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Fallatah DI, Adekola HA. Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks. Infect Prev Pract 2024; 6:100382. [PMID: 39091623 PMCID: PMC11292357 DOI: 10.1016/j.infpip.2024.100382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 06/13/2024] [Indexed: 08/04/2024] Open
Abstract
Digital epidemiology is the process of investigating the dynamics of disease-related patterns, both social and clinical, as well as the causes of these trends in epidemiology. Digital epidemiology, utilising big data from a variety of digital sources, has emerged as a viable method for early detection and monitoring of viral outbreaks. The present review gives an overview of digital epidemiology, emphasising its importance in the timely detection of infectious disease outbreaks. Researchers may discover and track outbreaks in real time using digital data sources such as search engine queries, social media trends, and digital health records. However, data quality, concerns about privacy, and data interoperability must be addressed to maximise the effectiveness of digital epidemiology. As the global landscape of infectious diseases evolves, integrating digital epidemiology becomes critical to improving pandemic preparedness and response efforts. Integrating digital epidemiology into routine monitoring systems has the potential to improve global health outcomes and save lives in the event of viral outbreaks.
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Affiliation(s)
- Deema Ibrahim Fallatah
- Department of Clinical Laboratory Sciences, Prince Sattam bin Abdulaziz University, Saudi Arabia
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2
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Melon DE, Pillsbury HC, Harrill WC. Otolaryngic Allergy Patient Journey Mapping: A Framework for Allergy Immunotherapy Adherence. Laryngoscope 2024. [PMID: 39140220 DOI: 10.1002/lary.31690] [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: 02/18/2024] [Revised: 05/21/2024] [Accepted: 07/17/2024] [Indexed: 08/15/2024]
Abstract
OBJECTIVES Allergen-specific immunotherapy (AIT) is an effective treatment for allergic disease but requires long treatment duration and premature cessation is of significant concern. Drivers of premature cessation remain poorly understood and no predictive models currently exist. We hypothesized that a novel patient journey map and de novo real-time patient electronic health status instruments (eHSIs) could effectively capture patient perceived cost, commitment, and treatment benefit to identify individual patients at risk for premature AIT cessation. STUDY TYPE Cross-Sectional Observational Study. METHODS A single Otolaryngology allergy immunotherapy (AIT) program was studied over 5 years. Instances of premature cessation were classified. An Otolaryngic Allergy Patient Journey Map was developed to identify and target automated, real-time, patient-reported, electronic health status instrument responses. RESULTS Data capture was robust, with 61,406 data points collected and an eHSI survey completion rate of 81.3%. However, based on correlation analysis and logistic regression alone, real-time eHSI responses were not predictive of individual patient premature AIT cessation. A total of 597 AIT patients discontinued treatment prematurely: 64.4% stopping within the first year. Specifically, 74.0%-76.3% of subcutaneous AIT patients and 88.5%-100% of sublingual AIT patients did not complete the minimum recommended treatment duration of 3 years. CONCLUSION Patient journey mapping can aid in the design of longitudinal care models and patient engagement strategies. Yet, eHSI patient responses of perception of AIT cost, benefit, and convenience did not correlate with the likelihood of premature treatment cessation. Our imperfect clinical intuition may not account for the dynamic drivers of premature AIT discontinuation. Future development of predictive tools feed by large patient-centric data sets may be incorporated into routine practice resulting in delivery of a more streamlined and personalized approach with reduced premature AIT cessation, improved outcomes, and reduced health care expenditures. LEVEL OF EVIDENCE NA Laryngoscope, 2024.
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Affiliation(s)
- David E Melon
- Carolina Ear, Nose & Throat-Sinus and Allergy Center, PA, Hickory, North Carolina, U.S.A
| | - Harold C Pillsbury
- Department of Otolaryngology-Head and Neck Surgery, University of North Carolina School of Medicine, Chapel Hill, North Carolina, U.S.A
| | - Willard C Harrill
- Carolina Ear, Nose & Throat-Sinus and Allergy Center, PA, Hickory, North Carolina, U.S.A
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Ahn SJ. Real-World Research on Retinal Diseases Using Health Claims Database: A Narrative Review. Diagnostics (Basel) 2024; 14:1568. [PMID: 39061705 PMCID: PMC11276298 DOI: 10.3390/diagnostics14141568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/05/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024] Open
Abstract
Real-world data (RWD) has emerged as a crucial component in understanding and improving patient outcomes across various medical conditions, including retinal diseases. Health claims databases, generated from healthcare reimbursement claims, offer a comprehensive source of RWD, providing insights into patient outcomes, healthcare utilization, and treatment effectiveness. However, the use of these databases for research also presents unique challenges. This narrative review explores the role of real-world research on retinal diseases using health claims databases, highlighting their advantages, limitations, and potential contributions to advancing our understanding and management of the diseases. The review examines the applications of health claims databases in retinal disease research, including epidemiological studies, comparative effectiveness and safety analyses, economic burden assessments, and evaluations of patient outcomes and quality of care. Previous findings demonstrate the value of these databases in generating prevalence and incidence estimates, identifying risk factors and predictors, evaluating treatment effectiveness and safety, and understanding healthcare utilization patterns and costs associated with retinal diseases. Despite their strengths, health claims databases face challenges related to data limitations, biases, privacy concerns, and methodological issues. Accordingly, the review also explores future directions and opportunities, including advancements in data collection and analysis, integration with electronic health records, collaborative research networks and consortia, and the evolving regulatory landscape. These developments are expected to enhance the utility of health claims databases for retinal disease research, resulting in more comprehensive and impactful findings across diverse retinal disorders and robust real-world insights from a large population.
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Affiliation(s)
- Seong Joon Ahn
- Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul 04763, Republic of Korea
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Gurnani B, Kaur K, Lalgudi VG, Kundu G, Mimouni M, Liu H, Jhanji V, Prakash G, Roy AS, Shetty R, Gurav JS. Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review. J Fr Ophtalmol 2024; 47:104242. [PMID: 39013268 DOI: 10.1016/j.jfo.2024.104242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 07/18/2024]
Abstract
In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.
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Affiliation(s)
- B Gurnani
- Department of Cataract, Cornea, External Disease, Trauma, Ocular Surface and Refractive Surgery, ASG Eye Hospital, Jodhpur, Rajasthan, India.
| | - K Kaur
- Department of Cataract, Pediatric Ophthalmology and Strabismus, ASG Eye Hospital, Jodhpur, Rajasthan, India
| | - V G Lalgudi
- Department of Cornea, Refractive surgery, Ira G Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, State University of New York (SUNY), Buffalo, USA
| | - G Kundu
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India
| | - M Mimouni
- Department of Ophthalmology, Rambam Health Care Campus affiliated with the Bruce and Ruth Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - H Liu
- Department of Ophthalmology, University of Ottawa Eye Institute, Ottawa, Canada
| | - V Jhanji
- UPMC Eye Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - G Prakash
- Department of Ophthalmology, School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - A S Roy
- Narayana Nethralaya Foundation, Bangalore, India
| | - R Shetty
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India
| | - J S Gurav
- Department of Opthalmology, Armed Forces Medical College, Pune, India
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Alhur A. The Role of Informatics in Advancing Emergency Medicine: A Comprehensive Review. Cureus 2024; 16:e63979. [PMID: 39105014 PMCID: PMC11299705 DOI: 10.7759/cureus.63979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2024] [Indexed: 08/07/2024] Open
Abstract
Emergency Medicine Informatics (EMI) is a rapidly advancing field that utilizes information technology to enhance the delivery of emergency medical services. This comprehensive literature review explores the key components, benefits, challenges, and future directions of EMI. By integrating Electronic Health Records, Clinical Decision Support Systems, telemedicine, data analytics, interoperability, and patient monitoring systems, EMI has the potential to significantly improve patient outcomes and operational efficiency in emergency departments. However, the implementation of these technologies faces several obstacles, including interoperability issues, data security concerns, usability challenges, and high costs. This review highlights how these technologies are transforming emergency care, discusses the barriers to their implementation, and provides perspectives on potential solutions and future progress in the field.
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Affiliation(s)
- Anas Alhur
- Health Informatics, University of Hail College of Public Health and Health Informatics, Hail, SAU
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Mustapha A, Ishak I, Zaki NNM, Ismail-Fitry MR, Arshad S, Sazili AQ. Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review. Heliyon 2024; 10:e32189. [PMID: 38975107 PMCID: PMC11225673 DOI: 10.1016/j.heliyon.2024.e32189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/20/2024] [Accepted: 05/29/2024] [Indexed: 07/09/2024] Open
Abstract
Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.
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Affiliation(s)
- Abdul Mustapha
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Iskandar Ishak
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, 43400, Malaysia
| | - Nor Nadiha Mohd Zaki
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Animal Science, Faculty of Agriculture, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Mohammad Rashedi Ismail-Fitry
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Syariena Arshad
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
| | - Awis Qurni Sazili
- Halal Products Research Institute, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
- Department of Animal Science, Faculty of Agriculture, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia
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Zhou J, Hao J, Tang M, Sun H, Wang J, Li J, Qian Q. Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction. BMC Med Inform Decis Mak 2024; 24:178. [PMID: 38915008 PMCID: PMC11194906 DOI: 10.1186/s12911-024-02533-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 05/13/2024] [Indexed: 06/26/2024] Open
Abstract
OBJECTIVE This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). MATERIALS AND METHODS The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. RESULTS The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. DISCUSSION The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation. CONCLUSION The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.
