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González-González MA, Conde SV, Latorre R, Thébault SC, Pratelli M, Spitzer NC, Verkhratsky A, Tremblay MÈ, Akcora CG, Hernández-Reynoso AG, Ecker M, Coates J, Vincent KL, Ma B. Bioelectronic Medicine: a multidisciplinary roadmap from biophysics to precision therapies. Front Integr Neurosci 2024; 18:1321872. [PMID: 38440417 PMCID: PMC10911101 DOI: 10.3389/fnint.2024.1321872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/10/2024] [Indexed: 03/06/2024] Open
Abstract
Bioelectronic Medicine stands as an emerging field that rapidly evolves and offers distinctive clinical benefits, alongside unique challenges. It consists of the modulation of the nervous system by precise delivery of electrical current for the treatment of clinical conditions, such as post-stroke movement recovery or drug-resistant disorders. The unquestionable clinical impact of Bioelectronic Medicine is underscored by the successful translation to humans in the last decades, and the long list of preclinical studies. Given the emergency of accelerating the progress in new neuromodulation treatments (i.e., drug-resistant hypertension, autoimmune and degenerative diseases), collaboration between multiple fields is imperative. This work intends to foster multidisciplinary work and bring together different fields to provide the fundamental basis underlying Bioelectronic Medicine. In this review we will go from the biophysics of the cell membrane, which we consider the inner core of neuromodulation, to patient care. We will discuss the recently discovered mechanism of neurotransmission switching and how it will impact neuromodulation design, and we will provide an update on neuronal and glial basis in health and disease. The advances in biomedical technology have facilitated the collection of large amounts of data, thereby introducing new challenges in data analysis. We will discuss the current approaches and challenges in high throughput data analysis, encompassing big data, networks, artificial intelligence, and internet of things. Emphasis will be placed on understanding the electrochemical properties of neural interfaces, along with the integration of biocompatible and reliable materials and compliance with biomedical regulations for translational applications. Preclinical validation is foundational to the translational process, and we will discuss the critical aspects of such animal studies. Finally, we will focus on the patient point-of-care and challenges in neuromodulation as the ultimate goal of bioelectronic medicine. This review is a call to scientists from different fields to work together with a common endeavor: accelerate the decoding and modulation of the nervous system in a new era of therapeutic possibilities.
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Affiliation(s)
- María Alejandra González-González
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, United States
- Department of Pediatric Neurology, Baylor College of Medicine, Houston, TX, United States
| | - Silvia V. Conde
- iNOVA4Health, NOVA Medical School, Faculdade de Ciências Médicas, NOVA University, Lisbon, Portugal
| | - Ramon Latorre
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Stéphanie C. Thébault
- Laboratorio de Investigación Traslacional en salud visual (D-13), Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Querétaro, Mexico
| | - Marta Pratelli
- Neurobiology Department, Kavli Institute for Brain and Mind, UC San Diego, La Jolla, CA, United States
| | - Nicholas C. Spitzer
- Neurobiology Department, Kavli Institute for Brain and Mind, UC San Diego, La Jolla, CA, United States
| | - Alexei Verkhratsky
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
- Achucarro Centre for Neuroscience, IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
- Department of Forensic Analytical Toxicology, School of Forensic Medicine, China Medical University, Shenyang, China
- International Collaborative Center on Big Science Plan for Purinergic Signaling, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of Stem Cell Biology, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | - Marie-Ève Tremblay
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Molecular Medicine, Université Laval, Québec City, QC, Canada
- Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, BC, Canada
| | - Cuneyt G. Akcora
- Department of Computer Science, University of Central Florida, Orlando, FL, United States
| | | | - Melanie Ecker
- Department of Biomedical Engineering, University of North Texas, Denton, TX, United States
| | | | - Kathleen L. Vincent
- Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, TX, United States
| | - Brandy Ma
- Stanley H. Appel Department of Neurology, Houston Methodist Hospital, Houston, TX, United States
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Barreto TDO, Veras NVR, Cardoso PH, Fernandes FRDS, Medeiros LPDS, Bezerra MV, de Andrade FMQ, Pinheiro CDO, Sánchez-Gendriz I, Silva GJPC, Rodrigues LF, de Morais AHF, dos Santos JPQ, Paiva JC, de Andrade IGM, Valentim RADM. Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil. Front Artif Intell 2023; 6:1290022. [PMID: 38145230 PMCID: PMC10748397 DOI: 10.3389/frai.2023.1290022] [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: 09/08/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.
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Affiliation(s)
- Tiago de Oliveira Barreto
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | - Nícolas Vinícius Rodrigues Veras
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Pablo Holanda Cardoso
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Felipe Ricardo dos Santos Fernandes
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | | | - Maria Valéria Bezerra
- Secretary of Public Health of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | | | | | - Ignacio Sánchez-Gendriz
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
| | - Gleyson José Pinheiro Caldeira Silva
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Leandro Farias Rodrigues
- Brazilian Company of Hospital Services (EBSERH), University Hospital of Pelotas, Federal University of Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brazil
| | - Antonio Higor Freire de Morais
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - João Paulo Queiroz dos Santos
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Jailton Carlos Paiva
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, Rio Grande do Norte, Brazil
| | - Ion Garcia Mascarenhas de Andrade
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil
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Carmina D, Benfenati V, Simonelli C, Rotolo A, Cardano P, Grovale N, Mangoni di S Stefano L, de Santo T, Zamboni R, Palermo V, Muccini M, De Seta F. Innovative solutions for disease management. Bioelectron Med 2023; 9:28. [PMID: 38053220 DOI: 10.1186/s42234-023-00131-4] [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: 07/24/2023] [Accepted: 11/06/2023] [Indexed: 12/07/2023] Open
Abstract
The increasing prevalence of chronic diseases is a driver for emerging big data technologies for healthcare including digital platforms for data collection, systems for active patient engagement and education, therapy specific predictive models, optimized patient pathway models. Powerful bioelectronic medicine tools for data collection, analysis and visualization allow for joint processing of large volumes of heterogeneous data, which in turn can produce new insights about patient outcomes and alternative interpretations of clinical patterns that can lead to implementation of optimized clinical decisions and clinical patient pathway by healthcare professionals.With this perspective, we identify innovative solutions for disease management and evaluate their impact on patients, payers and society, by analyzing their impact in terms of clinical outcomes (effectiveness, safety, and quality of life) and economic outcomes (cost-effectiveness, savings, and productivity).As a result, we propose a new approach based on the main pillars of innovation in the disease management area, i.e. progressive patient care models, patient-centric approaches, bioelectronics for precise medicine, and lean management that, combined with an increase in appropriate private-public-citizen-partnership, leads towards Patient-Centric Healthcare.
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Affiliation(s)
- Dafni Carmina
- Medtronic Clinical & Regulatory Solutions - Study & Scientific Solutions, Via Aurelia 866, Roma, 00165, Italy.
| | - Valentina Benfenati
- Consiglio Nazionale delle Ricerche, Istituto per la Sintesi Organica e Fotoreattività, via Gobetti 101, Bologna, 40129, Italy.
