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arXiv 2024年

作者： Isaradech, Natthanaphop Riedel, Andrea Sirikul, Wachiranun Kreuzthaler, Markus Schulz, Stefan Department of Community Medicine Faculty of Medicine Chiang Mai University Thailand Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Medical Center for Information and Communication Technology Universitätsklinikum Erlangen Germany Center of Data Analytics and Knowledge Synthesis for Health Care Institute for Medical Informatics Statistics and Documentation Medical University of Graz Austria

Introduction: Medication prescriptions are often in free text and include a mix of two languages, local brand names, and a wide range of idiosyncratic formats and abbreviations. Large language models (LLMs) have shown promising ability to generate text in response to input prompts. We use ChatGPT 3.5 to automatically structure and expand medication statements in discharge summaries and thus make them easier to interpret for people and machines. Methods: Named-entity Recognition (NER) and Text Expansion (EX) are used in a zero- and few-shot setting with different prompt strategies. 100 medication statements were manually annotated and curated. NER performance was measured by using strict and partial matching. For the task EX, two experts interpreted the results by assessing semantic equivalence between original and expanded statements. The model performance was measured by precision, recall, and F1 score. Results: For NER, the best-performing prompt reached an average F1 score of 0.94 in the test set. For EX, the few-shot prompt showed superior performance among other prompts, with an average F1 score of 0.87. Conclusion: Our study demonstrates good performance for NER and EX tasks in free-text medication statements using ChatGPT. Compared to a zero-shot baseline, a few-shot approach prevented the system from hallucinating, which would be unacceptable when processing safety-relevant medication data. © 2024, CC BY-NC-ND.

关键词： Zero-shot learning

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data anonymization: a novel optimal k-anonymity algorithm for identical generalization hierarchy data in IoT

Data anonymization: a novel optimal k-anonymity algorithm fo...

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作者： Mahanan, Waranya Chaovalitwongse, W. Art Natwichai, Juggapong Computer Engineering Department Chiang Mai University Chiang Mai Thailand Center of Data Analytics and Knowledge Synthesis for Healthcare Chiang Mai University Chiang Mai Thailand Department of Industrial Engineering University of Arkansas Arkansas United States

Advancement in the Internet of Things (IoT) technologies makes life more convenient for people. data sensed from the devices can be used for analyzing and responding to people’s needs seamlessly. An important consequence of such convenience is that privacy protection becomes a very important issue to be addressed effectively. Various data anonymization model has been proposed for such issue—one of the most widely applied models is the k-anonymity. The k-anonymity prevents the re-identification by replacing the input data with its more general form for transforming the data to have at least k identical tuples. In this paper, we focus on a special case of the input datasets which all the quasi-identifiers, the linkable attributes in the dataset, have identical data types, so-called identical generalization hierarchy (IGH). The solutions for such case will be applicable effectively to address the general IoT data privacy protection due to its data nature. We proposed a novel method to provide a globally optimized k-anonymity solution for the IGH datasets. The proposed algorithms determine an optimal solution based on the characteristics of the IGH data by visiting and evaluating only essential nodes of generalization lattice that satisfy the k-anonymity. Since the k-anonymization problem is an NP-hard, we show that our algorithm can efficiently find an optimal k-anonymity solutions with exploiting such special characteristics of the IGH data, i.e., the optimality between the nodes in different levels of generalization lattice. From the experimental results, it is obvious that our algorithm is much more efficient than the comparative algorithms by less searching on the given lattice. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Internet of things

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Draught and Odors: The Key of IAQ Perception to SBS and Office Environment in University Hospital - the Airmed Project

SSRN

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SSRN 2023年

作者： Surawattanasakul, Vithawat Sirikul, Wachiranun Sapbamrer, Ratana Wangsan, Kampanat Assavanopakun, Pheerasak Panumasvivat, Jinjuta Department of Community Medicine Faculty of Medicine Chiang Mai University Chiang Mai50200 Thailand Occupational and Environmental Medicine Unit Department of Community Medicine Faculty of Medicine Chiang Mai University Chiang Mai50200 Thailand Faculty of Medicine Chiang Mai University Chiang Mai50200 Thailand Center of Data Analytics and Knowledge Synthesis for Health Care Chiang Mai University Chiang Mai50200 Thailand

Background Indoor air quality (IAQ) is a growing concern, yet the link between IAQ perception, sick building syndromes (SBS), and actual IAQ parameters remains unclear. This study aims to investigate the association between perceived IAQ, SBS, and actual chemical parameters. Method A cross-sectional study was conducted in the academic medical center offices using a standardized self-assessment questionnaire for working conditions, IAQ perception and SBS. Formaldehyde and carbondioxide levels were measured and compared with international standards. Statistical analysis, including multivariable logistic and linear regression (p © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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An improved pharmacometric model for artesunate treatment of falciparum malaria