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Affiliation(s)
- Jiayin Zhou
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jie Hao
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Mingkun Tang
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Haixia Sun
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jiayang Wang
- Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jiao Li
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Qing Qian
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
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Garcia-Vozmediano A, Maurella C, Ceballos LA, Crescio E, Meo R, Martelli W, Pitti M, Lombardi D, Meloni D, Pasqualini C, Ru G. Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy. Vet Res 2024; 55:72. [PMID: 38840261 PMCID: PMC11154984 DOI: 10.1186/s13567-024-01323-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 04/15/2024] [Indexed: 06/07/2024] Open
Abstract
Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015-2018 (n = 1969) and food surveillance data collected in 2014-2018 were used to develop ML algorithms. We integrated the monthly municipal human incidence with 27 potential predictors, including the observed prevalence of Salmonella in food. We applied the tree regression, random forest and gradient boosting algorithms considering different scenarios and evaluated their predictivity in terms of the mean absolute percentage error (MAPE) and R2. Using a similar dataset from the year 2019, spatiotemporal predictions and their relative sensitivities and specificities were obtained. Random forest and gradient boosting (R2 = 0.55, MAPE = 7.5%) outperformed the tree regression algorithm (R2 = 0.42, MAPE = 8.8%). Salmonella prevalence in food; spatial features; and monitoring efforts in ready-to-eat milk, fruits and vegetables, and pig meat products contributed the most to the models' predictivity, reducing the variance by 90.5%. Conversely, the number of positive samples obtained for specific food matrices minimally influenced the predictions (2.9%). Spatiotemporal predictions for 2019 showed sensitivity and specificity levels of 46.5% (due to the lack of some infection hotspots) and 78.5%, respectively. This study demonstrates the added value of integrating data from human and veterinary health services to develop predictive models of human salmonellosis occurrence, providing early warnings useful for mitigating foodborne disease impacts on public health.
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Affiliation(s)
- Aitor Garcia-Vozmediano
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy.
| | - Cristiana Maurella
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Leonardo A Ceballos
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Elisabetta Crescio
- Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Tecnológico, 64849, Monterrey, N.L., México
| | - Rosa Meo
- Department of Computer Science, University of Turin, Corso Svizzera 185, 10149, Turin, Italy
| | - Walter Martelli
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Monica Pitti
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Daniela Lombardi
- Piedmont Regional Service for the Epidemiology of Infectious Diseases (SeREMI), Via Venezia 6, 15121, Alessandria, Italy
| | - Daniela Meloni
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
| | - Chiara Pasqualini
- Piedmont Regional Service for the Epidemiology of Infectious Diseases (SeREMI), Via Venezia 6, 15121, Alessandria, Italy
| | - Giuseppe Ru
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta, Via Bologna 148, 10154, Turin, Italy
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Ruksakulpiwat S, Thorngthip S, Niyomyart A, Benjasirisan C, Phianhasin L, Aldossary H, Ahmed BH, Samai T. A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next? J Multidiscip Healthc 2024; 17:1603-1616. [PMID: 38628616 PMCID: PMC11020344 DOI: 10.2147/jmdh.s459946] [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: 01/16/2024] [Accepted: 03/05/2024] [Indexed: 04/19/2024] Open
Abstract
Background Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care. Objective This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care. Methods Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation. Results A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan. Conclusion This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.
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Affiliation(s)
- Suebsarn Ruksakulpiwat
- Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand
| | - Sutthinee Thorngthip
- Department of Nursing Siriraj Hospital, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Atsadaporn Niyomyart
- Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | | | - Lalipat Phianhasin
- Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand
| | - Heba Aldossary
- Department of Nursing, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Bootan Hasan Ahmed
- Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA
| | - Thanistha Samai
- Department of Public Health Nursing, Faculty of Nursing, Mahidol University, Nakhon Pathom, Thailand
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10
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Kamel M, Aleya S, Alsubih M, Aleya L. Microbiome Dynamics: A Paradigm Shift in Combatting Infectious Diseases. J Pers Med 2024; 14:217. [PMID: 38392650 PMCID: PMC10890469 DOI: 10.3390/jpm14020217] [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: 12/26/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 02/24/2024] Open
Abstract
Infectious diseases have long posed a significant threat to global health and require constant innovation in treatment approaches. However, recent groundbreaking research has shed light on a previously overlooked player in the pathogenesis of disease-the human microbiome. This review article addresses the intricate relationship between the microbiome and infectious diseases and unravels its role as a crucial mediator of host-pathogen interactions. We explore the remarkable potential of harnessing this dynamic ecosystem to develop innovative treatment strategies that could revolutionize the management of infectious diseases. By exploring the latest advances and emerging trends, this review aims to provide a new perspective on combating infectious diseases by targeting the microbiome.
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Affiliation(s)
- Mohamed Kamel
- Department of Medicine and Infectious Diseases, Faculty of Veterinary Medicine, Cairo University, Giza 11221, Egypt
| | - Sami Aleya
- Faculty of Medecine, Université de Bourgogne Franche-Comté, Hauts-du-Chazal, 25030 Besançon, France
| | - Majed Alsubih
- Department of Civil Engineering, King Khalid University, Guraiger, Abha 62529, Saudi Arabia
| | - Lotfi Aleya
- Laboratoire de Chrono-Environnement, Université de Bourgogne Franche-Comté, UMR CNRS 6249, La Bouloie, 25030 Besançon, France
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Wang M, Liu G, Ni Z, Yang Q, Li X, Bi Z. Acute kidney injury comorbidity analysis based on international classification of diseases-10 codes. BMC Med Inform Decis Mak 2024; 24:35. [PMID: 38310256 PMCID: PMC10837944 DOI: 10.1186/s12911-024-02435-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 01/22/2024] [Indexed: 02/05/2024] Open
Abstract
OBJECTIVE Acute kidney injury (AKI) is a clinical syndrome that occurs as a result of a dramatic decline in kidney function caused by a variety of etiological factors. Its main biomarkers, serum creatinine and urine output, are not effective in diagnosing early AKI. For this reason, this study provides insight into this syndrome by exploring the comorbidities of AKI, which may facilitate the early diagnosis of AKI. In addition, organ crosstalk in AKI was systematically explored based on comorbidities to obtain clinically reliable results. METHODS We collected data from the Medical Information Mart for Intensive Care-IV database on patients aged [Formula: see text] 18 years in intensive care units (ICU) who were diagnosed with AKI using the criteria proposed by Kidney Disease: Improving Global Outcomes. The Apriori algorithm was used to mine association rules on the diagnoses of 55,486 AKI and non-AKI patients in the ICU. The comorbidities of AKI mined were validated through the Electronic Intensive Care Unit database, the Colombian Open Health Database, and medical literature, after which comorbidity results were visualized using a disease network. Finally, organ diseases were identified and classified from comorbidities to investigate renal crosstalk with other distant organs in AKI. RESULTS We found 579 AKI comorbidities, and the main ones were disorders of lipoprotein metabolism, essential hypertension, and disorders of fluid, electrolyte, and acid-base balance. Of the 579 comorbidities, 554 were verifiable and 25 were new and not previously reported. In addition, crosstalk between the kidneys and distant non-renal organs including the liver, heart, brain, lungs, and gut was observed in AKI with the strongest heart-kidney crosstalk, followed by lung-kidney crosstalk. CONCLUSION The comorbidities mined in this study using association rules are scientific and may be used for the early diagnosis of AKI and the construction of AKI predictive models. Furthermore, the organ crosstalk results obtained through comorbidities may provide supporting information for the management of short- and long-term treatment practices for organ dysfunction.
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Affiliation(s)
- Menglu Wang
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, 511436, China
| | - Guangjian Liu
- Shenzhen Dymind Biotechnology Co., Ltd, Shenzhen, 518000, China
| | - Zhennan Ni
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, 511436, China
| | - Qianjun Yang
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, 511436, China
| | - Xiaojun Li
- Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
| | - Zhisheng Bi
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, 511436, China.
- Department of Emergency Medicine, the Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, China.
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Selvaraj B, Rajasekar E, Balaguru Rayappan JB. Machine Learning Approaches: Detecting the Disease Variants in Human-Exhaled Breath Biomarkers. ACS OMEGA 2024; 9:215-226. [PMID: 38222575 PMCID: PMC10785631 DOI: 10.1021/acsomega.3c03755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 12/06/2023] [Accepted: 12/11/2023] [Indexed: 01/16/2024]
Abstract
In recent days, the development of sensor-based medical devices has been found to be very effective for the prediction and analysis of the onset of diseases. The instigation of an electronic nose (eNose) device is profound and very useful in diverse applications. The analysis of exhaled breath biomarkers using eNose sensors has attained wider attention among researchers, and the prediction of multiple disease variants using the same is still an open research problem. In this work, an enhanced XGBooster classifier-based prediction mechanism was introduced to identify the disease variants based on the responses of commercially available metal oxide-based Figaro (Japan) sensors including TGS826, TGS822, TGS2600, and TGS2602. The implemented model secured 98.36% prediction accuracy in multiclass disease prediction and classification. The homemade one-dimensional metal oxide sensing elements such as ZnO, Cr-doped ZnO, and ZnO/NiO were integrated with the aforementioned sensor array for the specific detection of the three biomarkers of interest. This model has attained a classification accuracy of 99.77, 94.91, and 96.56% toward ammonia, ethanol, and acetone, respectively. And the multiclass disease biomarker classification accuracy of the readymade and homemade eNose prototype models was compared, and the results are summarized.
