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Artificial Intelligence vs. Natural Stupidity: Evaluating AI readiness for the Vietnamese Medical Information System

Quan-Hoang Vuong, Manh-Tung Ho, Thu-Trang Vuong, Viet-Phuong La, Manh-Toan Ho, Kien-Cuong P. Nghiem, Bach Xuan Tran, Hai-Ha Giang, Thu-Vu Giang, Carl Latkin, Hong-Kong T. Nguyen, Cyrus S.H. Ho (+1 others)
2019 Journal of Clinical Medicine  
However, a deeper look at the funding sources suggests a lack of socio-political commitment, hence the financial sustainability, to advance the field.  ...  The AI readiness in Vietnam's healthcare also suffers from the unprepared information infrastructure—using text mining for the official annual reports from 2012 to 2016 of the Ministry of Health, the paper  ...  The authors thank Nguyen Pham Muoi of Vietnam Panorama Media Monitoring for his continual support. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jcm8020168 pmid:30717268 pmcid:PMC6406313 fatcat:ak4t22k3lbbf5el637awkflwr4

Biased intelligence: on the subjectivity of digital objectivity

Jeremy T Moreau, Sylvain Baillet, Roy Wr Dudley
2020 BMJ Health & Care Informatics  
The number of publications on applications of AI and machine learning to medical diagnosis has dramatically increased since around 2015 (figure 1).  ...  Figure 1 1 Publications on artificial intelligence (AI)/machine learning applied to medical diagnosis and number of private AI or healthcare startup companies valued at >US$1 billion.  ... 
doi:10.1136/bmjhci-2020-100146 pmid:32830107 pmcid:PMC7445351 fatcat:iqe5osjurbctnlopgu575obbxa

The Importance of Computer Science for Public Health Training: An Opportunity and Call to Action

Sarah Kunkle, Gillian Christie, Derek Yach, Abdulrahman M El-Sayed
2016 JMIR Public Health and Surveillance  
Emerging computer science techniques, such as machine learning, present an opportunity to extract insights from these data that could help identify high-risk individuals and tailor health interventions  ...  Since then, the system has evolved to address emerging health needs and integrate new technologies. Today, personalized health technologies generate large amounts of data.  ...  In 1988, a US Institute of Medicine (now the National Academy of Medicine) report on the future of public health called for a greater emphasis on public health practice and relationships with academic  ... 
doi:10.2196/publichealth.5018 pmid:27227145 pmcid:PMC4869246 fatcat:fdffytmp7zgntm24kflaeg4siy

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

Yi Guo, Yahan Zhang, Tianchen Lyu, Mattia Prosperi, Fei Wang, Hua Xu, Jiang Bian
2021 JAMIA Journal of the American Medical Informatics Association  
Materials and Methods We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021.  ...  prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19  ...  FUNDING Drs Guo and Bian were funded in part by the National Institutes of Health (NIH) (Award number: R01 CA246418, R21 CA245858, R21 AG068717, R21 CA253394) and Centers for Disease Control and Prevention  ... 
doi:10.1093/jamia/ocab098 pmid:34151987 fatcat:yjrtkqflwzcvdad2a7ukr43fy4

Winter Workshop 2019 [article]

University, Nottingham Trent, Connected Everything
learning and connected devices are helping to shape the future of our smart industry.  ...  This workshop will bring a multidisciplinary audience interested in understanding Computational Intelligence and Cognitive Robotics by considering ideas and topics presented by our prestige set of speakers  ...  He is a member of the Department of Health's National Institute for Health Research invention for innovation funding panel (NIHR i4i) and holds a number of journal editorial positions.  ... 
doi:10.6084/m9.figshare.20291340.v1 fatcat:ff7uztgezvbuboeorgdq4qmzci

Sentiment Analysis of Microtakaful Industry: Comparison between Indonesia and Malaysia

Aam Slamet Rusydiana, Irman Firmansyah, Lina Marlina
2019 International Journal of Nusantara Islam  
Data were analyzed using the software Semantria as an analytical tool in the form of text.  ...  The results showed that the assessment of existence of microtakaful in Indonesia amounted to 52% of the community showed positive sentiment, 28% indicate negative sentiment and 20% indicates a neutral  ...  Basically, text mining is an interdisciplinary field that refers to the acquisition of information (information retrieval), data mining, machine learning (machine learning), statistical, and computational  ... 
doi:10.15575/ijni.v6i1.3004 fatcat:x5m5cfzozbgrxmiqtpkzgozdye

The National Institutes of Health Affordable Cancer Technologies Program: Improving Access to Resource-Appropriate Technologies for Cancer Detection, Diagnosis, Monitoring, and Treatment in Low- and Middle-Income Countries

Paul C. Pearlman, Rao Divi, Michael Gwede, Pushpa Tandon, Brian S. Sorg, Miguel R. Ossandon, Lokesh Agrawal, Vinay Pai, Houston Baker, Tiffani Bailey Lash
2016 IEEE Journal of Translational Engineering in Health and Medicine  
In response to this need, the National Cancer Institute has partnered with the National Institute of Biomedical Imaging and Bioengineering to create the National Institutes of Health Affordable Cancer  ...  This program seeks to simplify the pathway to market by funding multidisciplinary investigative teams to adapt and validate the existing technologies for cancer detection, diagnosis, and treatment in LMIC  ...  Walter Reed Army Institute of Research, and the National Center of Research Resources, Center for Scientific Review, National Institutes of Health's Division of Research Grants.  ... 
doi:10.1109/jtehm.2016.2604485 pmid:27730015 pmcid:PMC5052025 fatcat:czqcbxffr5a6tob2owkrer455q

Artificial Intelligence (AI) in Evidence-Based Approaches to Effectively Respond to Public Health Emergencies [chapter]

