysis. Second, to test the DL framework's utility for ambulance transports prediction, we made 3 prediction models for daily ambulance transports (total daily ambulance transports more than 5 or not, cardiopulmonary arrest (CPA), and trauma) using meteorological and calendarial factors and evaluated their accuracies by internal cross-validation. RESULTS During the 1095 days of 3 years, the total ambulance transports were 5948, including 240 CPAs and 337 traumas. Cardiogenic CPA accounted for 72.3%, according to the Utstein classification. The relation between ambulance transports and meteorological parameters by polynomial curves were statistically obtained, but their r2s were small. On the other hand, all DL-based prediction models obtained satisfactory accuracies in the internal cross-validation. The areas under the curves obtained from each model were all over 0.947. CONCLUSION We could statistically make polynomial curves between the meteorological variables and the number of ambulance transport. We also preliminarily made DL-based prediction models. The DL-based prediction for daily ambulance transports would be used in the future, leading to solving the lack of medical resources in Japan.
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Affiliation(s)
- Ohmi Watanabe
- Kesennuma City Hospital, Kesennuma, Miyagi988-0181, Japan
| | - Norio Narita
- Department of Neurosurgery, Kesennuma City Hospital, Kesennuma, Miyagi988-0181, Japan
| | - Masahito Katsuki
- Department of Neurosurgery, Kesennuma City Hospital, Kesennuma, Miyagi988-0181, Japan
| | - Naoya Ishida
- Kesennuma City Hospital, Kesennuma, Miyagi988-0181, Japan
| | - Siqi Cai
- Kesennuma City Hospital, Kesennuma, Miyagi988-0181, Japan
| | - Hiroshi Otomo
- Department of Surgery, Kesennuma City Hospital, Kesennuma, Miyagi988-0181, Japan
| | - Kenichi Yokota
- Department of Surgery, Kesennuma City Hospital, Kesennuma, Miyagi988-0181, Japan
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15
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Tang KJW, Ang CKE, Constantinides T, Rajinikanth V, Acharya UR, Cheong KH. Artificial Intelligence and Machine Learning in Emergency Medicine. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2020.12.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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16
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Knowing in Nurses' Belief and Attitude about Patient Activation: A Validation of the Korean Clinician Support for Patient Activation Measure Using Rasch Analysis. Healthcare (Basel) 2020; 8:healthcare8040571. [PMID: 33348909 PMCID: PMC7766493 DOI: 10.3390/healthcare8040571] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/14/2020] [Accepted: 12/15/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Patient engagement is considered a critical factor in improving healthcare delivery. This study aimed to test the Korean version of the Clinician Support for Patient Activation Measure (CS-PAM) using Rasch analysis, and to explore nurses' beliefs about patient self-management. METHODS A cross-sectional, exploratory study design was employed. The staff nurses who were recruited from six hospitals were requested to complete the Korean CS-PAM. Their responses were subsequently subjected to Rasch analysis to validate the Korean CS-PAM. The CS-PAM was paraphrased into Korean using the standardized forward-backward translation method. RESULTS The internal consistency of the scale had good Cronbach's alpha value. For all items, the infit and outfit statistics fell well within the acceptable range of 0.5-1.5. This measure formed a unidimensional Guttman-like scale that explained 54.7% of the variance. CONCLUSIONS The Korean version of the CS-PAM showed good psychometric properties and appeared to be consistent with the meaning of the original CS-PAM. However, the items have a somewhat different ranking order when compared to the English and Dutch versions. The instrument might be useful for identifying the supportive beliefs and attitudes of nurses or healthcare providers in order to improve patient activation in healthcare.
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Ruggeri M, Drago C, Cadeddu C, Armuzzi A, Leone S, Marchetti M. The Determinants of Out-of-Pocket Expenditure in IBD Italian Patients. Results from the AMICI Survey. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E8156. [PMID: 33158223 PMCID: PMC7663576 DOI: 10.3390/ijerph17218156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 10/27/2020] [Accepted: 10/29/2020] [Indexed: 02/06/2023]
Abstract
Decision makers are used to consider Out-of-Pocket Expenditure (OOPE) within a health technology assessment framework in order to account for an indicator relying on the level of fairness and on the quality of care of a health system. In this paper, we provide estimates on the determinants of OOPE in Italy by using data coming from an observational cross-sectional study that enrolled a sample of 2526 patients suffering from inflammatory bowel diseases. We explore the association between OOPE and: (1) geographical location; (2) income effects; (3) performances in delivering healthcare. A regression model was used. Individuals' age were in the range of 18-88 (mean 44 ± 14.55). Forty-six percent were females, 54% were married and 19% held a bachelor degree. Ninety-six percent of respondents declared an OOPE >0 whose mean value was €960 ± €950. Individuals belonging to low-income and low-performance regions were more likely to declare an OOPE >0 (99%). Regression findings suggest that increases in OOPE could be considered as a response from patients aiming to compensate for lacks and inefficiencies in the public healthcare offers. Policymakers should consider increases in OOPE in patients with Inflammatory Bowel Diseases (IBDs) as an indicator of poor quality of care and poor fairness.
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Affiliation(s)
- Matteo Ruggeri
- National Center for HTA, Istituto Superiore di Sanità, 00161 Rome, Italy;
- School of Medicine, St. Camillus International University of Health Sciences, 00131 Rome, Italy
| | - Carlo Drago
- Faculty of Economics, Università Niccolò Cusano, 00166 Rome, Italy;
| | - Chiara Cadeddu
- School of Medicine, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (C.C.); (A.A.)
| | - Alessandro Armuzzi
- School of Medicine, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (C.C.); (A.A.)
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy
| | | | - Marco Marchetti
- National Center for HTA, Istituto Superiore di Sanità, 00161 Rome, Italy;
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Abstract
The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.
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Abstract
Over the years, fire departments have been searching for methods to identify their operational disruptions and establish strategies that allow them to efficiently organize their resources. The present work develops a methodology for breakage calculation and another for predicting disruptions based on machine learning techniques. The main objective is to establish indicators to identify the failures due to the temporal state of the organization in the human and vehicular material. Likewise, by forecasting disruptions, to determine strategies for the deployment or acquisition of the necessary armament. This would allow improving operational resilience and increasing the efficiency of the firemen over time. The methodology was applied to the Departmental Fire and Rescue Doubs (SDIS25) in France. However, it is generic enough to be extended and adapted to other fire departments. Considering a historic of breakdowns of 2017 and 2018, the best predictions of public service breakdowns for the year 2019, presented a root mean squared error of 2.5602 and a mean absolute error of 2.0240 on average with the XGBoost technique.
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