| | - Claudia Simonelli
- Medtronic Clinical & Regulatory Solutions - Study & Scientific Solutions, Via Aurelia 866, Roma, 00165, Italy
| | - Alessia Rotolo
- Consiglio Nazionale delle Ricerche, Istituto per lo Studio dei Materiali Nanostrutturati, via Gobetti 101, Bologna, 40129, Italy
| | - Paola Cardano
- Medtronic Clinical & Regulatory Solutions - Study & Scientific Solutions, Via Aurelia 866, Roma, 00165, Italy
| | - Nicoletta Grovale
- Medtronic Clinical & Regulatory Solutions - Study & Scientific Solutions, Via Aurelia 866, Roma, 00165, Italy
| | | | - Tiziana de Santo
- Medtronic Clinical & Regulatory Solutions - Study & Scientific Solutions, Via Aurelia 866, Roma, 00165, Italy
| | - Roberto Zamboni
- Consiglio Nazionale delle Ricerche, Istituto per la Sintesi Organica e Fotoreattività, via Gobetti 101, Bologna, 40129, Italy
| | - Vincenzo Palermo
- Consiglio Nazionale delle Ricerche, Istituto per la Sintesi Organica e Fotoreattività, via Gobetti 101, Bologna, 40129, Italy
| | - Michele Muccini
- Consiglio Nazionale delle Ricerche, Istituto per lo Studio dei Materiali Nanostrutturati, via Gobetti 101, Bologna, 40129, Italy
- Mister Smart Innovation S, via Gobetti 101, Bologna, 40129, Italy
| | - Francesco De Seta
- Medtronic Clinical & Regulatory Solutions - Study & Scientific Solutions, Via Aurelia 866, Roma, 00165, Italy
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Li H, Drukker K, Hu Q, Whitney HM, Fuhrman JD, Giger ML. Predicting intensive care need for COVID-19 patients using deep learning on chest radiography. J Med Imaging (Bellingham) 2023; 10:044504. [PMID: 37608852 PMCID: PMC10440543 DOI: 10.1117/1.jmi.10.4.044504] [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: 01/01/2023] [Revised: 07/12/2023] [Accepted: 08/01/2023] [Indexed: 08/24/2023] Open
Abstract
Purpose Image-based prediction of coronavirus disease 2019 (COVID-19) severity and resource needs can be an important means to address the COVID-19 pandemic. In this study, we propose an artificial intelligence/machine learning (AI/ML) COVID-19 prognosis method to predict patients' needs for intensive care by analyzing chest X-ray radiography (CXR) images using deep learning. Approach The dataset consisted of 8357 CXR exams from 5046 COVID-19-positive patients as confirmed by reverse transcription polymerase chain reaction (RT-PCR) tests for the SARS-CoV-2 virus with a training/validation/test split of 64%/16%/20% on a by patient level. Our model involved a DenseNet121 network with a sequential transfer learning technique employed to train on a sequence of gradually more specific and complex tasks: (1) fine-tuning a model pretrained on ImageNet using a previously established CXR dataset with a broad spectrum of pathologies; (2) refining on another established dataset to detect pneumonia; and (3) fine-tuning using our in-house training/validation datasets to predict patients' needs for intensive care within 24, 48, 72, and 96 h following the CXR exams. The classification performances were evaluated on our independent test set (CXR exams of 1048 patients) using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of distinguishing between those COVID-19-positive patients who required intensive care following the imaging exam and those who did not. Results Our proposed AI/ML model achieved an AUC (95% confidence interval) of 0.78 (0.74, 0.81) when predicting the need for intensive care 24 h in advance, and at least 0.76 (0.73, 0.80) for 48 h or more in advance using predictions based on the AI prognostic marker derived from CXR images. Conclusions This AI/ML prediction model for patients' needs for intensive care has the potential to support both clinical decision-making and resource management.
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Affiliation(s)
- Hui Li
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Karen Drukker
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Qiyuan Hu
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Heather M. Whitney
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Jordan D. Fuhrman
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Maryellen L. Giger
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
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Tiwari S, Chanak P, Singh SK. A Review of the Machine Learning Algorithms for Covid-19 Case Analysis. IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE 2023; 4:44-59. [PMID: 36908643 PMCID: PMC9983698 DOI: 10.1109/tai.2022.3142241] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/25/2021] [Indexed: 11/09/2022]
Abstract
The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemic prediction, researchers and authorities have given more attention to simple statistical and epidemiological methodologies. The inadequacy and absence of medical testing for diagnosing and identifying a solution is one of the key challenges in preventing the spread of COVID-19. A few statistical-based improvements are being strengthened to answer this challenge, resulting in a partial resolution up to a certain level. ML have advocated a wide range of intelligence-based approaches, frameworks, and equipment to cope with the issues of the medical industry. The application of inventive structure, such as ML and other in handling COVID-19 relevant outbreak difficulties, has been investigated in this article. The major goal of this article is to 1) Examining the impact of the data type and data nature, as well as obstacles in data processing for COVID-19. 2) Better grasp the importance of intelligent approaches like ML for the COVID-19 pandemic. 3) The development of improved ML algorithms and types of ML for COVID-19 prognosis. 4) Examining the effectiveness and influence of various strategies in COVID-19 pandemic. 5) To target on certain potential issues in COVID-19 diagnosis in order to motivate academics to innovate and expand their knowledge and research into additional COVID-19-affected industries.
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Affiliation(s)
- Shrikant Tiwari
- Department of Computer Science and EngineeringIndian Institute of Technology (BHU) Varanasi 221005 India
| | - Prasenjit Chanak
- Department of Computer Science and EngineeringIndian Institute of Technology (BHU) Varanasi 221005 India
| | - Sanjay Kumar Singh
- Department of Computer Science and EngineeringIndian Institute of Technology (BHU) Varanasi 221005 India
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Arowolo MO, Ogundokun RO, Misra S, Agboola BD, Gupta B. Machine learning-based IoT system for COVID-19 epidemics. COMPUTING 2023; 105. [PMCID: PMC8886203 DOI: 10.1007/s00607-022-01057-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The planet earth has been facing COVID-19 epidemic as a challenge in recent time. It is predictable that the world will be fighting the pandemic by taking precautions steps before an operative vaccine is found. The IoT produces huge data volumes, whether private or public, through the invention of IoT devices in the form of smart devices with an improved rate of IoT data generation. A lot of devices interact with each other in the IoT ecosystem through the cloud or servers. Various techniques have been presented in recent time, using data mining approach have proven help detect possible cases of coronaviruses. Therefore, this study uses machine learning technique (ABC and SVM) to predict COVID-19 for IoT data system. The system used two machine learning techniques which are Artificial Bee Colony algorithm with Support Vector Machine classifier on a San Francisco COVID-19 dataset. The system was evaluated using confusion matrix and had a 95% accuracy, 95% sensitivity, 95% specificity, 97% precision, 96% F1 score, 89% Matthews correlation coefficient for ABC-L-SVM and 97% accuracy, 95% sensitivity, 100% specificity, 100% precision, 97% F1 score, 93.1% Matthews correlation coefficient for ABC-Q-SVM. In conclusion, the system shows that the process of dimensionality reduction utilizing ABC feature extraction techniques can boost the classification production for SVM. It was observed that fetching relevant information from IoT systems before classification is relatively beneficial.
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Affiliation(s)
| | | | - Sanjay Misra
- Department of Computer Science and Communication, Ostfold University College, Halden, Norway
| | | | - Brij Gupta
- Department of Computer Science and Information Engineering, Asia University, Taichung, 40704 Taiwan
- King Abdulaziz University, Jeddah, 21589 Saudi Arabia
- National Institute of Technology Kurukshetra, Kurukshetra, 136119 Haryana India
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Debnath S, Koppel R, Saadi N, Potak D, Weinberger B, Zanos TP. Prediction of intrapartum fever using continuously monitored vital signs and heart rate variability. Digit Health 2023; 9:20552076231187594. [PMID: 37448783 PMCID: PMC10336767 DOI: 10.1177/20552076231187594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023] Open
Abstract
Objectives Neonatal early onset sepsis (EOS), bacterial infection during the first seven days of life, is difficult to diagnose because presenting signs are non-specific, but early diagnosis before birth can direct life-saving treatment for mother and baby. Specifically, maternal fever during labor from placental infection is the strongest predictor of EOS. Alterations in maternal heart rate variability (HRV) may precede development of intrapartum fever, enabling incipient EOS detection. The objective of this work was to build a predictive model for intrapartum fever. Methods Continuously measured temperature, heart rate, and beat-to-beat RR intervals were obtained from wireless sensors on women (n = 141) in labor; traditional manual vital signs were taken every 3-6 hours. Validated measures of HRV were calculated in moving 5-minute windows of RR intervals: standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive differences (RMSSD) between normal heartbeats. Results Fever (>38.0 °C) was detected by manual or continuous measurements in 48 women. Compared to afebrile mothers, average SDNN and RMSSD in febrile mothers decreased significantly (p < 0.001) at 2 and 3 hours before fever onset, respectively. This observed HRV divergence and raw recorded vitals were applied to a logistic regression model at various time horizons, up to 4-5 hours before fever onset. Model performance increased with decreasing time horizons, and a model built using continuous vital signs as input variables consistently outperformed a model built from episodic vital signs. Conclusions HRV-based predictive models could identify mothers at risk for fever and infants at risk for EOS, guiding maternal antibiotic prophylaxis and neonatal monitoring.