Lap Yan Wong, Chun Kit Yip, Dao Shen Tan, Wai Lim Ling
2021 Evidence-Based Approaches to Effectively Respond to Public Health Emergencies [Working Title]  
Artificial intelligence (AI) techniques have been commonly used to track, predict early warning, forecast trends, and model and measure public health responses.  ...  AI-enabled methods, such as machine learning and deep learning–based models, have exploded in popularity recently, complementing statistical approaches.  ...  This chapter is distributed under the terms of the Creative Commons Attribution License ( by/3.0), which permits unrestricted use, distribution, and reproduction in  ... 
doi:10.5772/intechopen.97499 fatcat:e7wsq7mu3bh2lbrly3wou77xkq

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Simon Williams, Hugo Layard Horsfall, Jonathan P. Funnell, John G. Hanrahan, Danyal Z. Khan, William Muirhead, Danail Stoyanov, Hani J. Marcus
2021 Cancers  
In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the  ...  operation, and better predicting the prognosis.  ...  Conflicts of Interest: D/S/ is an employee of Digital Surgery, Medtronic, which is developing products related to the research described in this paper. No funding was applied for this study.  ... 
doi:10.3390/cancers13195010 pmid:34638495 pmcid:PMC8508169 fatcat:35mmlo2jcjfwdgwtx6lbctq56i

Public Health, Population Health, and Epidemiology Informatics: Recent Research and Trends in the United States

B. L. Massoudi, K. G. Chester
2017 IMIA Yearbook of Medical Informatics  
Methods: We conducted a review of English-language research works conducted in the domain of public and population health informatics and published in MEDLINE or Web of Science between January 2015 and  ...  Selected articles were presented using a thematic analysis based on the 2011 American Medical Informatics Association (AMIA) Public Health Informatics Agenda tracks as a typology.  ...  Mini-Sentinel program (medical product safety); the National Patient-Centered Clinical Research Network (comparative effectiveness research); the National Institutes of Health's Health Care Systems Research  ... 
doi:10.1055/s-0037-1606510 fatcat:hcvwkmk4arhddconlfjqb4gbae

Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing

Shane O'Sullivan, Zulfiqur Ali, Xiaoyi Jiang, Reza Abdolvand, M Selim Ünlü, Hugo Plácido da Silva, Justin T. Baca, Brian Kim, Simon Scott, Mohammed Imran Sajid, Sina Moradian, Hakhamanesh Mansoorzare (+1 others)
2019 Sensors  
Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends.  ...  The most important methodologies of machine learning, including deep learning methods, are summarised.  ...  Conflicts of Interest: The authors declare no conflict of interest. This paper does not raise any ethical issues.  ... 
doi:10.3390/s19081917 fatcat:vppwb3tdtrcvvokmwqalpkomai

Deep Phenotyping: Embracing Complexity and Temporality—Towards Scalability, Portability, and Interoperability

Chunhua Weng, Nigam Shah, George Hripcsak
2020 Journal of Biomedical Informatics  
other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source.  ...  The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website.  ...  Acknowledgments The editors acknowledge funding support from the United States National Institutes of Health grants R01LM009886-10, R01LM006910-19, U01HG008680-05 and R01LM011369-07.  ... 
doi:10.1016/j.jbi.2020.103433 pmid:32335224 pmcid:PMC7179504 fatcat:53xsdcycxvgq5h6wkfivz4azei

Classifying the lifestyle status for Alzheimer's disease from clinical notes using deep learning with weak supervision

Zitao Shen, Dalton Schutte, Yoonkwon Yi, Anusha Bompelli, Fang Yu, Yanshan Wang, Rui Zhang
2022 BMC Medical Informatics and Decision Making  
By comparing with the traditional machine learning models, the study also demonstrates the high performance of BERT models for classifying lifestyle status for Alzheimer's disease in clinical notes.  ...  We conducted weak supervision for pre-trained Bidirectional Encoder Representations from Transformers (BERT) models and three traditional machine learning models as baseline models on the weakly labeled  ...  Acknowledgements YY thanks the University of Minnesota's Undergraduate Research Opportunities Program (UROP).  ... 
doi:10.1186/s12911-022-01819-4 pmid:35799294 pmcid:PMC9261217 fatcat:jrqmsazycvdbhmo5qqnlny3e3u

A digital health industry cohort across the health continuum

Adam B. Cohen, E. Ray Dorsey, Simon C. Mathews, David W. Bates, Kyan Safavi
2020 npj Digital Medicine  
We performed a cross-sectional study of a US digital health industry cohort that received publicly disclosed funding from 2011-2018.  ...  We assessed the number of companies; respective funding within each part of the health continuum; and products and services by technology type, clinical indication, purchasers, and end users.  ...  Brock Wester of the Johns Hopkins University Applied Physics Laboratory, and the research team at Rock Health, including Megan Zweig and Sean Day.  ... 
doi:10.1038/s41746-020-0276-9 pmid:32411829 pmcid:PMC7217869 fatcat:3ndyuenobzcdne7mykbhpplriu

"AI's gonna have an impact on everything in society, so it has to have an impact on public health": a fundamental qualitative descriptive study of the implications of artificial intelligence for public health

Jason D. Morgenstern, Laura C. Rosella, Mark J. Daley, Vivek Goel, Holger J. Schünemann, Thomas Piggott
2021 BMC Public Health  
and risks such as propagation of bias, exacerbation of inequity, hype, and poor regulation.  ...  Methods We used a fundamental qualitative descriptive study design, enrolling 15 experts in public health and AI from June 2018 until July 2019 who worked in North America and Asia.  ...  loop of learning and action.  ... 
doi:10.1186/s12889-020-10030-x pmid:33407254 fatcat:e2ip6w5movaz3jp77oz3xoei3m
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