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Gunawardana J, Viswakula SD, Rannan-Eliya RP, Wijemunige N. Machine learning approaches for asthma disease prediction among adults in Sri Lanka. Health Informatics J 2024; 30:14604582241283968. [PMID: 39262121 DOI: 10.1177/14604582241283968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Objectives: Addressing the challenge of cost-effective asthma diagnosis amidst diverse symptom patterns among patients, this study aims to develop a machine learning-based asthma prediction tool for self-detection of asthma. Methods: Data from 6,665 participants in the Sri Lanka Health and Ageing Study (2018-2019) are used for this research. Thirteen machine learning algorithms, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbors, Gradient Boost, XGBoost, AdaBoost, CatBoost, LightGBM, Multi-Layer Perceptron, and Probabilistic Neural Network, are employed. Results: A hybrid version of Logistic Regression and LightGBM outperformed other models, achieving an AUC of 0.9062 and 79.85% sensitivity. Key predictive features for asthma include wheezing, breathlessness with wheezing, shortness of breath attacks, coughing attacks, chest tightness, nasal allergies, physical activity, passive smoking, ethnicity, and residential sector. Conclusion: Combining Logistic Regression and LightGBM models can effectively predict adult asthma based on self-reported symptoms and demographic and behavioural characteristics. The proposed expert system assists clinicians and patients in diagnosing potential asthma cases.
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Affiliation(s)
- Jrna Gunawardana
- Institute for Health Policy, Sri Lanka and Robert Gordon University, UK
| | - S D Viswakula
- Department of Statistics, University of Colombo, Sri Lanka
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Feng X, Xu K, Luo MJ, Chen H, Yang Y, He Q, Song C, Li R, Wu Y, Wang H, Tham YC, Ting DSW, Lin H, Wong TY, Lam DSC. Latest developments of generative artificial intelligence and applications in ophthalmology. Asia Pac J Ophthalmol (Phila) 2024; 13:100090. [PMID: 39128549 DOI: 10.1016/j.apjo.2024.100090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 07/30/2024] [Accepted: 08/07/2024] [Indexed: 08/13/2024] Open
Abstract
The emergence of generative artificial intelligence (AI) has revolutionized various fields. In ophthalmology, generative AI has the potential to enhance efficiency, accuracy, personalization and innovation in clinical practice and medical research, through processing data, streamlining medical documentation, facilitating patient-doctor communication, aiding in clinical decision-making, and simulating clinical trials. This review focuses on the development and integration of generative AI models into clinical workflows and scientific research of ophthalmology. It outlines the need for development of a standard framework for comprehensive assessments, robust evidence, and exploration of the potential of multimodal capabilities and intelligent agents. Additionally, the review addresses the risks in AI model development and application in clinical service and research of ophthalmology, including data privacy, data bias, adaptation friction, over interdependence, and job replacement, based on which we summarized a risk management framework to mitigate these concerns. This review highlights the transformative potential of generative AI in enhancing patient care, improving operational efficiency in the clinical service and research in ophthalmology. It also advocates for a balanced approach to its adoption.
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Affiliation(s)
- Xiaoru Feng
- School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing, China; Institute for Hospital Management, Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Kezheng Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Ming-Jie Luo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Haichao Chen
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Yangfan Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Qi He
- Research Centre of Big Data and Artificial Research for Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Chenxin Song
- Research Centre of Big Data and Artificial Research for Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ruiyao Li
- Research Centre of Big Data and Artificial Research for Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - You Wu
- Institute for Hospital Management, Tsinghua Medicine, Tsinghua University, Beijing, China; School of Basic Medical Sciences, Tsinghua Medicine, Tsinghua University, Beijing, China; Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
| | - Haibo Wang
- Research Centre of Big Data and Artificial Research for Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
| | - Yih Chung Tham
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore; Byers Eye Institute, Stanford University, Palo Alto, CA, USA
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China; Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, China
| | - Tien Yin Wong
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua Medicine, Tsinghua University, Beijing, China; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Dennis Shun-Chiu Lam
- The International Eye Research Institute, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; The C-MER International Eye Care Group, Hong Kong, Hong Kong, China