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Affiliation(s)
- Shubham Debnath
- Institute of Health System Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Robert Koppel
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Neonatal-Perinatal Medicine, Cohen Children's Medical Center, Queens, NY, USA
| | - Nafeesa Saadi
- Neonatal-Perinatal Medicine, Cohen Children's Medical Center, Queens, NY, USA
| | - Debra Potak
- Neonatal-Perinatal Medicine, Cohen Children's Medical Center, Queens, NY, USA
| | - Barry Weinberger
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Neonatal-Perinatal Medicine, Cohen Children's Medical Center, Queens, NY, USA
| | - Theodoros P Zanos
- Institute of Health System Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
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8
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Lyu Y, Xu Q, Yang Z, Liu J. Prediction of patient choice tendency in medical decision-making based on machine learning algorithm. Front Public Health 2023; 11:1087358. [PMID: 36908484 PMCID: PMC9998498 DOI: 10.3389/fpubh.2023.1087358] [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: 11/02/2022] [Accepted: 02/07/2023] [Indexed: 03/14/2023] Open
Abstract
Objective Machine learning (ML) algorithms, as an early branch of artificial intelligence technology, can effectively simulate human behavior by training on data from the training set. Machine learning algorithms were used in this study to predict patient choice tendencies in medical decision-making. Its goal was to help physicians understand patient preferences and to serve as a resource for the development of decision-making schemes in clinical treatment. As a result, physicians and patients can have better conversations at lower expenses, leading to better medical decisions. Method Patient medical decision-making tendencies were predicted by primary survey data obtained from 248 participants at third-level grade-A hospitals in China. Specifically, 12 predictor variables were set according to the literature review, and four types of outcome variables were set based on the optimization principle of clinical diagnosis and treatment. That is, the patient's medical decision-making tendency, which is classified as treatment effect, treatment cost, treatment side effect, and treatment experience. In conjunction with the study's data characteristics, three ML classification algorithms, decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), were used to predict patients' medical decision-making tendency, and the performance of the three types of algorithms was compared. Results The accuracy of the DT algorithm for predicting patients' choice tendency in medical decision making is 80% for treatment effect, 60% for treatment cost, 56% for treatment side effects, and 60% for treatment experience, followed by the KNN algorithm at 78%, 66%, 74%, 84%, and the SVM algorithm at 82%, 76%, 80%, 94%. At the same time, the comprehensive evaluation index F1-score of the DT algorithm are 0.80, 0.61, 0.58, 0.60, the KNN algorithm are 0.75, 0.65, 0.71, 0.84, and the SVM algorithm are 0.81, 0.74, 0.73, 0.94. Conclusion Among the three ML classification algorithms, SVM has the highest accuracy and the best performance. Therefore, the prediction results have certain reference values and guiding significance for physicians to formulate clinical treatment plans. The research results are helpful to promote the development and application of a patient-centered medical decision assistance system, to resolve the conflict of interests between physicians and patients and assist them to realize scientific decision-making.
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Affiliation(s)
- Yuwen Lyu
- Institute of Humanities and Social Sciences, Guangzhou Medical University, Guangzhou, China
| | - Qian Xu
- School of Health Management, Guangzhou Medical University, Guangzhou, China
| | - Zhenchao Yang
- The Eighth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Junrong Liu
- Institute of Humanities and Social Sciences, Guangzhou Medical University, Guangzhou, China
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The Convergence of FTIR and EVs: Emergence Strategy for Non-Invasive Cancer Markers Discovery. Diagnostics (Basel) 2022; 13:diagnostics13010022. [PMID: 36611313 PMCID: PMC9818376 DOI: 10.3390/diagnostics13010022] [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: 11/01/2022] [Revised: 12/01/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
In conjunction with imaging analysis, pathology-based assessments of biopsied tissue are the gold standard for diagnosing solid tumors. However, the disadvantages of tissue biopsies, such as being invasive, time-consuming, and labor-intensive, have urged the development of an alternate method, liquid biopsy, that involves sampling and clinical assessment of various bodily fluids for cancer diagnosis. Meanwhile, extracellular vesicles (EVs) are circulating biomarkers that carry molecular profiles of their cell or tissue origins and have emerged as one of the most promising biomarkers for cancer. Owing to the biological information that can be obtained through EVs' membrane surface markers and their cargo loaded with biomolecules such as nucleic acids, proteins, and lipids, EVs have become useful in cancer diagnosis and therapeutic applications. Fourier-transform infrared spectroscopy (FTIR) allows rapid, non-destructive, label-free molecular profiling of EVs with minimal sample preparation. Since the heterogeneity of EV subpopulations may result in complicated FTIR spectra that are highly diverse, computational-assisted FTIR spectroscopy is employed in many studies to provide fingerprint spectra of malignant and non-malignant samples, allowing classification with high accuracy, specificity, and sensitivity. In view of this, FTIR-EV approach carries a great potential in cancer detection. The progression of FTIR-based biomarker identification in EV research, the rationale of the integration of a computationally assisted approach, along with the challenges of clinical translation are the focus of this review.
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10
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Pavlov VA, Tracey KJ. Bioelectronic medicine: Preclinical insights and clinical advances. Neuron 2022; 110:3627-3644. [PMID: 36174571 PMCID: PMC10155266 DOI: 10.1016/j.neuron.2022.09.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 07/28/2022] [Accepted: 09/02/2022] [Indexed: 11/17/2022]
Abstract
The nervous system maintains homeostasis and health. Homeostatic disruptions underlying the pathobiology of many diseases can be controlled by bioelectronic devices targeting CNS and peripheral neural circuits. New insights into the regulatory functions of the nervous system and technological developments in bioelectronics drive progress in the emerging field of bioelectronic medicine. Here, we provide an overview of key aspects of preclinical research, translation, and clinical advances in bioelectronic medicine.
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Affiliation(s)
- Valentin A Pavlov
- Institute of Bioelectronic Medicine, the Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA; Elmezzi Graduate School of Molecular Medicine, Northwell Health, Manhasset, NY, USA; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
| | - Kevin J Tracey
- Institute of Bioelectronic Medicine, the Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA; Elmezzi Graduate School of Molecular Medicine, Northwell Health, Manhasset, NY, USA; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
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11
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Karthik R, Menaka R, Hariharan M, Kathiresan GS. AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions. Ing Rech Biomed 2022; 43:486-510. [PMID: 34336141 PMCID: PMC8312058 DOI: 10.1016/j.irbm.2021.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/14/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Background and objective In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
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Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - M Hariharan
- School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India
| | - G S Kathiresan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
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12
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Abbasgholizadeh Rahimi S, Cwintal M, Huang Y, Ghadiri P, Grad R, Poenaru D, Gore G, Zomahoun HTV, Légaré F, Pluye P. Application of Artificial Intelligence in Shared Decision Making: Scoping Review. JMIR Med Inform 2022; 10:e36199. [PMID: 35943 PMCID: PMC9399841 DOI: 10.2196/36199] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/16/2022] [Accepted: 04/21/2022] [Indexed: 12/04/2022] Open
Abstract
Background Artificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM. Objective We aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM. Methods We performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. Results The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. Conclusions Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients’ values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.
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Affiliation(s)
- Samira Abbasgholizadeh Rahimi
- Department of Family Medicine, McGill University, Montreal, QC, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Mila-Quebec AI Institute, Montreal, QC, Canada
| | - Michelle Cwintal
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada
| | - Yuhui Huang
- Department of Integrated Studies in Education, McGill University, Montreal, QC, Canada
| | - Pooria Ghadiri
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Roland Grad
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Dan Poenaru
- Department of Pediatric Surgery, McGill University Health Centre, Montreal, QC, Canada
| | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, QC, Canada
| | - Hervé Tchala Vignon Zomahoun
- Centre de recherche en santé durable, Centre intégré universitaire de santé et services sociaux de la Capitale-Nationale, Quebec City, QC, Canada.,Quebec Support for People and Patient-Oriented Research and Trials Unit, Quebec City, QC, Canada
| | - France Légaré
- Centre de recherche en santé durable, Centre intégré universitaire de santé et services sociaux de la Capitale-Nationale, Quebec City, QC, Canada.,Quebec Support for People and Patient-Oriented Research and Trials Unit, Quebec City, QC, Canada.,Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université Laval, Quebec City, QC, Canada
| | - Pierre Pluye
- Department of Family Medicine, McGill University, Montreal, QC, Canada
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13
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Ge J, Kim WR, Lai JC, Kwong AJ. "Beyond MELD" - Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation. J Hepatol 2022; 76:1318-1329. [PMID: 35589253 DOI: 10.1016/j.jhep.2022.03.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/24/2022] [Accepted: 03/04/2022] [Indexed: 02/06/2023]
Abstract
In this review article, we discuss the model for end-stage liver disease (MELD) score and its dual purpose in general and transplant hepatology. As the landscape of liver disease and transplantation has evolved considerably since the advent of the MELD score, we summarise emerging concepts, methodologies, and technologies that may improve mortality prognostication in the future. Finally, we explore how these novel concepts and technologies may be incorporated into clinical practice.