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Affiliation(s)
- Bhuvaneswari Selvaraj
- Centre
for Nanotechnology & Advanced Biomaterials (CeNTAB), SASTRA Deemed University, Thanjavur, Tamil Nadu 613 401, India
- School
of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur, Tamil Nadu 613 401, India
| | - Elakkiya Rajasekar
- Department
of Computer Science, Birla Institute of
Technology & Science, Pilani Dubai Campus, Dubai International Academic
City, Dubai 345055, United Arab Emirates
| | - John Bosco Balaguru Rayappan
- Centre
for Nanotechnology & Advanced Biomaterials (CeNTAB), SASTRA Deemed University, Thanjavur, Tamil Nadu 613 401, India
- School
of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur, Tamil Nadu 613 401, India
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13
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Nkwanyana A, Mathews V, Zachary I, Bhayani V. Skills and competencies in health data analytics for health professionals: a scoping review protocol. BMJ Open 2023; 13:e070596. [PMID: 37989378 PMCID: PMC10668260 DOI: 10.1136/bmjopen-2022-070596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 10/25/2023] [Indexed: 11/23/2023] Open
Abstract
INTRODUCTION Healthcare data analytics is a methodological approach to the systematic analysis of health data, and it provides opportunities for healthcare professionals to improve health system management, patient engagement, budgeting, planning and performing evidence-based decision-making. Literature suggests that certain skills and/or competencies for health professionals working with big data in health care would be required. A review of the skills and competencies in health data analytics required by health professionals is needed to support the development or re-engineering of curriculum for health professionals to ensure they develop the abilities to make evidence-based decisions that ultimately can lead to the effective and efficient functioning of a healthcare system. METHODS Using Arksey and O'Malley's framework, this study will review literature published in English from January 2012 to December 2022. The database search includes Academic Search Complete, CINAHL, and MEDLINE via EBSCOhost, PubMed, Science Direct, Scopus, and Taylor and Francis. The reference lists of key studies will be searched to identify additional appropriate studies to include. The review will be conducted using an inclusion and exclusion criteria. Iterative processes will be involved at the various stages of search strategy piloting, screening and data extraction. Articles will be reviewed through a two-step process (title and abstract, and full-text review) by at least two reviewers. Data will be described quantitatively and/or qualitatively and presented in diagrams and tables. ETHICS AND DISSEMINATION Ethical clearance has been received, and strict protocol measures will be followed to ensure the data reported is of quality and relevant to the review purpose. The results will be disseminated through a peer-reviewed scientific journal, presentation at national and/or international conferences, and other platforms such as social media (eg, LinkedIn, Twitter), and relevant stakeholders.
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Affiliation(s)
- Akhona Nkwanyana
- Department of Psychology, University of the Western Cape, Cape Town, Western Cape, South Africa
| | - Verona Mathews
- School of Public Health, University of the Western Cape, Cape Town, Western Cape, South Africa
| | - Iris Zachary
- Department of Health Management and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Vishwa Bhayani
- Department of Health Management and Informatics, University of Missouri, Columbia, Missouri, USA
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14
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Arodudu O, Foley R, Taghikhah F, Brennan M, Mills G, Ningal T. A health data led approach for assessing potential health benefits of green and blue spaces: Lessons from an Irish case study. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 345:118758. [PMID: 37690253 DOI: 10.1016/j.jenvman.2023.118758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 09/12/2023]
Abstract
Research producing evidence-based information on the health benefits of green and blue spaces often has within its design, the potential for inherent or implicit bias which can unconsciously orient the outcomes of such studies towards preconceived hypothesis. Many studies are situated in proximity to specific or generic green and blue spaces (hence, constituting a green or blue space led approach), others are conducted due to availability of green and blue space data (hence, applying a green or blue space data led approach), while other studies are shaped by particular interests in the association of particular health conditions with presence of, or engagements with green or blue spaces (hence, adopting a health or health status led approach). In order to tackle this bias and develop a more objective research design for studying associations between human health outcomes and green and blue spaces, this paper discussed the features of a methodological framework suitable for that purpose after an initial, year-long, exploratory Irish study. The innovative approach explored by this study (i.e., the health-data led approach) first identifies sample sites with good and poor health outcomes from available health data (using data clustering techniques) before examining the potential role of the presence of, or engagement with green and blue spaces in creating such health outcomes. By doing so, we argue that some of the bias associated with the other three listed methods can be reduced and even eliminated. Finally, we infer that the principles and paradigm adopted by the health data led approach can be applicable and effective in analyzing other sustainability problems beyond associations between human health outcomes and green and blue spaces (e.g., health, energy, food, income, environment and climate inequality and justice etc.). The possibility of this is also discussed within this paper.
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Affiliation(s)
- Oludunsin Arodudu
- Department of Sustainable Resources Management, State University of New York, College of Environmental Science and Forestry, Syracuse, NY, USA; Department of Geography, Rhetoric House, National University of Ireland Maynooth, Co. Kildare, Ireland.
| | - Ronan Foley
- Department of Geography, Rhetoric House, National University of Ireland Maynooth, Co. Kildare, Ireland.
| | - Firouzeh Taghikhah
- Dicipline of Business Analytics, The University of Sydney, Sydney, Australia.
| | - Michael Brennan
- Eastern and Midland Regional Assembly, 3rd Floor North, Ballymun Civic Centre, Main Street, Ballymun, Dublin 9, Ireland.
| | - Gerald Mills
- School of Geography, Newman Building, Belfield, University College Dublin, Ireland
| | - Tine Ningal
- School of Geography, Newman Building, Belfield, University College Dublin, Ireland.
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15
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Rothe H, Lauer KB, Talbot-Cooper C, Sivizaca Conde DJ. Digital entrepreneurship from cellular data: How omics afford the emergence of a new wave of digital ventures in health. ELECTRONIC MARKETS 2023; 33:48. [PMID: 37724180 PMCID: PMC10505108 DOI: 10.1007/s12525-023-00669-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/25/2023] [Indexed: 09/20/2023]
Abstract
Data has become an indispensable input, throughput, and output for the healthcare industry. In recent years, omics technologies such as genomics and proteomics have generated vast amounts of new data at the cellular level including molecular, structural, and functional levels. Cellular data holds the potential to innovate therapeutics, vaccines, diagnostics, consumer products, or even ancestry services. However, data at the cellular level is generated with rapidly evolving omics technologies. These technologies use scientific knowledge from resource-rich environments. This raises the question of how new ventures can use cellular-level data from omics technologies to create new products and scale their business. We report on a series of interviews and a focus group discussion with entrepreneurs, investors, and data providers. By conceptualizing omics technologies as external enablers, we show how characteristics of cellular-level data negatively affect the combination mechanisms that drive venture creation and growth. We illustrate how data characteristics set boundary conditions for innovation and entrepreneurship and highlight how ventures seek to mitigate their impact. Supplementary Information The online version contains supplementary material available at 10.1007/s12525-023-00669-w.
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Affiliation(s)
- Hannes Rothe
- University of Duisburg Essen, Institute for Computer Science and Business Information Systems, Essen, Germany
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16
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Farasati Far B. Artificial intelligence ethics in precision oncology: balancing advancements in technology with patient privacy and autonomy. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:685-689. [PMID: 37720345 PMCID: PMC10501889 DOI: 10.37349/etat.2023.00160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 07/10/2023] [Indexed: 09/19/2023] Open
Abstract
Precision oncology is a rapidly evolving field that uses advanced technologies to deliver personalized cancer care based on a patient's unique genetic and clinical profile. The use of artificial intelligence (AI) in precision oncology has shown great potential to improve diagnosis, treatment planning, and treatment outcomes. However, the integration of AI in precision oncology also raises important ethical considerations related to patient privacy, autonomy, and protection from bias. In this opinion paper, an overview is provided of previous studies that have explored the use of AI in precision oncology and the ethical considerations associated with this technology. The conclusions of these studies are compared, and the importance of approaching the use of AI in precision oncology with caution is emphasized. It is stressed that patient privacy, autonomy, and protection from bias should be made central to the development and use of AI in precision oncology. Clear guidelines and regulations must be established to ensure that AI is used ethically and for the benefit of patients. The use of AI in precision oncology has the potential to revolutionize cancer care, but it should be ensured that it striked a balance between advancements in technology and ethical considerations. In conclusion, the use of AI in precision oncology is a promising development that has the potential to improve cancer outcomes. However, ethical considerations related to patient privacy, autonomy, and protection from bias must be central to the development and use of AI in precision oncology.
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Affiliation(s)
- Bahareh Farasati Far
- Department of Chemistry, Iran University of Science and Technology, Tehran 16846-13114, Iran
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17
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Tuladhar S, Mwamelo K, Manyama C, Obuobi D, Antunes M, Gashaw M, Vogel M, Shrinivasan H, Mugambwa KA, Korley I, Froeschl G, Hoffaeller L, Scholze S. Proceedings from the CIHLMU 2022 Symposium: "Availability of and Access to Quality Data in Health". BMC Proc 2023; 17:21. [PMID: 37587461 PMCID: PMC10433535 DOI: 10.1186/s12919-023-00270-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/20/2023] [Indexed: 08/18/2023] Open
Abstract
Data is an essential tool for valid and reliable healthcare management. Access to high-quality data is critical to ensuring the early identification of problems, the design of appropriate interventions, and the effective implementation and evaluation of health intervention outcomes. During the COVID-19 pandemic, the need for strong information systems and the value of producing high-quality data for timely response and tracking resources and progress have been very evident across countries. The availability of and access to high-quality data at all levels of the health systems of low and middle-income countries is a challenge, which is exacerbated by multiple parallels and poorly integrated data sources, a lack of data-sharing standards and policy frameworks, their weak enforcement, and inadequate skills among those handling data. Completeness, accuracy, integrity, validity, and timeliness are challenges to data availability and use. "Big Data" is a necessity and a challenge in the current complexities of health systems. In transitioning to digital systems with proper data standards and policy frameworks for privacy protection, data literacy, ownership, and data use at all levels of the health system, skill enhancement of the staff is critical. Adequate funding for strengthening routine information systems and periodic surveys and research, and reciprocal partnerships between high-income countries and low- and middle-income countries in data generation and use, should be prioritized by the low- and middle-income countries to foster evidence-based healthcare practices.