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Huang W, Ong WC, Wong MKF, Ng EYK, Koh T, Chandramouli C, Ng CT, Hummel Y, Huang F, Lam CSP, Tromp J. Applying the UTAUT2 framework to patients' attitudes toward healthcare task shifting with artificial intelligence. BMC Health Serv Res 2024; 24:455. [PMID: 38605373 PMCID: PMC11007870 DOI: 10.1186/s12913-024-10861-z] [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: 12/22/2023] [Accepted: 03/13/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Increasing patient loads, healthcare inflation and ageing population have put pressure on the healthcare system. Artificial intelligence and machine learning innovations can aid in task shifting to help healthcare systems remain efficient and cost effective. To gain an understanding of patients' acceptance toward such task shifting with the aid of AI, this study adapted the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), looking at performance and effort expectancy, facilitating conditions, social influence, hedonic motivation and behavioural intention. METHODS This was a cross-sectional study which took place between September 2021 to June 2022 at the National Heart Centre, Singapore. One hundred patients, aged ≥ 21 years with at least one heart failure symptom (pedal oedema, New York Heart Association II-III effort limitation, orthopnoea, breathlessness), who presented to the cardiac imaging laboratory for physician-ordered clinical echocardiogram, underwent both echocardiogram by skilled sonographers and the experience of echocardiogram by a novice guided by AI technologies. They were then given a survey which looked at the above-mentioned constructs using the UTAUT2 framework. RESULTS Significant, direct, and positive effects of all constructs on the behavioral intention of accepting the AI-novice combination were found. Facilitating conditions, hedonic motivation and performance expectancy were the top 3 constructs. The analysis of the moderating variables, age, gender and education levels, found no impact on behavioral intention. CONCLUSIONS These results are important for stakeholders and changemakers such as policymakers, governments, physicians, and insurance companies, as they design adoption strategies to ensure successful patient engagement by focusing on factors affecting the facilitating conditions, hedonic motivation and performance expectancy for AI technologies used in healthcare task shifting.
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Affiliation(s)
- Weiting Huang
- National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
| | - Wen Chong Ong
- National Healthcare Group Polyclinics, Singapore, Singapore
| | - Mark Kei Fong Wong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Eddie Yin Kwee Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Tracy Koh
- National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Chanchal Chandramouli
- National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Choon Ta Ng
- National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | | | | | - Carolyn Su Ping Lam
- National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- , Us2.ai, Singapore, Singapore
| | - Jasper Tromp
- Duke-NUS Medical School, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System Singapore, Singapore, Singapore
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Than J, Sim PY, Muttuvelu D, Ferraz D, Koh V, Kang S, Huemer J. Teleophthalmology and retina: a review of current tools, pathways and services. Int J Retina Vitreous 2023; 9:76. [PMID: 38053188 DOI: 10.1186/s40942-023-00502-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 10/02/2023] [Indexed: 12/07/2023] Open
Abstract
Telemedicine, the use of telecommunication and information technology to deliver healthcare remotely, has evolved beyond recognition since its inception in the 1970s. Advances in telecommunication infrastructure, the advent of the Internet, exponential growth in computing power and associated computer-aided diagnosis, and medical imaging developments have created an environment where telemedicine is more accessible and capable than ever before, particularly in the field of ophthalmology. Ever-increasing global demand for ophthalmic services due to population growth and ageing together with insufficient supply of ophthalmologists requires new models of healthcare provision integrating telemedicine to meet present day challenges, with the recent COVID-19 pandemic providing the catalyst for the widespread adoption and acceptance of teleophthalmology. In this review we discuss the history, present and future application of telemedicine within the field of ophthalmology, and specifically retinal disease. We consider the strengths and limitations of teleophthalmology, its role in screening, community and hospital management of retinal disease, patient and clinician attitudes, and barriers to its adoption.
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Affiliation(s)
- Jonathan Than
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK
| | - Peng Y Sim
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK
| | - Danson Muttuvelu
- Department of Ophthalmology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- MitØje ApS/Danske Speciallaeger Aps, Aarhus, Denmark
| | - Daniel Ferraz
- D'Or Institute for Research and Education (IDOR), São Paulo, Brazil
- Institute of Ophthalmology, University College London, London, UK
| | - Victor Koh
- Department of Ophthalmology, National University Hospital, Singapore, Singapore
| | - Swan Kang
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK
| | - Josef Huemer
- Moorfields Eye Hospital NHS Foundation Trust, 162 City Road, London, UK.
- Department of Ophthalmology and Optometry, Kepler University Hospital, Johannes Kepler University, Linz, Austria.
