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Kuang Y, Xie M. DrugDoctor: enhancing drug recommendation in cold-start scenario via visit-level representation learning and training. Brief Bioinform 2024; 25:bbae464. [PMID: 39311701 PMCID: PMC11418268 DOI: 10.1093/bib/bbae464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 08/13/2024] [Accepted: 09/05/2024] [Indexed: 09/26/2024] Open
Abstract
Medication recommendation is a crucial application of artificial intelligence in healthcare. Current methodologies mostly depend on patient-level longitudinal representation, which utilizes the entirety of historical electronic health records for making predictions. However, they tend to overlook a few key elements: (1) The need to analyze the impact of past medications on previous conditions. (2) Similarity in patient visits is more common than similarity in the complete medical histories of patients. (3) It is difficult to accurately represent patient-level longitudinal data due to the varying numbers of visits. To our knowledge, current models face difficulties in dealing with initial patient visits (i.e. in cold-start scenarios) which are common in clinical practice. This paper introduces DrugDoctor, an innovative drug recommendation model crafted to emulate the decision-making mechanics of human doctors. Unlike previous methods, DrugDoctor explores the visit-level relationship between prescriptions and diseases while considering the impact of past prescriptions on the patient's condition to provide more accurate recommendations. We design a plug-and-play block to effectively capture drug substructure-aware disease information and effectiveness-aware medication information, employing cross-attention and multi-head self-attention mechanisms. Furthermore, DrugDoctor adopts a fundamentally new visit-level training strategy, aligning more closely with the practices of doctors. Extensive experiments conducted on the MIMIC-III and MIMIC-IV datasets demonstrate that DrugDoctor outperforms 10 other state-of-the-art methods in terms of Jaccard, F1-score, and PRAUC. Moreover, DrugDoctor exhibits strong robustness in handling patients with varying numbers of visits and effectively tackles "cold-start" issues in medication combination recommendations.
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Affiliation(s)
- Yabin Kuang
- College of Information Science and Engineering, Hunan Normal University, 36 Lushan Road, Yuelu District, Changsha 410081, China
| | - Minzhu Xie
- College of Information Science and Engineering, Hunan Normal University, 36 Lushan Road, Yuelu District, Changsha 410081, China
- Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education), Changsha 410081, China
- College of Mathematics and Statistics, Hunan Normal University, 36 Lushan Road, Yuelu District, Changsha 410081, China
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Khosravi M, Mojtabaeian SM, Zare Z. Factors influencing the use of big data within healthcare services: a systematic review. HEALTH INF MANAG J 2024:18333583241270484. [PMID: 39166442 DOI: 10.1177/18333583241270484] [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: 08/23/2024]
Abstract
Background: The emergence of big data holds the promise of aiding healthcare providers by identifying patterns and converting vast quantities of data into actionable insights facilitating the provision of precision medicine and decision-making. Objective: This study aimed to investigate the factors influencing use of big data within healthcare services to facilitate their use. Method: A systematic review was conducted in February 2024, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Database searches for articles published between 01 January 2020 and 18 February 2024 and included PubMed, Scopus, ProQuest and Cochrane Library. The Authority, Accuracy, Coverage, Objectivity, Date, Significance ( AACODS) checklist was used to evaluate the quality of the included articles. Subsequently, a thematic analysis was conducted on the findings of the review, using the Boyatzis approach. Results: A final selection of 46 studies were included in this systematic review. A significant proportion of these studies demonstrated acceptable quality, and the level of bias was deemed satisfactory. Thematic analysis identified seven major themes that influenced the use of big data in healthcare services. These themes were grouped into four primary categories: performance expectancy, effort expectancy, social influence, and facilitating conditions. Factors associated with "effort expectancy" were the most highly cited in the included studies (67%), while those related to "social influence" received the fewest citations (15%). Conclusion: This study underscored the critical role of "effort expectancy" factors, particularly those under the theme of "data complexity and management," in the process of using big data in healthcare services. Implications: Results of this study provide groundwork for future research to explore facilitators and barriers to using big data in health care, particularly in relation to data complexity and the efficient and effective management of big data, with significant implications for healthcare administrators and policymakers.
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Affiliation(s)
| | | | - Zahra Zare
- Shiraz University of Medical Sciences, Iran
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Willingham TB, Stowell J, Collier G, Backus D. Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:79. [PMID: 38248542 PMCID: PMC10815484 DOI: 10.3390/ijerph21010079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/23/2024]
Abstract
Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.