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Affiliation(s)
- Jin Ge
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California - San Francisco, San Francisco, CA, USA
| | - W Ray Kim
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Jennifer C Lai
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California - San Francisco, San Francisco, CA, USA
| | - Allison J Kwong
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
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14
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Saleem F, AL-Ghamdi ASALM, Alassafi MO, AlGhamdi SA. Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:5099. [PMID: 35564493 PMCID: PMC9099605 DOI: 10.3390/ijerph19095099] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/11/2022] [Accepted: 04/20/2022] [Indexed: 01/27/2023]
Abstract
COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and has disrupted almost every field of life. Medical staff and laboratories are leading from the front, but researchers from various fields and governmental agencies have also proposed healthy ideas to protect each other. In this article, a Systematic Literature Review (SLR) is presented to highlight the latest developments in analyzing the COVID-19 data using machine learning and deep learning algorithms. The number of studies related to Machine Learning (ML), Deep Learning (DL), and mathematical models discussed in this research has shown a significant impact on forecasting and the spread of COVID-19. The results and discussion presented in this study are based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Out of 218 articles selected at the first stage, 57 met the criteria and were included in the review process. The findings are therefore associated with those 57 studies, which recorded that CNN (DL) and SVM (ML) are the most used algorithms for forecasting, classification, and automatic detection. The importance of the compartmental models discussed is that the models are useful for measuring the epidemiological features of COVID-19. Current findings suggest that it will take around 1.7 to 140 days for the epidemic to double in size based on the selected studies. The 12 estimates for the basic reproduction range from 0 to 7.1. The main purpose of this research is to illustrate the use of ML, DL, and mathematical models that can be helpful for the researchers to generate valuable solutions for higher authorities and the healthcare industry to reduce the impact of this epidemic.
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Affiliation(s)
- Farrukh Saleem
- Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Abdullah Saad AL-Malaise AL-Ghamdi
- Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Madini O. Alassafi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
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Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning. Viruses 2022; 14:v14030625. [PMID: 35337032 PMCID: PMC8955542 DOI: 10.3390/v14030625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/06/2022] [Accepted: 03/14/2022] [Indexed: 01/27/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has resulted in approximately 5 million deaths around the world with unprecedented consequences in people’s daily routines and in the global economy. Despite vast increases in time and money spent on COVID-19-related research, there is still limited information about the factors at the country level that affected COVID-19 transmission and fatality in EU. The paper focuses on the identification of these risk factors using a machine learning (ML) predictive pipeline and an associated explainability analysis. To achieve this, a hybrid dataset was created employing publicly available sources comprising heterogeneous parameters from the majority of EU countries, e.g., mobility measures, policy responses, vaccinations, and demographics/generic country-level parameters. Data pre-processing and data exploration techniques were initially applied to normalize the available data and decrease the feature dimensionality of the data problem considered. Then, a linear ε-Support Vector Machine (ε-SVM) model was employed to implement the regression task of predicting the number of deaths for each one of the three first pandemic waves (with mean square error of 0.027 for wave 1 and less than 0.02 for waves 2 and 3). Post hoc explainability analysis was finally applied to uncover the rationale behind the decision-making mechanisms of the ML pipeline and thus enhance our understanding with respect to the contribution of the selected country-level parameters to the prediction of COVID-19 deaths in EU.
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16
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Hung CM, Shi HY, Lee PH, Chang CS, Rau KM, Lee HM, Tseng CH, Pei SN, Tsai KJ, Chiu CC. Potential and role of artificial intelligence in current medical healthcare. Artif Intell Cancer 2022; 3:1-10. [DOI: 10.35713/aic.v3.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/31/2021] [Accepted: 02/20/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is defined as the digital computer or computer-controlled robot's ability to mimic intelligent conduct and crucial thinking commonly associated with intelligent beings. The application of AI technology and machine learning in medicine have allowed medical practitioners to provide patients with better quality of services; and current advancements have led to a dramatic change in the healthcare system. However, many efficient applications are still in their initial stages, which need further evaluations to improve and develop these applications. Clinicians must recognize and acclimate themselves with the developments in AI technology to improve their delivery of healthcare services; but for this to be possible, a significant revision of medical education is needed to provide future leaders with the required competencies. This article reviews the potential and limitations of AI in healthcare, as well as the current medical application trends including healthcare administration, clinical decision assistance, patient health monitoring, healthcare resource allocation, medical research, and public health policy development. Also, future possibilities for further clinical and scientific practice were also summarized.
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Affiliation(s)
- Chao-Ming Hung
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Hon-Yi Shi
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Business Management, National Sun Yat-Sen University, Kaohsiung 80420, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
| | - Po-Huang Lee
- College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
- Department of Surgery, E-Da Hospital, Kaohsiung 82445, Taiwan
| | - Chao-Sung Chang
- Department of Hematology & Oncology, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Kun-Ming Rau
- Department of Hematology & Oncology, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Hui-Ming Lee
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Cheng-Hao Tseng
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
- Department of Gastroenterology and Hepatology, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- Department of Gastroenterology and Hepatology, E-Da Hospital, Kaohsiung 82445, Taiwan
| | - Sung-Nan Pei
- Department of Hematology & Oncology, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Kuen-Jang Tsai
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
- Department of Medical Education and Research, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
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Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors. Vaccines (Basel) 2022; 10:vaccines10030366. [PMID: 35334998 PMCID: PMC8955470 DOI: 10.3390/vaccines10030366] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 02/04/2023] Open
Abstract
Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.
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Abstract
Unique pneumonia due to an unknown source emerged in December 2019 in the city of Wuhan, China. Consequently, the World Health Organization (WHO) declared this condition as a new coronavirus disease-19 also known as COVID-19 on February 11, 2020, which on March 13, 2020 was declared as a pandemic. The virus that causes COVID-19 was found to have a similar genome (80% similarity) with the previously known acute respiratory syndrome also known as SARS-CoV. The novel virus was later named Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 falls in the family of Coronaviridae which is further divided into Nidovirales and another subfamily called Orthocoronavirinae. The four generations of the coronaviruses belongs to the Orthocoronavirinae family that consists of alpha, beta, gamma and delta coronavirus which are denoted as α-CoV, β-CoV, γ-CoV, δ-CoV respectively. The α-CoV and β-CoVs are mainly known to infect mammals whereas γ-CoV and δ-CoV are generally found in birds. The β-CoVs also comprise of SARS-CoV and also include another virus that was found in the Middle East called the Middle East respiratory syndrome virus (MERS-CoV) and the cause of current pandemic SARS-CoV-2. These viruses initially cause the development of pneumonia in the patients and further development of a severe case of acute respiratory distress syndrome (ARDS) and other related symptoms that can be fatal leading to death.