Research Square

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Research Square 2022年

作者： Saralamba, Sompob Simpson, Julie A. Choosri, Noppon White, Lisa Pan-Ngum, Wirichada Dondorp, Arjen M. White, Nicholas J. Mahidol Oxford Tropical Medicine Research Unit Mahidol University Thailand Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health The University of Melbourne Australia Center of Data Analytics and Knowledge Synthesis for Healthcare Chiang Mai University Thailand Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Medicine University of Oxford United Kingdom Department of Tropical Hygiene Faculty of Tropical Medicine Mahidol University Thailand Mahidol Oxford Tropical Medicine Research Unit Faculty of Tropical Medicine Mahidol University Thailand

Previous mathematical models characterising the pharmacokinetic and pharmacodynamic properties of artemisinins in-vivo have not accounted satisfactorily for observed dose-response relationships. We present a new mathematical model of antimalarial pharmacodynamics which incorporates the hypothesis that parasites enter a transitional unresponsive state after contact with artemisinins, followed either by delayed death or reactivation. The model predictions are consistent with both in vitro and in vivo study results. © 2022, CC BY.

关键词： Pharmacodynamics

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Privacy preservation for recommendation databases

Privacy preservation for recommendation databases

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作者： Riyana, Surapon Natwichai, Juggapong Data Engineering and Network Technology Laboratory Faculty of Engineering Chiang Mai University Chiang Mai Thailand Center of Data Analytics and Knowledge Synthesis for Healthcare Chiang Mai University Chiang Mai Thailand

Since recommendation systems play an important role in the current situations where such digital transformation is highly demanded, the privacy of the individuals’ collected data in the systems must be secured effectively. In this paper, the vulnerability of the existing query framework for the recommendation systems is identified. Thus, we propose to apply the well-known k-anonymity model to generalize the given recommendation databases to satisfy the privacy preservation constraint. We show that such data generalization problem which minimizes the impact on data utility is NP-hard. To tackle with such problem, an algorithm to preserve the privacy of the individuals in the recommendation databases is proposed. The idea is to avoid excessive generalizing on the databases by forming a group of similar tuples in the databases. Thus, the impact on the data utility of the generalizing such group can be minimized. Our work is evaluated by extensive experiments. From the results, it is found that our work is highly effective, i.e., the impact quantified by the data utility metrics and the errors of the query results are less than the compared algorithms, and also it is highly efficient, i.e., the execution time is less than the result of its effectiveness-comparable algorithm by more than three times. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Recommender systems

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Correction: Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

JMIR aging

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JMIR aging 2025年 8卷 e75690页

作者： Natthanaphop Isaradech Wachiranun Sirikul Nida Buawangpong Penprapa Siviroj Amornphat Kitro Department of Community Medicine Faculty of Medicine Chiang Mai University 110 Intrawarorot Road Meaung 50200 Thailand 66 53935472 66 935476. Environmental and Occupational Medicine Excellence Center Faculty of Medicine Chiang Mai University Chiang Mai Thailand. Center of Data Analytics and Knowledge Synthesis for Health Care Faculty of Medicine Chiang Mai University Chiang Mai Thailand. Department of Biomedical Informatics and Clinical Epidemiology Faculty of Medicine Chiang Mai University Chiang Mai Thailand. Department of Family Medicine Faculty of Medicine Chiang Mai University Chiang Mai Thailand.

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Investigation of game preference in the University Student 12

Investigation of game preference in the University Student

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12th International Conference on Interfaces and Human Computer Interaction 2018, 11th International Conference on Game and Entertainment Technologies 2018 and 12th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2018, part of the Multi Conference on Computer Science and Information Systems 2018, MCCSIS 2018

作者： Choosri, Noppon Wongta, Noppon Palee, Patison Jitmun, Waritsara Swangtrakul, Napatsayod College of Arts Media and Technology Chiang Mai University Thailand Center of Data Analytics and Knowledge Synthesis for Healthcare Chiang Mai University Thailand 239 Huaykaew Rd. Suthep MuangChiang Mai50200 Thailand

ISBN: (纸本)9789898533791

Understanding gaming preference of users can increase the chance of success in game production. Under the concern in developing effective education game for undergraduate students, this study investigates the game preference of Thai university students in the different field of studies to benefit the ongoing education game projects. The important findings from the survey showed the difference in preference in some game genres between male and female which are Action, Action adventure, Role-playing and Strategy game. There is no significant impact of the field of studies to game preference. It has also been identified that the students who have physical activities in their leisure times tend to like to play the strategy games. Further, the study primarily examines the reason for preferences from some of the famous games. The most identified satisfactory criteria of game selections are whether the game is fun and the graphic of the game are attractive. © 2018.

