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Civit-Masot J, Luna-Perejon F, Muñoz-Saavedra L, Domínguez-Morales M, Civit A. A lightweight xAI approach to cervical cancer classification. Med Biol Eng Comput 2024:10.1007/s11517-024-03063-6. [PMID: 38507122 DOI: 10.1007/s11517-024-03063-6] [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: 05/16/2023] [Accepted: 02/24/2024] [Indexed: 03/22/2024]
Abstract
Cervical cancer is caused in the vast majority of cases by the human papilloma virus (HPV) through sexual contact and requires a specific molecular-based analysis to be detected. As an HPV vaccine is available, the incidence of cervical cancer is up to ten times higher in areas without adequate healthcare resources. In recent years, liquid cytology has been used to overcome these shortcomings and perform mass screening. In addition, classifiers based on convolutional neural networks can be developed to help pathologists diagnose the disease. However, these systems always require the final verification of a pathologist to make a final diagnosis. For this reason, explainable AI techniques are required to highlight the most significant data to the healthcare professional, as it can be used to determine the confidence in the results and the areas of the image used for classification (allowing the professional to point out the areas he/she thinks are most important and cross-check them against those detected by the system in order to create incremental learning systems). In this work, a 4-phase optimization process is used to obtain a custom deep-learning classifier for distinguishing between 4 severity classes of cervical cancer with liquid-cytology images. The final classifier obtains an accuracy over 97% for 4 classes and 100% for 2 classes with execution times under 1 s (including the final report generation). Compared to previous works, the proposed classifier obtains better accuracy results with a lower computational cost.
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Affiliation(s)
- Javier Civit-Masot
- Robotics and Computer Technology Lab, ETSII, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain.
| | - Francisco Luna-Perejon
- Robotics and Computer Technology Lab, ETSII, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
| | - Luis Muñoz-Saavedra
- Robotics and Computer Technology Lab, ETSII, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
| | - Manuel Domínguez-Morales
- Robotics and Computer Technology Lab, ETSII, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
- Computer Engineering Research Institute, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
| | - Anton Civit
- Robotics and Computer Technology Lab, ETSII, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
- Computer Engineering Research Institute, Universidad de Sevilla, Reina Mercedes s/n, Seville, 41018, Spain
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Zhou B, Deng Q, Zhou S, Zhuo D. Health care in future community: innovatively discover and respond to the needs of today's seniors. Front Public Health 2023; 11:1302493. [PMID: 38152669 PMCID: PMC10751950 DOI: 10.3389/fpubh.2023.1302493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 11/27/2023] [Indexed: 12/29/2023] Open
Abstract
Introduction In the context of the digital economy, the emergence and application of emerging technologies have accelerated the integration of traditional social structures with new technologies, leading to the inception of the "Future Community" as an innovative urban unit. With an aging population's rapid and sustained rise, integrating health care for older adults with modern information technology is gradually moving towards holistic governance. This approach utilizes the Future Community as a medium and aims for quality enhancement and increased efficiency, which instrumentally addresses the diversified health care needs of China's aging era. Methods In this study, we employed a questionnaire survey method that covered 11 communities in Tianjin City to understand better the current status and characteristics of their health care services. Results The survey results show that the means of community health care for older adults are gradually being upgraded, and the demands are shifting. Then, we arrive at three conclusions: firstly, technological innovation and smart approaches have the potential to positively influence the quality of health care in these communities. Secondly, allocating health care resources within communities can have a salutary effect on the psychological well-being of seniors. Thirdly, actively involving seniors in community life and governance can elevate their self-worth. Discussion At last, in conjunction with current challenges, we think that deepening multi-party collaboration, educating specialized talents, and bridging the "digital gap" would be effective ways to establish a future community for seniors.