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Affiliation(s)
- Sabita Tuladhar
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany.
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany.
| | - Kimothy Mwamelo
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Christina Manyama
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Dorothy Obuobi
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Mario Antunes
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Mulatu Gashaw
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Monica Vogel
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Harinee Shrinivasan
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Kashung Annie Mugambwa
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Isabella Korley
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Guenter Froeschl
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Lisa Hoffaeller
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Sarah Scholze
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
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18
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Qayyum A, Razzak I, Tanveer M, Mazher M, Alhaqbani B. High-Density Electroencephalography and Speech Signal Based Deep Framework for Clinical Depression Diagnosis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2587-2597. [PMID: 37028339 DOI: 10.1109/tcbb.2023.3257175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Depression is a mental disorder characterized by persistent depressed mood or loss of interest in performing activities, causing significant impairment in daily routine. Possible causes include psychological, biological, and social sources of distress. Clinical depression is the more-severe form of depression, also known as major depression or major depressive disorder. Recently, electroencephalography and speech signals have been used for early diagnosis of depression; however, they focus on moderate or severe depression. We have combined audio spectrogram and multiple frequencies of EEG signals to improve diagnostic performance. To do so, we have fused different levels of speech and EEG features to generate descriptive features and applied vision transformers and various pre-trained networks on the speech and EEG spectrum. We have conducted extensive experiments on Multimodal Open Dataset for Mental-disorder Analysis (MODMA) dataset, which showed significant improvement in performance in depression diagnosis (0.972, 0.973 and 0.973 precision, recall and F1 score respectively) for patients at the mild stage. Besides, we provided a web-based framework using Flask and provided the source code publicly.1.
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19
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Siddiqui SA, Li C, Aidoo OF, Fernando I, Haddad MA, Pereira JA, Blinov A, Golik A, Câmara JS. Unravelling the potential of insects for medicinal purposes - A comprehensive review. Heliyon 2023; 9:e15938. [PMID: 37206028 PMCID: PMC10189416 DOI: 10.1016/j.heliyon.2023.e15938] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/20/2023] [Accepted: 04/27/2023] [Indexed: 05/21/2023] Open
Abstract
Entomotherapy, the use of insects for medicinal purposes, has been practised for centuries in many countries around the world. More than 2100 edible insect species are eaten by humans, but little is known about the possibility of using these insects as a promising alternative to traditional pharmaceuticals for treating diseases. This review offers a fundamental understanding of the therapeutic applications of insects and how they might be used in medicine. In this review, 235 insect species from 15 orders are reported to be used as medicine. Hymenoptera contains the largest medicinal insect species, followed by Coleoptera, Orthoptera, Lepidoptera, and Blattodea. Scientists have examined and validated the potential uses of insects along with their products and by-products in treating various diseases, and records show that they are primarily used to treat digestive and skin disorders. Insects are known to be rich sources of bioactive compounds, explaining their therapeutic features such as anti-inflammatory, antimicrobial, antiviral, and so on. Challenges associated with the consumption of insects (entomophagy) and their therapeutic uses include regulation barriers and consumer acceptance. Moreover, the overexploitation of medicinal insects in their natural habitat has led to a population crisis, thus necessitating the investigation and development of their mass-rearing procedure. Lastly, this review suggests potential directions for developing insects used in medicine and offers advice for scientists interested in entomotherapy. In future, entomotherapy may become a sustainable and cost-effective solution for treating various ailments and has the potential to revolutionize modern medicine.
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Affiliation(s)
- Shahida Anusha Siddiqui
- Technical University of Munich Campus Straubing for Biotechnology and Sustainability, Essigberg 3, 94315 Straubing, Germany
- German Institute of Food Technologies (DIL e.V.), Prof.-von-Klitzing Str. 7, 49610 D-Quakenbrück, Germany
- Corresponding author. Technical University of Munich Campus Straubing for Biotechnology and Sustainability, Essigberg 3, 94315 Straubing, Germany.
| | - Chujun Li
- Guangzhou Unique Biotechnology Co., Ltd, 510663, Guangzhou, China
- State Key Laboratory of Biocontrol, School of Life Science, Sun Yat-sen University, Guangzhou, 510006, China
| | - Owusu Fordjour Aidoo
- Department of Biological, Physical and Mathematical Sciences, University of Environment and Sustainable Development, 00233, Somanya, Ghana
| | - Ito Fernando
- Department of Plant Pest and Diseases, Faculty of Agriculture, Universitas Brawijaya, Malang, 65145, East Java, Indonesia
| | - Moawiya A. Haddad
- Department of Nutrition and Food Processing, Faculty of Agricultural Technology, Al-Balqa Applied University, 19117, Al-Salt, Jordan
| | - Jorge A.M. Pereira
- CQM – Centro de Química da Madeira, Universidade da Madeira, Campus da Penteada, 9020-105 Funchal, Portugal
| | - Andrey Blinov
- North Caucasus Federal University, Pushkina Street 1, 355009, Stavropol, Russia
| | - Andrey Golik
- North Caucasus Federal University, Pushkina Street 1, 355009, Stavropol, Russia
| | - José S. Câmara
- CQM – Centro de Química da Madeira, Universidade da Madeira, Campus da Penteada, 9020-105 Funchal, Portugal
- Departamento de Química, Faculdade de Ciências Exatas e Engenharia, Universidade da Madeira, Campus da Penteada, 9020-105 Funchal, Portugal
- Corresponding author. CQM – Centro de Química da Madeira, Universidade da Madeira, Campus da Penteada, 9020-105 Funchal, Portugal.
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20
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Meng L, Shang X, Gao F, Li D. Comparative study of imaging staging and postoperative pathological staging of esophageal cancer based on smart medical big data. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10514-10529. [PMID: 37322946 DOI: 10.3934/mbe.2023464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Esophageal cancer has become a malignant tumor disease with high mortality worldwide. Many cases of esophageal cancer are not very serious in the beginning but become severe in the late stage, so the best treatment time is missed. Less than 20% of patients with esophageal cancer are in the late stage of the disease for 5 years. The main treatment method is surgery, which is assisted by radiotherapy and chemotherapy. Radical resection is the most effective treatment method, but a method for imaging examination of esophageal cancer with good clinical effect has yet to be developed. This study compared imaging staging of esophageal cancer with pathological staging after operation based on the big data of intelligent medical treatment. MRI can be used to evaluate the depth of esophageal cancer invasion and replace CT and EUS for accurate diagnosis of esophageal cancer. Intelligent medical big data, medical document preprocessing, MRI imaging principal component analysis and comparison and esophageal cancer pathological staging experiments were used. Kappa consistency tests were conducted to compare the consistency between MRI staging and pathological staging and between two observers. Sensitivity, specificity and accuracy were determined to evaluate the diagnostic effectiveness of 3.0T MRI accurate staging. Results showed that 3.0T MR high-resolution imaging could show the histological stratification of the normal esophageal wall. The sensitivity, specificity and accuracy of high-resolution imaging in staging and diagnosis of isolated esophageal cancer specimens reached 80%. At present, preoperative imaging methods for esophageal cancer have obvious limitations, while CT and EUS have certain limitations. Therefore, non-invasive preoperative imaging examination of esophageal cancer should be further explored.Esophageal cancer has become a malignant tumor disease with high mortality worldwide. Many cases of esophageal cancer are not very serious in the beginning but become severe in the late stage, so the best treatment time is missed. Less than 20% of patients with esophageal cancer are in the late stage of the disease for 5 years. The main treatment method is surgery, which is assisted by radiotherapy and chemotherapy. Radical resection is the most effective treatment method, but a method for imaging examination of esophageal cancer with good clinical effect has yet to be developed. This study compared imaging staging of esophageal cancer with pathological staging after operation based on the big data of intelligent medical treatment. MRI can be used to evaluate the depth of esophageal cancer invasion and replace CT and EUS for accurate diagnosis of esophageal cancer. Intelligent medical big data, medical document preprocessing, MRI imaging principal component analysis and comparison and esophageal cancer pathological staging experiments were used. Kappa consistency tests were conducted to compare the consistency between MRI staging and pathological staging and between two observers. Sensitivity, specificity and accuracy were determined to evaluate the diagnostic effectiveness of 3.0T MRI accurate staging. Results showed that 3.0T MR high-resolution imaging could show the histological stratification of the normal esophageal wall. The sensitivity, specificity and accuracy of high-resolution imaging in staging and diagnosis of isolated esophageal cancer specimens reached 80%. At present, preoperative imaging methods for esophageal cancer have obvious limitations, while CT and EUS have certain limitations. Therefore, non-invasive preoperative imaging examination of esophageal cancer should be further explored.
