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Hsu CC, Kao Y, Hsu CC, Chen CJ, Hsu SL, Liu TL, Lin HJ, Wang JJ, Liu CF, Huang CC. Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time. BMC Endocr Disord 2023; 23:234. [PMID: 37872536 PMCID: PMC10594858 DOI: 10.1186/s12902-023-01437-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 08/22/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Hyperglycemic crises are associated with high morbidity and mortality. Previous studies have proposed methods to predict adverse outcomes of patients in hyperglycemic crises; however, artificial intelligence (AI) has never been used to predict adverse outcomes. We implemented an AI model integrated with the hospital information system (HIS) to clarify whether AI could predict adverse outcomes. METHODS We included 2,666 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. The patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from the electronic medical records were collected. The performance of the multilayer perceptron (MLP), logistic regression, random forest, Light Gradient Boosting Machine (LightGBM), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms was compared. We selected the best algorithm to construct an AI model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. The outcomes between the non-AI and AI groups were compared after implementing the HIS and predicting the hyperglycemic crisis death (PHD) score. RESULTS The MLP had the best performance in predicting the three adverse outcomes, compared with the random forest, logistic regression, SVM, KNN, and LightGBM models. The areas under the curves (AUCs) using the MLP model were 0.852 for sepsis or septic shock, 0.743 for ICU admission, and 0.796 for all-cause mortality. Furthermore, we integrated the AI predictive model with the HIS to assist decision making in real time. No significant differences in ICU admission or all-cause mortality were detected between the non-AI and AI groups. The AI model performed better than the PHD score for predicting all-cause mortality (AUC 0.796 vs. 0.693). CONCLUSIONS A real-time AI predictive model is a promising method for predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies recruiting more patients are warranted.
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Affiliation(s)
- Chin-Chuan Hsu
- Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan
| | - Yuan Kao
- Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan
- Graduate Institute of Medical Sciences, College of Health Sciences, Chang Jung Christian University, Tainan, Taiwan
| | - Chien-Chin Hsu
- Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen university, Kaohsiung, Taiwan
| | - Chia-Jung Chen
- Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Shu-Lien Hsu
- Department of Nursing, Chi Mei Medical Center, Tainan, Taiwan
| | - Tzu-Lan Liu
- Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Hung-Jung Lin
- Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen university, Kaohsiung, Taiwan
- Department of Emergency Medicine, Taipei Medical University, Taipei, Taiwan
| | - Jhi-Joung Wang
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
- Department of Anesthesiology, National Defense Medical Center, Taipei, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan.
| | - Chien-Cheng Huang
- Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan.
- School of Medicine, College of Medicine, National Sun Yat-sen university, Kaohsiung, Taiwan.
- Department of Emergency Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
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Wu TC, Ho CTB. Blockchain Revolutionizing in Emergency Medicine: A Scoping Review of Patient Journey through the ED. Healthcare (Basel) 2023; 11:2497. [PMID: 37761695 PMCID: PMC10530815 DOI: 10.3390/healthcare11182497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/29/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Blockchain technology has revolutionized the healthcare sector, including emergency medicine, by integrating AI, machine learning, and big data, thereby transforming traditional healthcare practices. The increasing utilization and accumulation of personal health data also raises concerns about security and privacy, particularly within emergency medical settings. METHOD Our review focused on articles published in databases such as Web of Science, PubMed, and Medline, discussing the revolutionary impact of blockchain technology within the context of the patient journey through the ED. RESULTS A total of 33 publications met our inclusion criteria. The findings emphasize that blockchain technology primarily finds its applications in data sharing and documentation. The pre-hospital and post-discharge applications stand out as distinctive features compared to other disciplines. Among various platforms, Ethereum and Hyperledger Fabric emerge as the most frequently utilized options, while Proof of Work (PoW) and Proof of Authority (PoA) stand out as the most commonly employed consensus algorithms in this emergency care domain. The ED journey map and two scenarios are presented, exemplifying the most distinctive applications of emergency medicine, and illustrating the potential of blockchain. Challenges such as interoperability, scalability, security, access control, and cost could potentially arise in emergency medical contexts, depending on the specific scenarios. CONCLUSION Our study examines the ongoing research on blockchain technology, highlighting its current influence and potential future advancements in optimizing emergency medical services. This approach empowers frontline medical professionals to validate their practices and recognize the transformative potential of blockchain in emergency medical care, ultimately benefiting both patients and healthcare providers.
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Affiliation(s)
- Tzu-Chi Wu
- Institute of Technology Management, National Chung-Hsing University, Taichung 40227, Taiwan;
- Department of Emergency Medicine, Show Chwan Memorial Hospital, Changhua 500009, Taiwan
| | - Chien-Ta Bruce Ho
- Institute of Technology Management, National Chung-Hsing University, Taichung 40227, Taiwan;
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Stroud AM, Pacyna JE, Sharp RR. Ethical Aspects of Machine Listening in Healthcare. Am J Bioeth 2023; 23:1-3. [PMID: 37130383 DOI: 10.1080/15265161.2023.2199646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
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Nath S, Marie A, Ellershaw S, Korot E, Keane PA. New meaning for NLP: the trials and tribulations of natural language processing with GPT-3 in ophthalmology. Br J Ophthalmol 2022; 106:889-892. [PMID: 35523534 DOI: 10.1136/bjophthalmol-2022-321141] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 04/25/2022] [Indexed: 11/04/2022]
Abstract
Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently been discussed in the mainstream media and the literature with the advent of Generative Pre-trained Transformer 3 (GPT-3), a language model capable of producing human-like text. The release of GPT-3 has also sparked renewed interest on the applicability of NLP to contemporary healthcare problems. This article provides an overview of NLP models, with a focus on GPT-3, as well as discussion of applications specific to ophthalmology. We also outline the limitations of GPT-3 and the challenges with its integration into routine ophthalmic care.