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19
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Guerrier C, McDonnell C, Magoc T, Fishe JN, Harle CA. Understanding Health Care Administrators’ Data and Information Needs for Decision Making during the COVID-19 Pandemic: A Qualitative Study at an Academic Health System. MDM Policy Pract 2022; 7:23814683221089844. [PMID: 35368410 PMCID: PMC8972941 DOI: 10.1177/23814683221089844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/02/2022] [Indexed: 11/23/2022] Open
Abstract
Objective. The COVID-19 pandemic created an unprecedented strain on the health care system, and administrators had to make many critical decisions to respond appropriately. This study sought to understand how health care administrators used data and information for decision making during the first 6 mo of the COVID-19 pandemic. Materials and Methods. We conducted semistructured interviews with administrators across University of Florida (UF) Health. We performed an inductive thematic analysis of the transcripts. Results. Four themes emerged from the interviews: 1) common types of health systems or hospital operations data; 2) public health and other external data sources; 3) data interaction, integration, and exchange; and 4) novelty and evolution in data, information, or tools used over time. Participants illustrated the organizational, public health, and regional information they considered essential (e.g., hospital census, community positivity rate, etc.). Participants named specific challenges they faced due to data quality and timeliness. Participants elaborated on the necessity of data integration, validation, and coordination across different boundaries (e.g., different hospital systems in the same metro areas, public health agencies at the local, state, and federal level, etc.). Participants indicated that even within the first 6 mo of the COVID-19 pandemic, the data and tools used for making critical decisions changed. Discussion. While existing medical informatics infrastructure can facilitate decision making in pandemic response, data may not always be readily available in a usable format. Interoperable infrastructure and data standardization across multiple health systems would help provide more reliable and timely information for decision making. Conclusion. Our findings contribute to future discussions of improving data infrastructure and developing harmonized data standards needed to facilitate critical decisions at multiple health care system levels.
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Affiliation(s)
- Christina Guerrier
- Center for Data Solutions, University of Florida Health Science Center, Jacksonville, Florida, USA
| | - Cara McDonnell
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Tanja Magoc
- Integrated Data Repository, University of Florida, Gainesville, Florida, USA
| | - Jennifer N. Fishe
- Center for Data Solutions, University of Florida Health Science Center, Jacksonville, Florida, USA
| | - Christopher A. Harle
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
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Chen J, Lim CP, Tan KH, Govindan K, Kumar A. Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments. ANNALS OF OPERATIONS RESEARCH 2021:1-24. [PMID: 34785834 PMCID: PMC8582343 DOI: 10.1007/s10479-021-04373-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/19/2021] [Indexed: 06/13/2023]
Abstract
Pandemic events, particularly the current Covid-19 disease, compel organisations to re-formulate their day-to-day operations for achieving various business goals such as cost reduction. Unfortunately, small and medium enterprises (SMEs) making up more than 95% of all businesses is the hardest hit sector. This has urged SMEs to rethink their operations to survive through pandemic events. One key area is the use of new technologies pertaining to digital transformation for optimizing pandemic preparedness and minimizing business disruptions. This is especially true from the perspective of digitizing asset management methodologies in the era of Industry 4.0 under pandemic environments. Incidentally, human-centric approaches have become increasingly important in predictive maintenance through the exploitation of digital tools, especially when the workforce is increasingly interacting with new technologies such as Artificial Intelligence (AI) and Internet-of-Things devices for condition monitoring in equipment maintenance services. In this research, we propose an AI-based human-centric decision support framework for predictive maintenance in asset management, which can facilitate prompt and informed decision-making under pandemic environments. For predictive maintenance of complex systems, an enhanced trust-based ensemble model is introduced to undertake imbalanced data issues. A human-in-the-loop mechanism is incorporated to exploit the tacit knowledge elucidated from subject matter experts for providing decision support. Evaluations with both benchmark and real-world databases demonstrate the effectiveness of the proposed framework for addressing imbalanced data issues in predictive maintenance tasks. In the real-world case study, an accuracy rate of 82% is achieved, which indicates the potential of the proposed framework in assisting business sustainability pertaining to asset predictive maintenance under pandemic environments.
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Affiliation(s)
- Jacky Chen
- Institute for Intelligent Systems Research and Innovation, Deakin University, Melbourne, Australia
| | - Chee Peng Lim
- Institute for Intelligent Systems Research and Innovation, Deakin University, Melbourne, Australia
| | - Kim Hua Tan
- Operations Management and Information Systems Division, Nottingham University Business School, Nottingham, UK
| | - Kannan Govindan
- Center for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, Odense, Denmark
| | - Ajay Kumar
- AIM Research Center on AI in Value Creation, EMLYON Business School, Paris, France
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Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing Artificial Intelligence for Clinical Decision-Making. Front Digit Health 2021; 3:645232. [PMID: 34713115 PMCID: PMC8521931 DOI: 10.3389/fdgth.2021.645232] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 06/01/2021] [Indexed: 11/13/2022] Open
Abstract
Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.
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Affiliation(s)
- Chris Giordano
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, United States
| | - Meghan Brennan
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, United States
| | - Basma Mohamed
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, United States
| | - Parisa Rashidi
- J. Clayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - François Modave
- Department of Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States
| | - Patrick Tighe
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, United States
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Hasimbegovic E, Papp L, Grahovac M, Krajnc D, Poschner T, Hasan W, Andreas M, Gross C, Strouhal A, Delle-Karth G, Grabenwöger M, Adlbrecht C, Mach M. A Sneak-Peek into the Physician's Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis. J Pers Med 2021; 11:jpm11111062. [PMID: 34834414 PMCID: PMC8622882 DOI: 10.3390/jpm11111062] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/10/2021] [Accepted: 10/16/2021] [Indexed: 12/22/2022] Open
Abstract
Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.
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Affiliation(s)
- Ena Hasimbegovic
- Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, 1090 Vienna, Austria; (E.H.); (T.P.); (M.A.); (C.G.)
- Division of Cardiology, Department of Internal Medicine II, Medical University of Vienna, 1090 Vienna, Austria
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (L.P.); (D.K.)
| | - Marko Grahovac
- Division of Nuclear Medicine, Medical University of Vienna, 1090 Vienna, Austria;
| | - Denis Krajnc
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (L.P.); (D.K.)
| | - Thomas Poschner
- Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, 1090 Vienna, Austria; (E.H.); (T.P.); (M.A.); (C.G.)
| | - Waseem Hasan
- Faculty of Medicine, Imperial College London, London SW7 2AZ, UK;
| | - Martin Andreas
- Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, 1090 Vienna, Austria; (E.H.); (T.P.); (M.A.); (C.G.)
| | - Christoph Gross
- Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, 1090 Vienna, Austria; (E.H.); (T.P.); (M.A.); (C.G.)
- Vienna North Hospital—Floridsdorf Clinic and the Karl Landsteiner Institute for Cardiovascular and Critical Care Research, 1090 Vienna, Austria
| | - Andreas Strouhal
- Department of Cardiovascular Surgery, Hospital Hietzing and the Karl Landsteiner Institute for Cardiovascular and Critical Care Research, 1090 Vienna, Austria; (A.S.); (G.D.-K.); (C.A.)
| | - Georg Delle-Karth
- Department of Cardiovascular Surgery, Hospital Hietzing and the Karl Landsteiner Institute for Cardiovascular and Critical Care Research, 1090 Vienna, Austria; (A.S.); (G.D.-K.); (C.A.)
| | - Martin Grabenwöger
- Faculty of Medicine, Sigmund Freud University, 1090 Vienna, Austria;
- Imed19—Internal Medicine Doebling, 1090 Vienna, Austria
| | - Christopher Adlbrecht
- Department of Cardiovascular Surgery, Hospital Hietzing and the Karl Landsteiner Institute for Cardiovascular and Critical Care Research, 1090 Vienna, Austria; (A.S.); (G.D.-K.); (C.A.)
- Imed19—Internal Medicine Doebling, 1090 Vienna, Austria
| | - Markus Mach
- Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, 1090 Vienna, Austria; (E.H.); (T.P.); (M.A.); (C.G.)
- Department of Cardiovascular Surgery, Hospital Hietzing and the Karl Landsteiner Institute for Cardiovascular and Critical Care Research, 1090 Vienna, Austria; (A.S.); (G.D.-K.); (C.A.)