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Affiliation(s)
- Bowen Zhou
- School of International Economics and Trade, Jilin University of Finance and Economics, Changchun, China
| | - Qidan Deng
- School of International Economics and Trade, Jilin University of Finance and Economics, Changchun, China
| | - Shiyuan Zhou
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Dongni Zhuo
- College of Education, University of Washington, Seattle, WA, United States
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Yu S, Chai Y, Samtani S, Liu H, Chen H. Motion Sensor–Based Fall Prevention for Senior Care: A Hidden Markov Model with Generative Adversarial Network Approach. INFORMATION SYSTEMS RESEARCH 2023. [DOI: 10.1287/isre.2023.1203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
Abstract
Whereas modern medicine has enabled humans to live longer and more robust lives, recent years have seen a significant increase in chronic care costs. The prevention of threats to mobility is critical for chronic disease management. Researchers and physicians often analyze data from wearable motion sensor–based information systems (IS) to prevent falls. However, prior studies on fall prevention often achieve suboptimal performance because of their limited capacities in modeling data distributions. In this study, we adopt the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states and a logistic regression that utilizes the snippet states and determines whether and when to trigger protective devices to prevent fall injuries. We evaluate the proposed framework against prevailing fall-prevention models and the HMM-GAN component against state-of-the-art sensor analytics models on large-scale data sets. Through an in-depth case study, we demonstrate how the proposed framework can lead to significantly reduced potentially catastrophic falls. Besides practical health information technology contributions, HMM-GAN offers methodological contributions to the IS knowledge base for scholars designing novel IT artifacts for healthcare applications.
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Affiliation(s)
- Shuo Yu
- Area of Information Systems and Quantitative Sciences, Rawls College of Business, Texas Tech University, Lubbock, Texas 79409
| | - Yidong Chai
- Department of Electronic Commerce, School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
- Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management, Hefei, Anhui 230009, China
- Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance, Ministry of Education, Hefei, Anhui 230009, China
| | - Sagar Samtani
- Department of Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405
| | - Hongyan Liu
- Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Beijing 100084, China
| | - Hsinchun Chen
- Department of Management Information Systems, Eller College of Management, University of Arizona, Tucson, Arizona 85721
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Samtani S, Zhu H, Padmanabhan B, Chai Y, Chen H, Nunamaker JF. Deep Learning for Information Systems Research. J MANAGE INFORM SYST 2023. [DOI: 10.1080/07421222.2023.2172772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Affiliation(s)
- Sagar Samtani
- Kelley School of Business, Indiana University, Bloomington, IN, USA
| | - Hongyi Zhu
- University of Texas at San Antonio, Alvarez College of Business, San Antonio, TX, USA
| | | | - Yidong Chai
- Hefei University of Technology, School of Management, Heifei, Anhui, China
| | - Hsinchun Chen
- Eller College of Management, University of Arizona, Tucson, AZ, USA
| | - Jay F. Nunamaker
- Center for the Management of Information, Eller College of Management, University of Arizona, Tucson, AZ, USA
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Nasir M, Dag A, Simsek S, Ivanov A, Oztekin A. Improving Imbalanced Machine Learning with Neighborhood-Informed Synthetic Sample Placement. J MANAGE INFORM SYST 2022. [DOI: 10.1080/07421222.2022.2127453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Murtaza Nasir
- Department of Finance, Real Estate, and Decision Sciences, W. Frank Barton School of Business, Wichita State University, Wichita, KS, USA
| | - Ali Dag
- Department of Business Intelligence & Analytics, Heider College of Business, Creighton University, Omaha, NE, USA
| | - Serhat Simsek
- Department of Information Management & Business Analytics, Feliciano School of Business, Montclair State University, Montclair, NJ, USA
| | - Anton Ivanov
- Department of Business Administration, Gies College of Business, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Asil Oztekin
- Department of Operations & Information Systems, Manning School of Business, University of Massachusetts Lowell, Lowell, MA, USA
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Wang T, Du Y, Gong Y, Choo KKR, Guo Y. Applications of Federated Learning in Mobile Health: Scoping Review (Preprint). J Med Internet Res 2022; 25:e43006. [PMID: 37126398 DOI: 10.2196/43006] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 01/24/2023] [Accepted: 03/10/2023] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND The proliferation of mobile health (mHealth) applications is partly driven by the advancements in sensing and communication technologies, as well as the integration of artificial intelligence techniques. Data collected from mHealth applications, for example, on sensor devices carried by patients, can be mined and analyzed using artificial intelligence-based solutions to facilitate remote and (near) real-time decision-making in health care settings. However, such data often sit in data silos, and patients are often concerned about the privacy implications of sharing their raw data. Federated learning (FL) is a potential solution, as it allows multiple data owners to collaboratively train a machine learning model without requiring