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Affiliation(s)
- Siddharth Nath
- Ophthalmology and Visual Sciences, McGill University, Montreal, Quebec, Canada.,National Institute for Health Research, Biomedical Research Centre for Ophthalmology, UCL Institute of Ophthalmology, Moorfields Eye Hospital City Road Campus, London, UK
| | - Abdullah Marie
- School of Medicine and Dentistry, Queen's University Belfast, Belfast, UK
| | - Simon Ellershaw
- UKRI Centre for Doctoral Training in AI-enabled Healthcare, University College London, London, UK
| | - Edward Korot
- Byers Eye Institute, Stanford University, Stanford, California, USA
| | - Pearse A Keane
- National Institute for Health Research, Biomedical Research Centre for Ophthalmology, UCL Institute of Ophthalmology, Moorfields Eye Hospital City Road Campus, London, UK
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Lim JS, Hong M, Lam WST, Zhang Z, Teo ZL, Liu Y, Ng WY, Foo LL, Ting DSW. Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2022; 33:174-187. [PMID: 35266894 DOI: 10.1097/icu.0000000000000846] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. RECENT FINDINGS Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SUMMARY AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.
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Affiliation(s)
- Jane S Lim
- Singapore National Eye Centre, Singapore Eye Research Institute
| | | | - Walter S T Lam
- Yong Loo Lin School of Medicine, National University of Singapore
| | - Zheting Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University
| | - Zhen Ling Teo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Yong Liu
- National University of Singapore, DukeNUS Medical School, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
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Jenssen BP, Thayer J, Nekrasova E, Grundmeier RW, Fiks AG. Innovation in the pediatric electronic health record to realize a more effective platform. Curr Probl Pediatr Adolesc Health Care 2022; 52:101109. [PMID: 34895836 DOI: 10.1016/j.cppeds.2021.101109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Commercial electronic health records (EHRs) were first developed to automate business processes. As EHRs developed, design principles focused on transferring existing paper-based documentation to comparable electronic forms. In addition, a strong industry focus on adult healthcare settings and quality measures has limited attention and resources for high priority EHR functionality needed for the unique health care of children. The objective of this paper is to provide a review of innovation in the EHR, that includes a variety of established and emerging technologies that may help realize a more effective EHR in child health settings. A more effective EHR would serve as an electronic hub. Existing EHR infrastructure could provide the foundation upon which new technologies and approaches branch and extend, enabling more rapid and customizable innovation to better meet shifting stakeholder and end-user needs. Among many areas for improvement, key goals of innovation could include technology that relieves ambulatory primary care clinician documentation burden, identifies needs, and supports improved care coordination and outcomes, focused on the following key areas: identification of child and family care needs, decision support, documentation, care coordination, and family communication.
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Affiliation(s)
- Brian P Jenssen
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; The Possibilities Project, Center for Pediatric Clinical Effectiveness and PolicyLab, Children's Hospital of Philadelphia (CHOP), USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
| | - Jeritt Thayer
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Ekaterina Nekrasova
- The Possibilities Project, Center for Pediatric Clinical Effectiveness and PolicyLab, Children's Hospital of Philadelphia (CHOP), USA
| | - Robert W Grundmeier
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; The Possibilities Project, Center for Pediatric Clinical Effectiveness and PolicyLab, Children's Hospital of Philadelphia (CHOP), USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Alexander G Fiks
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; The Possibilities Project, Center for Pediatric Clinical Effectiveness and PolicyLab, Children's Hospital of Philadelphia (CHOP), USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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Yang LWY, Ng WY, Foo LL, Liu Y, Yan M, Lei X, Zhang X, Ting DSW. Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions. Curr Opin Ophthalmol 2021; 32:397-405. [PMID: 34324453 DOI: 10.1097/icu.0000000000000789] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type of AI that transforms human language, to one that computers can interpret and process. NLP is still in the formative stages of development in healthcare, with promising applications and potential challenges in its applications. This review provides an overview of AI-based NLP, its applications in healthcare and ophthalmology, next-generation use case, as well as potential challenges in deployment. RECENT FINDINGS The integration of AI-based NLP systems into existing clinical care shows considerable promise in disease screening, risk stratification, and treatment monitoring, amongst others. Stakeholder collaboration, greater public acceptance, and advancing technologies will continue to shape the NLP landscape in healthcare and ophthalmology. SUMMARY Healthcare has always endeavored to be patient centric and personalized. For AI-based NLP systems to become an eventual reality in larger-scale applications, it is pertinent for key stakeholders to collaborate and address potential challenges in application. Ultimately, these would enable more equitable and generalizable use of NLP systems for the betterment of healthcare and society.
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Affiliation(s)
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Yong Liu
- Institute of High Performance Computing, A STAR
| | - Ming Yan
- Institute of High Performance Computing, A STAR
| | | | | | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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Grant K, McParland A, Mehta S, Ackery AD. Artificial Intelligence in Emergency Medicine: Surmountable Barriers With Revolutionary Potential. Ann Emerg Med 2020; 75:721-726. [DOI: 10.1016/j.annemergmed.2019.12.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Indexed: 12/31/2022]
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