- Correspondence: ; Tel.: +43-40400-52620
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23
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Debnath S, Levy TJ, Bellehsen M, Schwartz RM, Barnaby DP, Zanos S, Volpe BT, Zanos TP. A method to quantify autonomic nervous system function in healthy, able-bodied individuals. Bioelectron Med 2021; 7:13. [PMID: 34446089 PMCID: PMC8394599 DOI: 10.1186/s42234-021-00075-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 07/20/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The autonomic nervous system (ANS) maintains physiological homeostasis in various organ systems via parasympathetic and sympathetic branches. ANS function is altered in common diffuse and focal conditions and heralds the beginning of environmental and disease stresses. Reliable, sensitive, and quantitative biomarkers, first defined in healthy participants, could discriminate among clinically useful changes in ANS function. This framework combines controlled autonomic testing with feature extraction during physiological responses. METHODS Twenty-one individuals were assessed in two morning and two afternoon sessions over two weeks. Each session included five standard clinical tests probing autonomic function: squat test, cold pressor test, diving reflex test, deep breathing, and Valsalva maneuver. Noninvasive sensors captured continuous electrocardiography, blood pressure, breathing, electrodermal activity, and pupil diameter. Heart rate, heart rate variability, mean arterial pressure, electrodermal activity, and pupil diameter responses to the perturbations were extracted, and averages across participants were computed. A template matching algorithm calculated scaling and stretching features that optimally fit the average to an individual response. These features were grouped based on test and modality to derive sympathetic and parasympathetic indices for this healthy population. RESULTS A significant positive correlation (p = 0.000377) was found between sympathetic amplitude response and body mass index. Additionally, longer duration and larger amplitude sympathetic and longer duration parasympathetic responses occurred in afternoon testing sessions; larger amplitude parasympathetic responses occurred in morning sessions. CONCLUSIONS These results demonstrate the robustness and sensitivity of an algorithmic approach to extract multimodal responses from standard tests. This novel method of quantifying ANS function can be used for early diagnosis, measurement of disease progression, or treatment evaluation. TRIAL REGISTRATION This study registered with Clinicaltrials.gov , identifier NCT04100486 . Registered September 24, 2019, https://www.clinicaltrials.gov/ct2/show/NCT04100486 .
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Affiliation(s)
- Shubham Debnath
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, 350 Community Dr, Manhasset, NY, 11030, USA
| | - Todd J Levy
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, 350 Community Dr, Manhasset, NY, 11030, USA
| | - Mayer Bellehsen
- Department of Psychiatry, Unified Behavioral Health Center and World Trade Center Health Program, Northwell Health, Bay Shore, NY, USA
| | - Rebecca M Schwartz
- Department of Occupational Medicine, Epidemiology and Prevention, Northwell Health, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Center for Disaster Health, Trauma, and Resilience, New York, NY, USA
- Northwell Health, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Douglas P Barnaby
- Northwell Health, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Northwell Health, Institute of Health Innovations and Outcomes Research, Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Stavros Zanos
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, 350 Community Dr, Manhasset, NY, 11030, USA
| | - Bruce T Volpe
- Northwell Health, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Northwell Health, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Theodoros P Zanos
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, 350 Community Dr, Manhasset, NY, 11030, USA.
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24
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Reeves JJ, Pageler NM, Wick EC, Melton GB, Tan YHG, Clay BJ, Longhurst CA. The Clinical Information Systems Response to the COVID-19 Pandemic. Yearb Med Inform 2021; 30:105-125. [PMID: 34479384 PMCID: PMC8416224 DOI: 10.1055/s-0041-1726513] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE The year 2020 was predominated by the coronavirus disease 2019 (COVID-19) pandemic. The objective of this article is to review the areas in which clinical information systems (CIS) can be and have been utilized to support and enhance the response of healthcare systems to pandemics, focusing on COVID-19. METHODS PubMed/MEDLINE, Google Scholar, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies pertaining to CIS, pandemics, and COVID-19 through October 2020. The most informative and detailed studies were highlighted, while many others were referenced. RESULTS CIS were heavily relied upon by health systems and governmental agencies worldwide in response to COVID-19. Technology-based screening tools were developed to assist rapid case identification and appropriate triaging. Clinical care was supported by utilizing the electronic health record (EHR) to onboard frontline providers to new protocols, offer clinical decision support, and improve systems for diagnostic testing. Telehealth became the most rapidly adopted medical trend in recent history and an essential strategy for allowing safe and effective access to medical care. Artificial intelligence and machine learning algorithms were developed to enhance screening, diagnostic imaging, and predictive analytics - though evidence of improved outcomes remains limited. Geographic information systems and big data enabled real-time dashboards vital for epidemic monitoring, hospital preparedness strategies, and health policy decision making. Digital contact tracing systems were implemented to assist a labor-intensive task with the aim of curbing transmission. Large scale data sharing, effective health information exchange, and interoperability of EHRs remain challenges for the informatics community with immense clinical and academic potential. CIS must be used in combination with engaged stakeholders and operational change management in order to meaningfully improve patient outcomes. CONCLUSION Managing a pandemic requires widespread, timely, and effective distribution of reliable information. In the past year, CIS and informaticists made prominent and influential contributions in the global response to the COVID-19 pandemic.
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Affiliation(s)
- J. Jeffery Reeves
- Department of Surgery, University of California, San Diego, La Jolla, California, USA
| | - Natalie M. Pageler
- Department of Pediatrics, Division of Critical Care Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Elizabeth C. Wick
- Department of Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Genevieve B. Melton
- Department of Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Yu-Heng Gamaliel Tan
- Department of Orthopedics, Chief Medical Information Officer, Ng Teng Fong General Hospital, National University Health System, Singapore
| | - Brian J. Clay
- Department of Medicine, Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA
| | - Christopher A. Longhurst
- Department of Medicine, Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA
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25
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Migriño JR, Batangan ARU. Using machine learning to create a decision tree model to predict outcomes of COVID-19 cases in the Philippines. Western Pac Surveill Response J 2021; 12:56-64. [PMID: 34703636 PMCID: PMC8521127 DOI: 10.5365/wpsar.2021.12.3.831] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE The aim of this study was to create a decision tree model with machine learning to predict the outcomes of COVID-19 cases from data publicly available in the Philippine Department of Health (DOH) COVID Data Drop. METHODS The study design was a cross-sectional records review of the DOH COVID Data Drop for 25 August 2020. Resolved cases that had either recovered or died were used as the final data set. Machine learning processes were used to generate, train and validate a decision tree model. RESULTS A list of 132 939 resolved COVID-19 cases was used. The notification rates and case fatality rates were higher among males (145.67 per 100 000 and 2.46%, respectively). Most COVID-19 cases were clustered among people of working age, and older cases had higher case fatality rates. The majority of cases were from the National Capital Region (590.20 per 100 000), and the highest case fatality rate (5.83%) was observed in Region VII. The decision tree model prioritized age and history of hospital admission as predictors of mortality. The model had high accuracy (81.42%), sensitivity (81.65%), specificity (81.41%) and area under the curve (0.876) but a poor F-score (16.74%). DISCUSSION The model predicted higher case fatality rates among older people. For cases aged > 51 years, a history of hospital admission increased the probability of COVID-19-related death. We recommend that more comprehensive primary COVID-19 data sets be used to create more robust prognostic models.
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Affiliation(s)
- Julius R. Migriño
- San Beda University College of Medicine, Manila, Philippines
- School of Medicine and Public Health, Ateneo de Manila University, Pasig City, Philippines
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26
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Alhasan M, Hasaneen M. Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic. Comput Med Imaging Graph 2021; 91:101933. [PMID: 34082281 PMCID: PMC8123377 DOI: 10.1016/j.compmedimag.2021.101933] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/15/2021] [Accepted: 04/27/2021] [Indexed: 12/13/2022]
Abstract
The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovation to address the universal healthcare concerns. The novel coronavirus holds a substantial foothold of planet earth which is combatted by digital interventions across afflicted geographical boundaries and territories. This study aims to explore the trends of modern healthcare technologies and Artificial Intelligence (AI) during COVID-19 crisis, define the concepts and clinical role of AI in the mitigation of COVID-19, investigate and correlate the efficacy of AI-enabled technology in medical imaging during COVID-19 and determine advantages, drawbacks, and challenges of artificial intelligence during COVID-19 pandemic. The paper applied systematic review approach using a deliberated research protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart. Digital technologies can coordinate COVID-19 responses in a cascade fashion that extends from the clinical care facility to the exterior of the pending viral epicenter. With cases of healthcare robotics, aerial drones, and the internet of things as evidentiary examples. PCR tests and medical imaging are the frontier diagnostics of COVID-19. Computed tomography helped to correct the accuracy variation of PCR tests at a clinical sensitivity of 98 %. Artificial intelligence can enable autonomous COVID-19 responses using techniques like machine learning. Technology could be an endless system of innovation and opportunities when sourced effectively. Scientists can utilize technology to resolve global concerns challenging the history of tangible possibility. Digital interventions have enhanced the responses to COVID-19, magnified the role of medical imaging amid the COVID-19 crisis and have exposed healthcare professionals to the opportunity of contactless care.