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Affiliation(s)
- Linglei Meng
- Department CT/MR, People's Hospital of Xing Tai, Xing Tai 054000, Hebei, China
| | - XinFang Shang
- Department CT/MR, People's Hospital of Xing Tai, Xing Tai 054000, Hebei, China
| | - FengXiao Gao
- Department CT/MR, People's Hospital of Xing Tai, Xing Tai 054000, Hebei, China
| | - DeMao Li
- Department chest surgery, People's Hospital of Xing Tai, Xing Tai 054000, Hebei, China
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21
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Gupta G, Mahor R, Pawar N, Gumashta R, Gumashta J. Program management challenges of clinical studies: A qualitative critical assessment. J Family Med Prim Care 2023; 12:625-631. [PMID: 37312779 PMCID: PMC10259540 DOI: 10.4103/jfmpc.jfmpc_1151_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 09/30/2022] [Accepted: 10/06/2022] [Indexed: 06/15/2023] Open
Abstract
Background Organization, coordination, and discipline are required to complete any intricate operation, conducting a clinical experiment is no different. There are usually many moving components, from designing a plan, to conveying changes, to calculating risk, and excellent project management which are necessary to guarantee the study works successfully. Past evidence suggested that roadblock at any level hampers the progress of the clinical research. Understanding program management challenges hence becomes the key for timely and effective completion of clinical research. Methods A cross-sectional qualitative enquiry involving stakeholders in clinical research program management. We used problem tree-based approach wherein we documented views of various stakeholders to understand the interaction, interdependence, and related interventional needs of bottlenecks for long-term research gains using modern management methods applicable in clinical settings. The best fit approach was also explored to augment maximum benefit in limited resource settings. Results Non-alignment with state policy aims, a lack of effective coordination and communication among members, challenging logistic management, limited use of technology, a need for training, and an inefficient monitoring mechanism were among the major issues highlighted, and solutions were proposed. Conclusion Study concludes that an Integrated Process-cum-Timeline-Based Management strategy with multisectoral emphasis is ideal for program management of clinical projects.
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Affiliation(s)
- Girjesh Gupta
- Department of Community Medicine, PCMS and RC, Bhopal, Madhya Pradesh, India
| | - Rajendra Mahor
- Department of Community Medicine, PCMS and RC, Bhopal, Madhya Pradesh, India
| | - Neeraj Pawar
- Department of Community and Family Medicine, AIIMS Raebareli, Uttar Pradesh, India
| | - Raghvendra Gumashta
- Department of Community Medicine, PCMS and RC, Bhopal, Madhya Pradesh, India
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22
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Marimuthu P, Vaidehi V. An unsupervised approach for personalized RHM with reduced mean alert latency. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-220539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Remote Health Monitoring (RHM) is an important research topic among the researchers, where many challenges are to be addressed with respect to communication, device, synchronization, data analysis, knowledge inferencing, database maintenance, security, timely notification etc. Among these multi challenges, personalization of health data and scheduling of alert generation have been focused on this work. Recognizing the regular health pattern of each individual helps in diagnosing the disease accurately (reduces the False Alarm Ratio (FAR)) and provides the necessary treatment earlier. Similarly, in real time, with multiple patients, the latency should be minimal for timely alert generation. To address these two challenges, a Density-based K- means clustering (DbK-meansC) approach has been proposed in this work that personalize the vital health values. From the personalized health values the abnormalities in the health status of a person can be detected earlier. Here the health records are continuously updated with respect to health values that reflects in personalization of health records. If any abnormality noted in the health values, then the proposed work sends an alert message to the caretaker / the respective doctor using a dynamic preemptive priority scheduling scheme. The scheduling is done with respect to the severity levels of the vital health values of each individual respectively. The arrived results show that the proposed personalized abnormality detection RHM model generate alerts with minimum latency in terms of response and waiting time in a multi patient environment. With proper personalization, the obtained specificity and sensitivity are 91.56% and 92.87% respectively and the computational time is reduced as the degree of personalization increases.
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Affiliation(s)
- Poorani Marimuthu
- Department of Information Science & Engineering, CMR Institute of Technology, Bengaluru, Karnataka, India
| | - V. Vaidehi
- Vice Chancellor, Mother Teresa Women’s University, Kodaikanal, Tamilnadu, India
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23
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Kifle F, Kifleyohanes T, Moore J, Teshome A, Biccard BM. Indications, Challenges, and Characteristics of Successful Implementation of Perioperative Registries in Low Resource Settings: A Systematic Review. World J Surg 2023; 47:1387-1396. [PMID: 36656359 PMCID: PMC10156757 DOI: 10.1007/s00268-023-06909-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/25/2022] [Indexed: 01/20/2023]
Abstract
Perioperative registries can be utilised to track outcomes, develop risk prediction models, and make evidence-based decisions and interventions. To better understand and support initiatives to establish clinical registries, this study aimed to assess the indications, challenges, and characteristics of successful perioperative registries in low-resource settings, where there is unmet surgical demand and patients have a mortality rate up to double that of high-income countries. We conducted a librarian-assisted literature search of international research databases of articles published between January 1969 and January 2021. Studies were filtered using predefined criteria and responses to two Mixed Method Appraisal Tool screening questions. A Direct Content Analysis Method was used to synthesis. e data for eligible studies based on predefined criteria. The search identified 2793 abstracts. After removing duplicates and excluding studies that did not meet eligibility criteria, twelve studies were included, conducted in South America (n = 4), Africa (n = 5), the Middle East (n = 2), and Asia (n = 1). The lack of context-specific data for determining and evaluating patient outcomes (n = 7) was the major indication for implementation. Organising local research teams and engaging stakeholders in the host country were associated with successful implementation. Inadequate funding for data collectors and monitoring data quality were identified as challenges (n = 4). The goal of a perioperative registry is to generate data to influence and support quality improvement, and national surgical policies. Efforts to establish perioperative registries in low- and middle-income countries should engage local teams and stakeholders and seek to overcome challenges in data collection and monitoring.
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Affiliation(s)
- Fitsum Kifle
- Division of Global Surgery, University of Cape Town (UCT), Cape Town, South Africa. .,College of Medicine, Asrat Weldyes Health Science Campus, Debre Berhan University, Debre Berhan, Ethiopia. .,African Perioperative Research Group, Network for Perioperative and Critical Care (APORG-N4PCc), Addis Ababa, Ethiopia.
| | - Tewodros Kifleyohanes
- College of Medicine, Asrat Weldyes Health Science Campus, Debre Berhan University, Debre Berhan, Ethiopia.,African Perioperative Research Group, Network for Perioperative and Critical Care (APORG-N4PCc), Addis Ababa, Ethiopia
| | - Jolene Moore
- College of Medicine, Asrat Weldyes Health Science Campus, Debre Berhan University, Debre Berhan, Ethiopia.,School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.,African Perioperative Research Group, Network for Perioperative and Critical Care (APORG-N4PCc), Addis Ababa, Ethiopia
| | | | - Bruce M Biccard
- Division of Global Surgery, University of Cape Town (UCT), Cape Town, South Africa.,African Perioperative Research Group, Network for Perioperative and Critical Care (APORG-N4PCc), Addis Ababa, Ethiopia.,Department of Anaesthesia and Perioperative Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, Western Cape, South Africa
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24
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Shaikh AK, Alhashmi SM, Khalique N, Khedr AM, Raahemifar K, Bukhari S. Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector. Digit Health 2023; 9:20552076221149296. [PMID: 36683951 PMCID: PMC9850136 DOI: 10.1177/20552076221149296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 12/18/2022] [Indexed: 01/19/2023] Open
Abstract
Artificial Intelligent (AI) applications in e-health have evolved considerably in the last 25 years. To track the current research progress in this field, there is a need to analyze the most recent trend of adopting AI applications in e-health. This bibliometric analysis study covers AI applications in e-health. It differs from the existing literature review as the journal articles are obtained from the Scopus database from its beginning to late 2021 (25 years), which depicts the most recent trend of AI in e-health. The bibliometric analysis is employed to find the statistical and quantitative analysis of available literature of a specific field of study for a particular period. An extensive global literature review is performed to identify the significant research area, authors, or their relationship through published articles. It also provides the researchers with an overview of the work evolution of specific research fields. The study's main contribution highlights the essential authors, journals, institutes, keywords, and states in developing the AI field in e-health.
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Affiliation(s)
| | - Saadat M Alhashmi
- Department of Information Systems, College of Computing and
Informatics, University of
Sharjah, Sharjah, United Arab
Emirates,Saadat M Alhashmi, University of Sharjah,
College of Computing and Informatics, College of Computing and Informatics,
University of Sharjah, Sharjah 27272, United Arab Emirates.
| | - Nadia Khalique
- College of
Economics and Political Science, Sultan Qaboos
University, Muscat, Oman
| | - Ahmed M. Khedr
- Department of Information Systems, College of Computing and
Informatics, University of
Sharjah, Sharjah, United Arab
Emirates
| | | | - Sadaf Bukhari
- Beijing
Institute of Technology, Beijing, Beijing,
China
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25
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Gomes MAS, Kovaleski JL, Pagani RN, da Silva VL, Pasquini TCDS. Transforming healthcare with big data analytics: technologies, techniques and prospects. J Med Eng Technol 2023; 47:1-11. [PMID: 35852400 DOI: 10.1080/03091902.2022.2096133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In different studies in the field of healthcare, big data analytics technology has been shown to be effective in observing the behaviour of data, of which analysed to allow the discovery of relevant insights for strategy and decision making. The objective of this study is to present the results of a systematic review of the literature on big data analytics in healthcare, focussing in technologies, main areas and purposes of adoption. To reach its objective, the study conducts an exploratory research, through a systematic review of the literature, using the Methodi Ordinatio protocol supported by content analysis. The results reveal that the use of tools implies work performance at the clinical and managerial level, improving the cost-benefit ratio and reducing the time factor in the practice of the workforce in health services. Thus, this study hopes to contribute to the technological advancement of computational intelligence applied to healthcare.