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Affiliation(s)
- T. Bradley Willingham
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
- Department of Physical Therapy, Georgia State University, Atlanta, GA 30302, USA
| | - Julie Stowell
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
- Department of Physical Therapy, Georgia State University, Atlanta, GA 30302, USA
| | - George Collier
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
| | - Deborah Backus
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
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Tseng YJ, Chen CJ, Chang CW. lab: an R package for generating analysis-ready data from laboratory records. PeerJ Comput Sci 2023; 9:e1528. [PMID: 37705643 PMCID: PMC10495959 DOI: 10.7717/peerj-cs.1528] [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: 05/04/2023] [Accepted: 07/20/2023] [Indexed: 09/15/2023]
Abstract
Background Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses. Methods To address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed lab package generates analysis-ready time series data by segmenting the data into time-series windows and imputing missing values. Moreover, users can map local laboratory codes to the Logical Observation Identifier Names and Codes (LOINC), an international standard. This mapping allows users to incorporate additional information, such as reference ranges and related diseases. Moreover, the reference ranges provided by LOINC enable us to categorize results into normal or abnormal. Finally, the analysis-ready time series data can be further summarized using descriptive statistics and utilized to develop models using machine learning technologies. Results Using the lab package, we analyzed data from MIMIC-III, focusing on newborns with patent ductus arteriosus (PDA). We extracted time-series laboratory records and compared the differences in test results between patients with and without 30-day in-hospital mortality. We then identified significant variations in several laboratory test results 7 days after PDA diagnosis. Leveraging the time series-analysis-ready data, we trained a prediction model with the long short-term memory algorithm, achieving an area under the receiver operating characteristic curve of 0.83 for predicting 30-day in-hospital mortality in model training. These findings demonstrate the lab package's effectiveness in analyzing disease progression. Conclusions The proposed lab package simplifies and expedites the workflow involved in laboratory records extraction. This tool is particularly valuable in assisting clinical data analysts in overcoming the obstacles associated with heterogeneous and sparse laboratory records.
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Affiliation(s)
- Yi-Ju Tseng
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, United States of America
| | - Chun Ju Chen
- Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Chia Wei Chang
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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Mahajan P, Macias C, Barda A, Fung CM. Federated data health networks hold potential for accelerating emergency research. J Am Coll Emerg Physicians Open 2023; 4:e12968. [PMID: 37220474 PMCID: PMC10200260 DOI: 10.1002/emp2.12968] [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: 02/08/2023] [Revised: 03/28/2023] [Accepted: 05/01/2023] [Indexed: 05/25/2023] Open
Abstract
Multi-center research networks often supported by centralized data centers are integral in generating high-quality evidence needed to address the gaps in emergency care. However, there are substantial costs to maintain high-functioning data centers. A novel distributed or federated data health networks (FDHN) approach has been used recently to overcome the shortcomings of centralized data approaches. A FDHN in emergency care is comprised of a series of decentralized, interconnected emergency departments (EDs) where each site's data is structured according to a common data model that allows data to be queried and/or analyzed without the data leaving the site's institutional firewall. To best leverage FDHNs for emergency care research networks, we propose a stepwise, 2-level development and deployment process-creating a lower resource requiring Level I FDHN capable of basic analyses, or a more resource-intense Level II FDHN capable of sophisticated analyses such as distributed machine learning. Importantly, existing electronic health records-based analytical tools can be leveraged without substantial cost implications for research networks to implement a Level 1 FDHN. Fewer regulatory barriers associated with FDHN have a potential for diverse, non-network EDs to contribute to research, foster faculty development, and improve patient outcomes in emergency care.
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Affiliation(s)
- Prashant Mahajan
- Departments of Emergency Medicine and PediatricsUniversity of MichiganAnn ArborMichiganUSA
| | - Charles Macias
- Department of Pediatric Emergency MedicineUniversity Hospitals Rainbow Babies and Children's HospitalClevelandOhioUSA
| | - Amie Barda
- Department of Emergency MedicineUniversity of MichiganAnn ArborMichiganUSA
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Wong C, Macias C, Miller M. Imperfection in adverse event detection: is this the opportunity to mature our focus on preventing harm in paediatrics? BMJ Qual Saf 2023:bmjqs-2022-015776. [PMID: 37142413 DOI: 10.1136/bmjqs-2022-015776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 05/06/2023]
Affiliation(s)
- Chris Wong
- UH Rainbow Babies & Children's Hospital, Cleveland, Ohio, USA
- Department of Pediatrics, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- University Hospitals Seidman Cancer Center, Cleveland, Ohio, USA
| | - Charles Macias
- UH Rainbow Babies & Children's Hospital, Cleveland, Ohio, USA
- Department of Pediatrics, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Marlene Miller
- UH Rainbow Babies & Children's Hospital, Cleveland, Ohio, USA
- Department of Pediatrics, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
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7
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Malhotra A, Molloy EJ, Bearer CF, Mulkey SB. Emerging role of artificial intelligence, big data analysis and precision medicine in pediatrics. Pediatr Res 2023; 93:281-283. [PMID: 36807652 DOI: 10.1038/s41390-022-02422-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 12/01/2022] [Indexed: 02/19/2023]
Affiliation(s)
- Atul Malhotra
- Department of Paediatrics, Monash University, Melbourne, VIC, Australia. .,Monash Newborn, Monash Children's Hospital, Melbourne, VIC, Australia.
| | - Eleanor J Molloy
- Paediatrics, Trinity College, Dublin, Ireland.,Children's Hospital Ireland at Tallaght, Dublin, Ireland.,Neonatology, Coombe Women's and Infants University Hospital, Dublin, Ireland
| | - Cynthia F Bearer
- Department of Pediatrics, Rainbow Babies & Children's Hospital, UH CMC, Cleveland, OH, USA
| | - Sarah B Mulkey
- Prenatal Pediatrics Institute, Children's National Hospital, Washington, DC, USA.,Department of Neurology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.,Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
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