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Affiliation(s)
- Mustafa Alhasan
- Radiography and Medical Imaging Department, Fatima College of Health Sciences, United Arab Emirates; Radiologic Technology Program, Applied Medical Sciences College, Jordan University of Science and Technology, Jordan.
| | - Mohamed Hasaneen
- Radiography and Medical Imaging Department, Fatima College of Health Sciences, United Arab Emirates.
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27
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Shamout FE, Shen Y, Wu N, Kaku A, Park J, Makino T, Jastrzębski S, Witowski J, Wang D, Zhang B, Dogra S, Cao M, Razavian N, Kudlowitz D, Azour L, Moore W, Lui YW, Aphinyanaphongs Y, Fernandez-Granda C, Geras KJ. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. NPJ Digit Med 2021; 4:80. [PMID: 33980980 PMCID: PMC8115328 DOI: 10.1038/s41746-021-00453-0] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/19/2021] [Indexed: 12/23/2022] Open
Abstract
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
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Affiliation(s)
| | - Yiqiu Shen
- Center for Data Science, New York University, New York, NY, USA
| | - Nan Wu
- Center for Data Science, New York University, New York, NY, USA
| | - Aakash Kaku
- Center for Data Science, New York University, New York, NY, USA
| | - Jungkyu Park
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, USA
| | - Taro Makino
- Center for Data Science, New York University, New York, NY, USA
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Stanisław Jastrzębski
- Center for Data Science, New York University, New York, NY, USA
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, NY, USA
| | - Jan Witowski
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, NY, USA
| | - Duo Wang
- Department of Population Health, NYU Langone Health, New York, NY, USA
| | - Ben Zhang
- Department of Population Health, NYU Langone Health, New York, NY, USA
| | - Siddhant Dogra
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Meng Cao
- Department of Medicine, NYU Langone Health, New York, NY, USA
| | - Narges Razavian
- Center for Data Science, New York University, New York, NY, USA
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Department of Population Health, NYU Langone Health, New York, NY, USA
| | - David Kudlowitz
- Department of Medicine, NYU Langone Health, New York, NY, USA
| | - Lea Azour
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - William Moore
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, NY, USA
| | | | - Carlos Fernandez-Granda
- Center for Data Science, New York University, New York, NY, USA
- Department of Mathematics, Courant Institute, New York University, New York, NY, USA
| | - Krzysztof J Geras
- Center for Data Science, New York University, New York, NY, USA.
- Department of Radiology, NYU Langone Health, New York, NY, USA.
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, NY, USA.
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28
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Chen Z, Russo NW, Miller MM, Murphy RX, Burmeister DB. An observational study to develop a scoring system and model to detect risk of hospital admission due to COVID-19. J Am Coll Emerg Physicians Open 2021; 2:e12406. [PMID: 33817689 PMCID: PMC8011617 DOI: 10.1002/emp2.12406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 01/28/2021] [Accepted: 02/24/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND COVID-19 has caused an unprecedented global health emergency. The strains of such a pandemic can overwhelm hospital capacity. Efficient clinical decision-making is crucial for proper healthcare resource utilization in this crisis. Using observational study data, we set out to create a predictive model that could anticipate which COVID-19 patients would likely be admitted and developed a scoring tool that could be used in the clinical setting and for population risk stratification. METHODS We retrospectively evaluated data from COVID-19 patients across a network of 6 hospitals in northeastern Pennsylvania. Analysis was limited to age, gender, and historical variables. After creating a variable importance plot, we chose a selection of the best predictors to train a logistic regression model. Variable selection was done using a lasso regularization technique. Using the coefficients in our logistic regression model, we then created a scoring tool and validated the score on a test set data. RESULTS A total of 6485 COVID-19 patients were included in our analysis, of which 707 were hospitalized. The biggest predictors of patient hospitalization included age, a history of hypertension, diabetes, chronic heart disease, gender, tobacco use, and chronic kidney disease. The logistic regression model demonstrated an AUC of 0.81. The coefficients for our logistic regression model were used to develop a scoring tool. Low-, intermediate-, and high-risk patients were deemed to have a 3.5%, 26%, and 38% chance of hospitalization, respectively. The best predictors of hospitalization included age (odds ratio [OR] = 1.03, confidence interval [CI] = 1.02-1.03), diabetes (OR = 2.08, CI = 1.69-2.57), hypertension (OR = 2.36, CI = 1.90-2.94), chronic heart disease (OR = 1.53, CI = 1.22-1.91), and male gender (OR = 1.32, CI = 1.11-1.58). CONCLUSIONS Using retrospective observational data from a 6-hospital network, we determined risk factors for admission and developed a predictive model and scoring tool for use in the clinical and population setting that could anticipate admission for COVID-19 patients.
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Affiliation(s)
- Zhe Chen
- Department of Emergency and Hospital Medicine/USF Morsani College of Medicine, Lehigh Valley Health NetworkAllentownPennsylvaniaUSA
| | - Nicholas W. Russo
- Department of Emergency and Hospital Medicine/USF Morsani College of Medicine, Lehigh Valley Health NetworkAllentownPennsylvaniaUSA
| | - Matthew M. Miller
- Department of Emergency and Hospital Medicine/USF Morsani College of Medicine, Lehigh Valley Health NetworkAllentownPennsylvaniaUSA
| | - Robert X. Murphy
- Department of SurgeryDivision of Plastic Surgery, Lehigh Valley Health NetworkAllentownPennsylvaniaUSA
| | - David B. Burmeister
- Department of Emergency and Hospital Medicine/USF Morsani College of Medicine, Lehigh Valley Health NetworkAllentownPennsylvaniaUSA
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29
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Shahid O, Nasajpour M, Pouriyeh S, Parizi RM, Han M, Valero M, Li F, Aledhari M, Sheng QZ. Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance. J Biomed Inform 2021; 117:103751. [PMID: 33771732 PMCID: PMC7987503 DOI: 10.1016/j.jbi.2021.103751] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/06/2021] [Accepted: 03/11/2021] [Indexed: 12/15/2022]
Abstract
COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.
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Affiliation(s)
- Osama Shahid
- Department of Information Technology, Kennesaw State University, Marietta, GA, USA.
| | - Mohammad Nasajpour
- Department of Information Technology, Kennesaw State University, Marietta, GA, USA.
| | - Seyedamin Pouriyeh
- Department of Information Technology, Kennesaw State University, Marietta, GA, USA.
| | - Reza M Parizi
- Department of Software Engineering and Game Development, Kennesaw State University, Marietta, GA, USA.
| | - Meng Han
- Department of Information Technology, Kennesaw State University, Marietta, GA, USA.
| | - Maria Valero
- Department of Information Technology, Kennesaw State University, Marietta, GA, USA.
| | - Fangyu Li
- Department of Electrical and Computer Engineering, Kennesaw State University, Marietta, GA, USA.
| | - Mohammed Aledhari
- Department of Computer Science, Kennesaw State University, Marietta, GA, USA.
| | - Quan Z Sheng
- Department of Computing, Macquarie University, Sydney, Australia.
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30
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Mangoni AA, Erre GL. Translating evidence into practice during the COVID-19 pandemic: pitfalls and mileages. Ther Adv Drug Saf 2021; 12:2042098621998876. [PMID: 33796258 PMCID: PMC7968020 DOI: 10.1177/2042098621998876] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Affiliation(s)
- Arduino A. Mangoni
- Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University and Flinders Medical Centre, Bedford Park, SA 5042, Australia
| | - Gian Luca Erre
- Dipartimento di Scienze Mediche, Università degli Studi di Sassari, Sassari, Italy, and Dipartimento di Specialità Mediche, Azienda Ospedaliero-Universitaria di Sassari, Sassari, Italy
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31
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Zanville N, Cohen B, Gray TF, Phillips J, Linder L, Starkweather A, Yeager KA, Cooley ME. The Oncology Nursing Society Rapid Review and Research Priorities for Cancer Care in the Context of COVID-19. Oncol Nurs Forum 2021; 48:131-145. [PMID: 33600397 DOI: 10.1188/21.onf.131-145] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
OBJECTIVES To identify research priorities addressing COVID-19 that build on the 2019-2022 Oncology Nursing Society (ONS) Research Agenda, in alignment with ONS's mission to promote excellence in oncology nursing and quality cancer care. METHODS & VARIABLES Priority areas were identified using a multistep approach combining rapid review of the literature; consultation with experts/stakeholders; and review of priorities from other funding agencies, public health, and cancer-focused organizations. RESULTS The rapid research response team identified five priority areas for research related to COVID-19. IMPLICATIONS FOR NURSING Oncology nurses are well positioned to address the research priorities and cross-cutting themes identified through this review. The use of innovative methodologic approaches and attention to disparities are necessary to advance cancer care related to COVID-19.