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Affiliation(s)
- Myller Augusto Santos Gomes
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - João Luiz Kovaleski
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - Regina Negri Pagani
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
| | - Vander Luiz da Silva
- Production Engineering, Federal University of Technology of Paraná, State University of Ponta Grossa, Ponta Grossa, Brazil
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26
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Zhou Y, Varzaneh MG. Efficient and scalable patients clustering based on medical big data in cloud platform. JOURNAL OF CLOUD COMPUTING: ADVANCES, SYSTEMS AND APPLICATIONS 2022; 11:49. [PMID: 36188195 PMCID: PMC9510253 DOI: 10.1186/s13677-022-00324-3] [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: 07/26/2022] [Accepted: 09/09/2022] [Indexed: 11/21/2022] Open
Abstract
With the outbreak and popularity of COVID-19 pandemic worldwide, the volume of patients is increasing rapidly all over the world, which brings a big risk and challenge for the maintenance of public healthcare. In this situation, quick integration and analysis of the medical records of patients in a cloud platform are of positive and valuable significance for accurate recognition and scientific diagnosis of the healthy conditions of potential patients. However, due to the big volume of medical data of patients distributed in different platforms (e.g., multiple hospitals), how to integrate these data for patient clustering and analysis in a time-efficient and scalable manner in cloud platform is still a challenging task, while guaranteeing the capability of privacy-preservation. Motivated by this fact, a time-efficient, scalable and privacy-guaranteed patient clustering method in cloud platform is proposed in this work. At last, we demonstrate the competitive advantages of our method via a set of simulated experiments. Experiment results with competitive methods in current research literatures have proved the feasibility of our proposal.
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27
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Improving healthcare outcomes using multimedia big data analytics. Neural Comput Appl 2022; 34:15095-15097. [PMID: 35669536 PMCID: PMC9152831 DOI: 10.1007/s00521-022-07397-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 11/23/2022]
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28
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Santhana Marichamy V, Natarajan V. Efficient big data security analysis on HDFS based on combination of clustering and data perturbation algorithm using health care database. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this manuscript proposes an efficient big data security analysis on HDFS based on the combination of Improved Deep K-means Clustering (IDFKM) algorithm and Modified 3D rotation data perturbation algorithm using health care database. To compile a similar group of data, an Improved Deep K-means Clustering (IDFKM) Algorithm is used as partitioning the medical data. After clustering, Modified 3D rotation data perturbation technique is used to satisfy the privacy requirement of the client. Modified 3D rotation Data Perturbation technique perturbs each and every sensitive data of the cluster and all the key parameters values used for clustering have warehoused in the database file sector. The proposed approach is executed by Java program, its efficiency is assessed by Health care database. The metrics under the study of memory usage attains higher accuracy 34.765%, 23.44%, 52.74%, 18.74%, lower execution time 35.23%, 23.76%, 27.86%, 27.76%, higher Efficiency 26.85%, 38.97%, 28.97%, 35.65%. then the proposed method is compared with the existing methods such asSecurity Analysis of SDN Applications for Big Data with spoofing identity, Tampering with data, Repudiation threats, Information disclosure, Denial of service and Elevation of privileges (STRIDE), Big Data Analysis-based Secure Cluster Management for using Ant Colony Optimization (ACA) Optimized Control Plane in Software-Defined Networks, System Architecture for Secure Authentication and Data Sharing in Cloud Enabled Big Data Environment using LemperlZivMarkow Algorithm (LZMA) and Density-based Clustering of Applications with Noise (DBSCAN), Big Data Based Security Analytics using data based security analytics (BDSA) approach for Protecting Virtualized Infrastructures in Cloud Computing respectively.
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Affiliation(s)
- V. Santhana Marichamy
- Department of Computer Science and Engineering, SRM Valliammai Engineering College, SRM Nagar, Kattankulathur, Tamil Nadu, India
| | - V. Natarajan
- Department of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chennai, India
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29
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Hassan M, Awan FM, Naz A, deAndrés-Galiana EJ, Alvarez O, Cernea A, Fernández-Brillet L, Fernández-Martínez JL, Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. Int J Mol Sci 2022; 23:4645. [PMID: 35563034 PMCID: PMC9104788 DOI: 10.3390/ijms23094645] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.
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Affiliation(s)
- Mubashir Hassan
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
| | - Faryal Mehwish Awan
- Department of Medical Lab Technology, The University of Haripur, Haripur 22620, Pakistan;
| | - Anam Naz
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Oscar Alvarez
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | - Ana Cernea
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | | | - Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43205, USA
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30
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Nafisah SI, Muhammad G. Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence. Neural Comput Appl 2022; 36:1-21. [PMID: 35462630 PMCID: PMC9016694 DOI: 10.1007/s00521-022-07258-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 03/29/2022] [Indexed: 12/18/2022]
Abstract
In most regions of the world, tuberculosis (TB) is classified as a malignant infectious disease that can be fatal. Using advanced tools and technology, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative to the subjective assessment performed by healthcare professionals. Thus, in the study, we propose an automatic TB detection system using advanced deep learning (DL) models. A significant portion of a CXR image is dark, providing no information for diagnosis and potentially confusing DL models. Therefore, in the proposed system, we use sophisticated segmentation networks to extract the region of interest from multimedia CXRs. Then, segmented images are fed into the DL models. For the subjective assessment, we use explainable artificial intelligence to visualize TB-infected parts of the lung. We use different convolutional neural network (CNN) models in our experiments and compare their classification performance using three publicly available CXR datasets. EfficientNetB3, one of the CNN models, achieves the highest accuracy of 99.1%, with a receiver operating characteristic of 99.9%, and an average accuracy of 98.7%. Experiment results confirm that using segmented lung CXR images produces better performance than does using raw lung CXR images.
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Affiliation(s)
- Saad I. Nafisah
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
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31
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Razzak I, Naz S. Unit-Vise: Deep Shallow Unit-Vise Residual Neural Networks With Transition Layer For Expert Level Skin Cancer Classification. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1225-1234. [PMID: 33211666 DOI: 10.1109/tcbb.2020.3039358] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Many modern neural network architectures with over parameterized regime have been used for identification of skin cancer. Recent work showed that network, where the hidden units are polynomially smaller in size, showed better performance than overparameterized models. Hence, in this paper, we present multistage unit-vise deep dense residual network with transition and additional supervision blocks that enforces the shorter connections resulting in better feature representation. Unlike ResNet, We divided the network into several stages, and each stage consists of several dense connected residual units that support residual learning with dense connectivity and limited the skip connectivity. Thus, each stage can consider the features from its earlier layers locally as well as less complicated in comparison to its counter network. Evaluation results on ISIC-2018 challenge consisting of 10,015 training images show considerable improvement over other approaches achieving 98.05 percent accuracy and improving on the best results achieved in comparison to state of the art methods. The code of Unit-vise network is publicly available.1.
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32
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Rashid ASK. The extent of the teacher academic development from the accreditation evaluation system perspective using machine learning. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2021.1960635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Ameer Sardar Kwekha Rashid
- Business Information Technology, College of Administration and Economics, University of Sulaimani, Sulaimaniya, Iraq
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33
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John Cremin C, Dash S, Huang X. Big Data: Historic Advances and Emerging Trends in Biomedical Research. CURRENT RESEARCH IN BIOTECHNOLOGY 2022. [DOI: 10.1016/j.crbiot.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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34
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Sedik A, Hammad M, Abd El-Samie FE, Gupta BB, Abd El-Latif AA. Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Comput Appl 2022. [PMID: 33487885 DOI: 10.1016/j.compeleceng.2022.108011] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.
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Affiliation(s)
- Ahmed Sedik
- Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh, Egypt
| | - Mohamed Hammad
- Information Technology Department, Faculty of Computers and Information, Menoufia University, Shebeen El-Kom, Egypt
| | - Fathi E Abd El-Samie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf, 32952 Egypt
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 84428 Saudi Arabia
| | - Brij B Gupta
- National Institute of Technology, Kurukshetra, India
- Department of Computer Science and Information Engineering, Asia University, Taichung City, Taiwan
| | - Ahmed A Abd El-Latif
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebeen El-Kom, 32511 Egypt
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35
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Mahendran N, P M DRV. A deep learning framework with an embedded-based feature selection approach for the early detection of the Alzheimer's disease. Comput Biol Med 2021; 141:105056. [PMID: 34839903 DOI: 10.1016/j.compbiomed.2021.105056] [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] [Received: 09/14/2021] [Revised: 11/20/2021] [Accepted: 11/20/2021] [Indexed: 12/29/2022]
Abstract
Ageing is associated with various ailments including Alzheimer 's disease (AD), which is a progressive form of dementia. AD symptoms develop over a period of years and, unfortunately, there is no cure. Existing AD treatments can only slow down the progression of symptoms and thus it is critical to diagnose the disease at an early stage. To help improve the early diagnosis of AD, a deep learning-based classification model with an embedded feature selection approach was used to classify AD patients. An AD DNA methylation data set (64 records with 34 cases and 34 controls) from the GEO omnibus database was used for the analysis. Before selecting the relevant features, the data were preprocessed by performing quality control, normalization and downstream analysis. As the number of associated CpG sites was huge, four embedded-based feature selection models were compared and the best method was used for the proposed classification model. An Enhanced Deep Recurrent Neural Network (EDRNN) was implemented and compared to other existing classification models, including a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Deep Recurrent Neural Network (DRNN). The results showed a significant improvement in the classification accuracy of the proposed model as compared to the other methods.