access to each other's raw data. OBJECTIVE The goal of this scoping review is to gain an understanding of FL and its potential in dealing with sensitive and heterogeneous data in mHealth applications. Through this review, various stakeholders, such as health care providers, practitioners, and policy makers, can gain insight into the limitations and challenges associated with using FL in mHealth and make informed decisions when considering implementing FL-based solutions. METHODS We conducted a scoping review following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched 7 commonly used databases. The included studies were analyzed and summarized to identify the possible real-world applications and associated challenges of using FL in mHealth settings. RESULTS A total of 1095 articles were retrieved during the database search, and 26 articles that met the inclusion criteria were included in the review. The analysis of these articles revealed 2 main application areas for FL in mHealth, that is, remote monitoring and diagnostic and treatment support. More specifically, FL was found to be commonly used for monitoring self-care ability, health status, and disease progression, as well as in diagnosis and treatment support of diseases. The review also identified several challenges (eg, expensive communication, statistical heterogeneity, and system heterogeneity) and potential solutions (eg, compression schemes, model personalization, and active sampling). CONCLUSIONS This scoping review has highlighted the potential of FL as a privacy-preserving approach in mHealth applications and identified the technical limitations associated with its use. The challenges and opportunities outlined in this review can inform the research agenda for future studies in this field, to overcome these limitations and further advance the use of FL in mHealth.
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Affiliation(s)
- Tongnian Wang
- Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Yan Du
- School of Nursing, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Yanmin Gong
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Kim-Kwang Raymond Choo
- Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Yuanxiong Guo
- Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, United States
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Ketter W, Schroer K, Valogianni K. Information Systems Research for Smart Sustainable Mobility: A Framework and Call for Action. INFORMATION SYSTEMS RESEARCH 2022. [DOI: 10.1287/isre.2022.1167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Transportation is a backbone of modern globalized societies. It also causes approximately one third of all European Union and U.S. greenhouse gas emissions, represents a major health hazard for global populations, and poses significant economic costs. However, rapid innovation in vehicle technology, mobile connectivity, computing hardware, and artificial intelligence (AI)-powered information systems heralds a deep socio-technical transformation of the sector. The emergence of connected, autonomous, shared, and electric (CASE) vehicle technology has created a digital layer that resides on top of the traditional physical mobility system. This article contributes a framework to direct research and practice toward leveraging the opportunities afforded by CASE for a more efficient and less environmentally problematic mobility system. The authors propose seven overarching dimensions of action. These range from designing real-time digital coordination mechanisms for the management of mobility systems to developing AI-powered real-time decision support for mobility resource planning and operations. Per each dimension, concrete angles of attack are suggested which, we hope, will spur structured engagement from both researchers and practitioners in the field.
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Affiliation(s)
- Wolfgang Ketter
- Faculty of Management, Economics, and Social Sciences, Cologne Institute of Information Systems, University of Cologne, 50923 Cologne, Germany
- Rotterdam School of Management, Erasmus University, 3062 PA Rotterdam, Netherlands
| | - Karsten Schroer
- Faculty of Management, Economics, and Social Sciences, Cologne Institute of Information Systems, University of Cologne, 50923 Cologne, Germany
| | - Konstantina Valogianni
- IE Business School Information Systems & Technology, IE University, 40003 Segovia, Spain
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Bhatt P, Liu J, Gong Y, Wang J, Guo Y. Emerging Artificial Intelligence–Empowered mHealth: Scoping Review. JMIR Mhealth Uhealth 2022; 10:e35053. [PMID: 35679107 PMCID: PMC9227797 DOI: 10.2196/35053] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/23/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background
Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions.
Objective
Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years.
Methods
Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as “mobile healthcare,” “wearable medical sensors,” “smartphones”, and “AI.” We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain.
Results
We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research.
Conclusions
The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.
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Affiliation(s)
- Paras Bhatt
- Department of Electrical & Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Jia Liu
- The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Yanmin Gong
- Department of Electrical & Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Jing Wang
- Florida State University, Tallahassee, FL, United States
| | - Yuanxiong Guo
- Department of Electrical & Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, United States
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