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32
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Bolourani S, Brenner M, Wang P, McGinn T, Hirsch JS, Barnaby D, Zanos TP. A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation. J Med Internet Res 2021; 23:e24246. [PMID: 33476281 PMCID: PMC7879728 DOI: 10.2196/24246] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/18/2020] [Accepted: 01/18/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. OBJECTIVE Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. METHODS Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. RESULTS The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. CONCLUSIONS The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.
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Affiliation(s)
- Siavash Bolourani
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Max Brenner
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Ping Wang
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Thomas McGinn
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Jamie S Hirsch
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Douglas Barnaby
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Theodoros P Zanos
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
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Jayatilake SMDAC, Ganegoda GU. Involvement of Machine Learning Tools in Healthcare Decision Making. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6679512. [PMID: 33575021 PMCID: PMC7857908 DOI: 10.1155/2021/6679512] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/18/2020] [Accepted: 01/09/2021] [Indexed: 01/07/2023]
Abstract
In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence.
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Tayarani N MH. Applications of artificial intelligence in battling against covid-19: A literature review. CHAOS, SOLITONS, AND FRACTALS 2021; 142:110338. [PMID: 33041533 PMCID: PMC7532790 DOI: 10.1016/j.chaos.2020.110338] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/01/2020] [Indexed: 05/14/2023]
Abstract
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.
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Affiliation(s)
- Mohammad-H Tayarani N
- Biocomputation Group, School of Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom
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Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic. SUSTAINABLE OPERATIONS AND COMPUTERS 2021; 2. [PMCID: PMC8052508 DOI: 10.1016/j.susoc.2021.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Background and aims Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart. Methods We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure. Results AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed. Conclusions Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.
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Abstract
This 21st century is notable for experiencing so many disturbances at economic, social, cultural, and political levels in the entire world. The outbreak of novel corona virus 2019 (COVID-19) has been treated as a Public Health crisis of global Concern by the World Health Organization (WHO). Various outbreak models for COVID-19 are being utilized by researchers throughout the world to get well-versed decisions and impose significant control measures. Amid the standard methods for COVID-19 worldwide epidemic prediction, easy statistical, as well as epidemiological methods have got more consideration by researchers and authorities. One main difficulty in controlling the spreading of COVID-19 is the inadequacy and lack of medical tests for detecting as well as identifying a solution. To solve this problem, a few statistical-based advances are being enhanced and turn into a partial resolution up-to some level. To deal with the challenges of the medical field, a broad range of intelligent based methods, frameworks, and equipment have been recommended by Machine Learning (ML) and Deep Learning. As ML and DL have the ability of identifying and predicting patterns in complex large datasets, they are recognized as a suitable procedure for producing effective solutions for the diagnosis of COVID-19. In this paper, a perspective research has been conducted in the applicability of intelligent systems such as ML, DL and others in solving COVID-19 related outbreak issues. The main intention behind this study is (i) to understand the importance of intelligent approaches such as ML and DL for COVID-19 pandemic, (ii) discussing the efficiency and impact of these methods in the prognosis of COVID-19, (iii) the growth in the development of type of ML and advanced ML methods for COVID-19 prognosis,(iv) analyzing the impact of data types and the nature of data along with challenges in processing the data for COVID-19,(v) to focus on some future challenges in COVID-19 prognosis to inspire the researchers for innovating and enhancing their knowledge and research on other impacted sectors due to COVID-19.
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Islam MN, Inan TT, Rafi S, Akter SS, Sarker IH, Islam AKMN. A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic. IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE 2020; 1:258-270. [PMID: 35784006 PMCID: PMC8545030 DOI: 10.1109/tai.2021.3062771] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/31/2020] [Accepted: 02/24/2021] [Indexed: 11/16/2022]
Abstract
Artificial intelligence (AI) and machine learning (ML) have caused a paradigm shift in healthcare that can be used for decision support and forecasting by exploring medical data. Recent studies have shown that AI and ML can be used to fight COVID-19. The objective of this article is to summarize the recent AI- and ML-based studies that have addressed the pandemic. From an initial set of 634 articles, a total of 49 articles were finally selected through an inclusion-exclusion process. In this article, we have explored the objectives of the existing studies (i.e., the role of AI/ML in fighting the COVID-19 pandemic); the context of the studies (i.e., whether it was focused on a specific country-context or with a global perspective; the type and volume of the dataset; and the methodology, algorithms, and techniques adopted in the prediction or diagnosis processes). We have mapped the algorithms and techniques with the data type by highlighting their prediction/classification accuracy. From our analysis, we categorized the objectives of the studies into four groups: disease detection, epidemic forecasting, sustainable development, and disease diagnosis. We observed that most of these studies used deep learning algorithms on image-data, more specifically on chest X-rays and CT scans. We have identified six future research opportunities that we have summarized in this paper. Impact Statement: Artificial intelligence (AI) and machine learning(ML) methods have been widely used to assist in the fight against COVID-19 pandemic. A very few in-depth literature reviews have been conducted to synthesize the knowledge and identify future research agenda including a previously published review on data science for COVID-19 in this article. In this article, we synthesized reviewed recent literature that focuses on the usages and applications of AI and ML to fight against COVID-19. We have identified seven future research directions that would guide researchers to conduct future research. The most significant of these are: develop new treatment options, explore the contextual effect and variation in research outcomes, support the health care workforce, and explore the effect and variation in research outcomes based on different types of data.
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Affiliation(s)
- Muhammad Nazrul Islam
- Department of Computer Science, and EngineeringMilitary Institute of Science and TechnologyDhaka1216Bangladesh
| | - Toki Tahmid Inan
- Department of Computer ScienceGeorge Mason UniversityFairfaxVA22031USA
| | - Suzzana Rafi
- Department of Computer Science and EngineeringBangladesh University of Engineering and TechnologyDhaka1205Bangladesh
| | | | - Iqbal H. Sarker
- Department of Computer Science and EngineeringChittagong University of Engineering and TechnologyChittagong4349Bangladesh
| | - A. K. M. Najmul Islam
- LUT School of Engineering ScienceLUT UniversityLahti15210Finland
- Department of ComputingUniversity of Turku20500TurkuFinland
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Marques G, Agarwal D, de la Torre Díez I. Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network. Appl Soft Comput 2020; 96:106691. [PMID: 33519327 PMCID: PMC7836808 DOI: 10.1016/j.asoc.2020.106691] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 08/22/2020] [Accepted: 08/26/2020] [Indexed: 01/18/2023]
Abstract
COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors' knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario.
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Affiliation(s)
- Gonçalo Marques
- Department of Signal Theory and Communications, and Telematics Engineering University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Deevyankar Agarwal
- Department of Signal Theory and Communications, and Telematics Engineering University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Isabel de la Torre Díez
- Department of Signal Theory and Communications, and Telematics Engineering University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
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Abstract
International psychiatry is currently facing serious challenges triggered by the global economic crisis and the COVID-19 pandemic. These global events lead to the need to broaden our nosographic and therapeutic horizons, and to make use of the newest psychological approaches and the latest neuroscience acquisitions. The focus should be on the psychological consequences of the pandemic, not only on people suffering from mental disorders, but also on the general population, for which the risk of developing psychic symptoms appears to be increased. A population that needs special attention is that of health workers involved in the management of the pandemic. In facing these problems, psychiatry today can use numerous new clinical applications and technologies in the fields of precision medicine. These include genomics, neuroimaging, and microbiomics, which can also be integrated with each other through machine learning systems. They can provide new contributions both in treatment personalization and in the evolution of nosographic systems. Besides this, the contribution of psychotherapies and dynamic and clinical psychology appears to be indispensable for a complete understanding of the clinical and personological aspects of patients. This journal aims to include innovative studies deriving from original, clinical, and basic research in the fields of mental health, precision psychiatry, genomics, neuroimaging, neuropsychopharmacology, and dynamic and clinical psychology.
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