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Affiliation(s)
- Nivedhitha Mahendran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
| | - Durai Raj Vincent P M
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
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36
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Qayyum A, Mazhar M, Razzak I, Bouadjenek MR. Multilevel depth-wise context attention network with atrous mechanism for segmentation of COVID19 affected regions. Neural Comput Appl 2021; 35:1-13. [PMID: 34720443 PMCID: PMC8546198 DOI: 10.1007/s00521-021-06636-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 10/13/2021] [Indexed: 12/02/2022]
Abstract
Severe acute respiratory syndrome coronavirus (SARS-CoV-2) also named COVID-19, aggressively spread all over the world in just a few months. Since then, it has multiple variants that are far more contagious than its parent. Rapid and accurate diagnosis of COVID-19 and its variants are crucial for its treatment, analysis of lungs damage and quarantine management. Deep learning-based solution for efficient and accurate diagnosis to COVID-19 and its variants using Chest X-rays, and computed tomography images could help to counter its outbreak. This work presents a novel depth-wise residual network with an atrous mechanism for accurate segmentation and lesion location of COVID-19 affected areas using volumetric CT images. The proposed framework consists of 3D depth-wise and 3D residual squeeze and excitation block in cascaded and parallel to capture uniformly multi-scale context (low-level detailed, mid-level comprehensive and high-level rich semantic features). The squeeze and excitation block adaptively recalibrates channel-wise feature responses by explicitly modeling inter-dependencies between various channels. We further have introduced an atrous mechanism with a different atrous rate as the bottom layer. Extensive experiments on benchmark CT datasets showed considerable gain (5%) for accurate segmentation and lesion location of COVID-19 affected areas.
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Affiliation(s)
- Abdul Qayyum
- Computer Science Department, University of Burgundy, Dijon, France
| | - Mona Mazhar
- Department of Computer Engineering and Mathematics, University Rovira i Virgili, Tarragona, Spain
| | - Imran Razzak
- School of Information Technology, Deakin University, Geelong, Australia
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37
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Chopra K. Maslow’s theory for preventive healthcare in India – a content analysis approach. INTERNATIONAL JOURNAL OF PHARMACEUTICAL AND HEALTHCARE MARKETING 2021. [DOI: 10.1108/ijphm-10-2020-0088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The purpose of the study was to apply Maslow’s hierarchy of needs to understand consumer motivation for preventive health care in India using content analysis.
Design/methodology/approach
Content analysis approach which is a qualitative-based approach was adopted. The responses were collected through semi-structured interviews using purposeful sampling method, and the responses were analyzed using content analysis approach. Sub themes and main themes were derived from the data which related to concepts in Maslow’s theory.
Findings
The results indicate the following: healthy food, healthy diet and health supplements are the basic need; sustainability of health and fitness and health security relate to safety and security need; feeling of pride in being fit, being a role model of fitness for others and influence of electronic media relate to social and self-esteem need; freedom from disease and peace of mind fulfill the need for self-actualization.
Research limitations/implications
To strengthen the external validity, a mix of alternate research methodologies adopting qualitative and quantitative approach need to be adopted.
Practical implications
This study will help to better understand motivation for preventive health care. It will enable health-care companies to design health-care marketing programs based on Maslow’s theory to motivate individuals to purchase health products. The public health-care departments can issue guidelines based on Maslow’s theory to motivate citizens toward preventive health care.
Originality/value
Maslow’s theory was applied in the context of preventive health care.
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38
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Nasser N, Emad-ul-Haq Q, Imran M, Ali A, Razzak I, Al-Helali A. A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing. Neural Comput Appl 2021; 35:13775-13789. [PMID: 34522068 PMCID: PMC8431959 DOI: 10.1007/s00521-021-06396-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 07/27/2021] [Indexed: 12/26/2022]
Abstract
Coronavirus (COVID-19) is a very contagious infection that has drawn the world's attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data's intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system's robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion.
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Affiliation(s)
- Nidal Nasser
- College of Engineering, Alfaisal University, Riyadh, Kingdom of Saudi Arabia
| | - Qazi Emad-ul-Haq
- College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Muhammad Imran
- College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia
- School of Engineering, Information Technology and Physical Sciences, Federation University, Brisbane, Australia
| | - Asmaa Ali
- School of Computing, Queen’s University, Kingston, Canada
| | - Imran Razzak
- School of Information Technology, Deakin University, Geelong, Australia
| | - Abdulaziz Al-Helali
- College of Engineering, Alfaisal University, Riyadh, Kingdom of Saudi Arabia
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39
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Kumar PM, Hong CS, Afghah F, Manogaran G, Yu K, Hua Q, Gao J. Clouds Proportionate Medical Data Stream Analytics for Internet of Things-based Healthcare Systems. IEEE J Biomed Health Inform 2021; 26:973-982. [PMID: 34415841 DOI: 10.1109/jbhi.2021.3106387] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.
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Qayyum A, Razzak I, Tanveer M, Kumar A. Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis. ANNALS OF OPERATIONS RESEARCH 2021:1-21. [PMID: 34248242 PMCID: PMC8254442 DOI: 10.1007/s10479-021-04154-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/08/2021] [Indexed: 05/09/2023]
Abstract
Coronavirus (COVID-19) and its new strain resulted in massive damage to society and brought panic worldwide. Automated medical image analysis such as X-rays, CT, and MRI offers excellent early diagnosis potential to augment the traditional healthcare strategy to fight against COVID-19. However, the identification of COVID infected lungs X-rays is challenging due to the high variation in infection characteristics and low-intensity contrast between normal tissues and infections. To identify the infected area, in this work, we present a novel depth-wise dense network that uniformly scales all dimensions and performs multilevel feature embedding, resulting in increased feature representations. The inclusion of depth wise component and squeeze-and-excitation results in better performance by capturing a more receptive field than the traditional convolutional layer; however, the parameters are almost the same. To improve the performance and training set, we have combined three large scale datasets. The extensive experiments on the benchmark X-rays datasets demonstrate the effectiveness of the proposed framework by achieving 96.17% in comparison to cutting-edge methods primarily based on transfer learning.
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Qiu Y, Lu W, Guo J, Sun C, Jia P. Urban and Rural Population and Development Research on Medical Coordination: In View of Dalian 2008-2017 Official Statistics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18126395. [PMID: 34199165 PMCID: PMC8296219 DOI: 10.3390/ijerph18126395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 11/16/2022]
Abstract
Providing universal quality health services is one of the Sustainable Development Goals (SDG) to achieve by 2030. We evaluated the sustainable and coordinated development of urban and rural medical care from 2008–2017 in Dalian, China, by developing an evaluation system based on population and health services. We used a comprehensive development index model and a coupling coordination model to evaluate the status and sustainable development of population and medical services in Dalian. The overall level of population development index in urban areas was significantly lower than in rural areas in the past decade. Comparing the data for 2008 and 2017, Zhongshan District (−31.51%), Ganjingzi District (−25.67%), Lyshunkou District (−35.45%), and Pulandian District (−19.59%) posted significant declines in the population development index. The overall medical service development index for both urban and rural areas registered a steady upward trend. In terms of the relationship between population and medical services, a more pronounced coupling running-in stage was observed among urban areas than among rural areas. Among urban areas, the coupling running-in stage in Zhongshan District (2013–2016) and Shahekou District (2011–2014) was most pronounced, while among rural areas, Jinzhou District (2012–2016, 0.684~0.756) had the most distinct coupling running-in stage. In terms of coordination development, we found that both urban and rural areas experienced a long period of moderate coordination stage. Among urban areas, except for some middle and mountainous districts with unstable changes in the coordination degree, the overall development trend in the region showed a stable transition from moderate coordination stage towards high coordination stage. From 2008 to 2017, only the coordination degree in Jinzhou District (−9.17%) showed negative growth. Although considerable efforts have been initiated to improve the coordinated development of Dalian’s urban and rural populations and its medical services, the medical and healthcare systems still face numerous challenges.
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Affiliation(s)
- Yukun Qiu
- School of Architecture and Arts, Dalian University of Technology, Dalian 116081, China;
| | - Wei Lu
- School of Architecture and Arts, Dalian University of Technology, Dalian 116081, China;
- Correspondence:
| | - Jianke Guo
- Research Center of Ocean Economy and Sustainable Development, Liaoning Normal University, Dalian 116029, China; (J.G.); (C.S.)
| | - Caizhi Sun
- Research Center of Ocean Economy and Sustainable Development, Liaoning Normal University, Dalian 116029, China; (J.G.); (C.S.)
| | - Peng Jia
- School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China;
- International Institute of Spatial Lifecourse Epidemiology (ISLE), Hong Kong, China
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Wang H, Pujos-Guillot E, Comte B, de Miranda JL, Spiwok V, Chorbev I, Castiglione F, Tieri P, Watterson S, McAllister R, de Melo Malaquias T, Zanin M, Rai TS, Zheng H. Deep learning in systems medicine. Brief Bioinform 2021; 22:1543-1559. [PMID: 33197934 PMCID: PMC8382976 DOI: 10.1093/bib/bbaa237] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 12/11/2022] Open
Abstract
Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.