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Abisheganaden J, Lee KH, Low LL, Shum E, Goh HL, Ang CGL, Ta AWA, Miller SM. Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model. HEALTH CARE SCIENCE 2023; 2:153-163. [PMID: 38939111 PMCID: PMC11080848 DOI: 10.1002/hcs2.44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/18/2023] [Accepted: 04/07/2023] [Indexed: 06/29/2024]
Abstract
In a prior practice and policy article published in Healthcare Science, we introduced the deployed application of an artificial intelligence (AI) model to predict longer-term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home (H2H) program that has been operating since 2017. In this follow on practice and policy article, we further elaborate on Singapore's H2H program and care model, and its supporting AI model for multiple readmission prediction, in the following ways: (1) by providing updates on the AI and supporting information systems, (2) by reporting on customer engagement and related service delivery outcomes including staff-related time savings and patient benefits in terms of bed days saved, (3) by sharing lessons learned with respect to (i) analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants, (ii) balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables, and (iii) the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems, (4) by highlighting how this H2H effort supported broader Covid-19 response efforts across Singapore's public healthcare system, and finally (5) by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards. For the convenience of the reader, some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.
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Affiliation(s)
- John Abisheganaden
- Department of Respiratory and Critical Care Medicine, Tan Tock Seng HospitalNational Healthcare GroupSingaporeSingapore
- National Working Group for the Hospital to Home ProgramSingaporeSingapore
| | - Kheng Hock Lee
- Department of Family Medicine and Continuing Care, Singapore General Hospital, SingHealth GroupSingaporeSingapore
- SingHealth Community Hospitals, SingHealth GroupSingaporeSingapore
| | - Lian Leng Low
- Department of Family Medicine and Continuing Care, Singapore General Hospital, SingHealth GroupSingaporeSingapore
- Population Health and Integrated Care Office, SingHealth GroupSingaporeSingapore
| | - Eugene Shum
- Office of Community DevelopmentChangi General Hospital, SingHealth GroupSingaporeSingapore
| | - Han Leong Goh
- Data Analytics and AI Department, Integrated Health Information SystemsSingaporeSingapore
| | - Christine Gia Lee Ang
- Data Analytics and AI Department, Integrated Health Information SystemsSingaporeSingapore
| | - Andy Wee An Ta
- Data Analytics and AI Department, Integrated Health Information SystemsSingaporeSingapore
| | - Steven M. Miller
- School of Computing and Information SystemsSingapore Management UniversitySingaporeSingapore
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Zhu A, Tailor P, Verma R, Zhang I, Schott B, Ye C, Szirth B, Habiel M, Khouri AS. Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19. J Telemed Telecare 2023:1357633X231158832. [PMID: 36908254 PMCID: PMC10014445 DOI: 10.1177/1357633x231158832] [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: 09/24/2022] [Accepted: 02/05/2023] [Indexed: 03/14/2023]
Abstract
INTRODUCTION Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of vision-threatening diseases. Our study prospectively investigated deep learning artificial intelligence applications in student-run non-mydriatic screenings for an underserved, primarily Hispanic community during COVID-19. METHODS Five supervised student-run community screenings were held in West New York, New Jersey. Participants underwent non-mydriatic 45-degree retinal imaging by medical students. Images were uploaded to a cloud-based deep learning artificial intelligence for vision-threatening disease referral. An on-site tele-ophthalmology grader and remote clinical ophthalmologist graded images, with adjudication by a senior ophthalmologist to establish the gold standard diagnosis, which was used to assess the performance of deep learning artificial intelligence. RESULTS A total of 385 eyes from 195 screening participants were included (mean age 52.43 ± 14.5 years, 40.0% female). A total of 48 participants were referred for at least one vision-threatening disease. Deep learning artificial intelligence marked 150/385 (38.9%) eyes as ungradable, compared to 10/385 (2.6%) ungradable as per the human gold standard (p < 0.001). Deep learning artificial intelligence had 63.2% sensitivity, 94.5% specificity, 32.0% positive predictive value, and 98.4% negative predictive value in vision-threatening disease referrals. Deep learning artificial intelligence successfully referred all 4 eyes with multiple vision-threatening diseases. Deep learning artificial intelligence graded images (35.6 ± 13.3 s) faster than the tele-ophthalmology grader (129 ± 41.0) and clinical ophthalmologist (68 ± 21.9, p < 0.001). DISCUSSION Deep learning artificial intelligence can increase the efficiency and accessibility of vision-threatening disease screenings, particularly in underserved communities. Deep learning artificial intelligence should be adaptable to different environments. Consideration should be given to how deep learning artificial intelligence can best be utilized in a real-world application, whether in computer-aided or autonomous diagnosis.
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Affiliation(s)
- Aretha Zhu
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Priya Tailor
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Rashika Verma
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Isis Zhang
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Brian Schott
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Catherine Ye
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Bernard Szirth
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Miriam Habiel
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Albert S Khouri
- Institute of Ophthalmology & Visual Science, Rutgers New Jersey Medical School, Newark, NJ, USA
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