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Affiliation(s)
| | - Estelle Pujos-Guillot
- metabolomic platform dedicated to metabolism studies in nutrition and health in the French National Research Institute for Agriculture, Food and Environment
| | - Blandine Comte
- French National Research Institute for Agriculture, Food and Environment
| | - Joao Luis de Miranda
- (ESTG/IPP) and a Researcher (CERENA/IST) in optimization methods and process systems engineering
| | - Vojtech Spiwok
- Molecular Modelling Researcher applying machine learning to accelerate molecular simulations
| | - Ivan Chorbev
- Faculty for Computer Science and Engineering, University Ss Cyril and Methodius in Skopje, North Macedonia working in the area of eHealth and assistive technologies
| | | | - Paolo Tieri
- National Research Council of Italy (CNR) and a lecturer at Sapienza University in Rome, working in the field of network medicine and computational biology
| | | | - Roisin McAllister
- Research Associate working in CTRIC, University of Ulster, Derry, and has worked in clinical and academic roles in the fields of molecular diagnostics and biomarker discovery
| | | | - Massimiliano Zanin
- Researcher working in the Institute for Cross-Disciplinary Physics and Complex Systems, Spain, with an interest on data analysis and integration using statistical physics techniques
| | - Taranjit Singh Rai
- Lecturer in cellular ageing at the Centre for Stratified Medicine. Dr Rai’s research interests are in cellular senescence, which is thought to promote cellular and tissue ageing in disease, and the development of senolytic compounds to restrict this process
| | - Huiru Zheng
- Professor of computer sciences at Ulster University
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Kwon S, Wan N, Burns RD, Brusseau TA, Kim Y, Kumar S, Ertin E, Wetter DW, Lam CY, Wen M, Byun W. The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings. SENSORS 2021; 21:s21041411. [PMID: 33670507 PMCID: PMC7922785 DOI: 10.3390/s21041411] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/05/2021] [Accepted: 02/10/2021] [Indexed: 12/12/2022]
Abstract
MotionSense HRV is a wrist-worn accelerometery-based sensor that is paired with a smartphone and is thus capable of measuring the intensity, duration, and frequency of physical activity (PA). However, little information is available on the validity of the MotionSense HRV. Therefore, the purpose of this study was to assess the concurrent validity of the MotionSense HRV in estimating sedentary behavior (SED) and PA. A total of 20 healthy adults (age: 32.5 ± 15.1 years) wore the MotionSense HRV and ActiGraph GT9X accelerometer (GT9X) on their non-dominant wrist for seven consecutive days during free-living conditions. Raw acceleration data from the devices were summarized into average time (min/day) spent in SED and moderate-to-vigorous PA (MVPA). Additionally, using the Cosemed K5 indirect calorimetry system (K5) as a criterion measure, the validity of the MotionSense HRV was examined in simulated free-living conditions. Pearson correlations, mean absolute percent errors (MAPE), Bland–Altman (BA) plots, and equivalence tests were used to examine the validity of the MotionSense HRV against criterion measures. The correlations between the MotionSense HRV and GT9X were high and the MAPE were low for both the SED (r = 0.99, MAPE = 2.4%) and MVPA (r = 0.97, MAPE = 9.1%) estimates under free-living conditions. BA plots illustrated that there was no systematic bias between the MotionSense HRV and criterion measures. The estimates of SED and MVPA from the MotionSense HRV were significantly equivalent to those from the GT9X; the equivalence zones were set at 16.5% for SED and 29% for MVPA. The estimates of SED and PA from the MotionSense HRV were less comparable when compared with those from the K5. The MotionSense HRV yielded comparable estimates for SED and PA when compared with the GT9X accelerometer under free-living conditions. We confirmed the promising application of the MotionSense HRV for monitoring PA patterns for practical and research purposes.
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Affiliation(s)
- Sunku Kwon
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
| | - Neng Wan
- Department of Geography, University of Utah, Salt Lake City, UT 84112, USA;
| | - Ryan D. Burns
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
| | - Timothy A. Brusseau
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
| | - Youngwon Kim
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong;
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0SL, UK
| | - Santosh Kumar
- Department of Computer Science, University of Memphis, Memphis, TN 38152, USA;
| | - Emre Ertin
- Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA;
| | - David W. Wetter
- Department of Population Health Sciences and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84132, USA; (D.W.W.); (C.Y.L.)
| | - Cho Y. Lam
- Department of Population Health Sciences and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84132, USA; (D.W.W.); (C.Y.L.)
| | - Ming Wen
- Department of Sociology, University of Utah, Salt Lake City, UT 84112, USA;
| | - Wonwoo Byun
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
- Correspondence: ; Tel.: +1-801-585-1119
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Lewis JR, Voortman T, Ioannidis JP. Evaluating and Strengthening the Evidence for Nutritional Bone Research: Ready to Break New Ground? J Bone Miner Res 2021; 36:219-226. [PMID: 33503301 DOI: 10.1002/jbmr.4236] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/01/2020] [Accepted: 12/18/2020] [Indexed: 01/19/2023]
Abstract
A healthy diet is essential to attain genetically determined peak bone mass and maintain optimal skeletal health across the adult lifespan. Despite the importance of nutrition for bone health, many of the nutritional requirements of the skeleton across the lifespan remain underexplored, poorly understood, or controversial. With increasingly aging populations, combined with rapidly changing diets and lifestyles globally, one anticipates large increases in the prevalence of osteoporosis and incidence of osteoporotic fractures. Robust, transparent, and reproducible nutrition research is a cornerstone for developing reliable public health recommendations to prevent osteoporosis and osteoporotic fractures. However, nutrition research is often criticized or ignored by healthcare professionals due to the overemphasis of weak science, conflicting, confusing or implausible findings, industry interests, common misconceptions, and strong opinions. Conversely, spurious research findings are often overemphasized or misconstrued by the media or prominent figures especially via social media, potentially leading to confusion and a lack of trust by the general public. Recently, reforms of the broader discipline of nutrition science have been suggested and promoted, leading to new tools and recommendations to attempt to address these issues. In this perspective, we provide a brief overview of what has been achieved in the field on nutrition and bone health, focusing on osteoporosis and osteoporotic fractures. We discuss what we view as some of the challenges, including inherent difficulties in assessing diet and its change, disentangling complex interactions between dietary components and between diet and other factors, selection of bone-related outcomes for nutrition studies, obtaining evidence with more unbiased designs, and perhaps most importantly, ensuring the trust of the public and healthcare professionals. This perspective also provides specific recommendations and highlights new developments and future opportunities for scientists studying nutrition and bone health. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Joshua R Lewis
- Institute for Nutrition Research, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia.,Medical School, The University of Western Australia, Perth, WA, Australia.,Centre for Kidney Research, Children's Hospital at Westmead, School of Public Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - John Pa Ioannidis
- Department of Medicine, Stanford Prevention Research Center, Stanford University, Stanford, CA, USA.,Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.,Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.,Department of Statistics, Stanford University, Stanford, CA, USA.,Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA
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45
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CircNet: an encoder–decoder-based convolution neural network (CNN) for circular RNA identification. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05673-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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46
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Sedik A, Hammad M, Abd El-Samie FE, Gupta BB, Abd El-Latif AA. Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Comput Appl 2021; 34:11423-11440. [PMID: 33487885 PMCID: PMC7814271 DOI: 10.1007/s00521-020-05410-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 09/29/2020] [Indexed: 02/04/2023]
Abstract
The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.
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Affiliation(s)
- Ahmed Sedik
- Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh, Egypt
| | - Mohamed Hammad
- Information Technology Department, Faculty of Computers and Information, Menoufia University, Shebeen El-Kom, Egypt
| | - Fathi E. Abd El-Samie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf, 32952 Egypt
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 84428 Saudi Arabia
| | - Brij B. Gupta
- National Institute of Technology, Kurukshetra, India
- Department of Computer Science and Information Engineering, Asia University, Taichung City, Taiwan
| | - Ahmed A. Abd El-Latif
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebeen El-Kom, 32511 Egypt
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48
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Yildirim O, Talo M, Ciaccio EJ, Tan RS, Acharya UR. Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105740. [PMID: 32932129 PMCID: PMC7477611 DOI: 10.1016/j.cmpb.2020.105740] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 08/31/2020] [Indexed: 05/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database. METHODS Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers. RESULTS We performed two different class scenarios, including reduced rhythms (seven rhythm types) and merged rhythms (four rhythm types) according to the records from the database. Our trained DNN model achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. CONCLUSION Recently, deep learning algorithms have been found to be useful because of their high performance. The main challenge is the scarcity of appropriate training and testing resources because model performance is dependent on the quality and quantity of case samples. In this study, we used a new public arrhythmia database comprising more than 10,000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records.
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Affiliation(s)
- Ozal Yildirim
- Department of Computer Engineering, Munzur University, Tunceli,62000, Turkey
| | - Muhammed Talo
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Edward J Ciaccio
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Ru San Tan
- National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; School of Management and Enterprise University of Southern Queensland, Springfield, Australia.
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Amin M, Shehwar D, Ullah A, Guarda T, Tanveer TA, Anwar S. A deep learning system for health care IoT and smartphone malware detection. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05429-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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50
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Beyond traditional approaches: a partial directed coherence with graph theory-based mental load assessment using EEG modality. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05408-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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