关键词： Surveys

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019

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eClinicalMedicine 2023年 64卷 102193-102193页

作者： Wu, Dongze Jin, Yingzhao Xing, Yuhan Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abd-Allah, Foad Abdelmasseh, Michael Abdollahifar, Mohammad-Amin Abdulah, Deldar Morad Abedi, Aidin Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abuabara, Katrina Abyadeh, Morteza Addo, Isaac Yeboah Adeniji, Kayode Nelson Adepoju, Abiola Victor Adesina, Miracle Ayomikun Adnani, Qorinah Estiningtyas Sakilah Afarideh, Mohsen Aghamiri, Shahin Agodi, Antonella Agrawal, Anurag Arriagada, Constanza Elizabeth Aguilera Ahmad, Aqeel Ahmad, Danish Ahmad, Sohail Ahmadi, Ali Ahmed, Ayman Aithala, Janardhana P. Ajadi, Abdullateef Abiodun Ajami, Marjan Akbarzadeh-Khiavi, Mostafa Alahdab, Fares AlBataineh, Mohammad T. Alemi, Sharifullah Al-Gheethi, Adel Ali Saeed Ali, Liaqat Alif, Sheikh Mohammad Almazan, Joseph Uy Almustanyir, Sami Alqahtani, Jaber S. Alqasmi, Ibrahim Altaf, Ihsan Ullah Khan Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Al-Worafi, Yaser Mohammed Aly, Hany Amani, Reza Amu, Hubert Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Ansar, Adnan Ansariniya, Hossein Anyasodor, Anayochukwu Edward Arabloo, Jalal Arefnezhad, Reza Arulappan, Judie Asghari-Jafarabadi, Mohammad Ashraf, Tahira Atata, Jamila Abdulhamid Athari, Seyyed Shamsadin Atlaw, Daniel Atout, Maha Moh'd Wahbi Aujayeb, Avinash Awan, Asma Tahir Ayatollahi, Haleh Azadnajafabad, Sina Azzam, Ahmed Y. Badawi, Alaa Badiye, Ashish D. Bagherieh, Sara Baig, Atif Amin Bantie, Berihun Bantie Barchitta, Martina Bardhan, Mainak Barker-Collo, Suzanne Lyn Barone-Adesi, Francesco Batra, Kavita Bayileyegn, Nebiyou Simegnew Behnoush, Amir Hossein Belgaumi, Uzma Iqbal Bemanalizadeh, Maryam Bensenor, Isabela M. Beyene, Kebede A. Bhagavathula, Akshaya Srikanth Bhardwaj, Pankaj Bhaskar, Sonu Bhat, Ajay Nagesh Bitaraf, Saeid Bitra, Veera R. Boloor, Archith Bora, Kaustubh Botelho, João Silva Buchbinder, Rachelle Calina, Daniela Cámera, Luis Alberto Carvalho, Andre F. Chan, Jeffrey Shi Kai Chattu, Vijay Kumar Abebe, Endeshaw Chekol Department of Rheumatology and Immunology Sichuan Provincial People's Hospital School of Medicine University of Electronic Science and Technology of China China Department of Medicine & Therapeutics The Chinese University of Hong Kong Hong Kong Department of Paediatrics The Chinese University of Hong Kong Hong Kong School of Pharmacy The Chinese University of Hong Kong Hong Kong Jockey Club School of Public Health and Primary Care The Chinese University of Hong Kong Hong Kong Department of Nursing Woldia University Woldia Ethiopia Department of Orthopedic Surgery Harvard University Boston MA United States Postgraduate Medical Education Harvard University Boston MA United States Maternal Fetal Care Center Harvard University Boston MA United States Department of Health Policy and Oral Epidemiology Harvard University Boston MA United States Division of General Internal Medicine Harvard University Boston MA United States Department of Orthopaedic Surgery Shahid Beheshti University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Biotechnology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran National Nutrition and Food Technology Research Institute Shahid Beheshti University of Medical Sciences Tehran Iran Department of Immunology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Medical Genetics Shahid Beheshti University of Medical Sciences Tehran Iran Center for Comprehensive Genetic Services Shahid Beheshti University of Medical Sciences Tehran Iran Cancer Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Functional Neuro

Background: The causes for immune-mediated inflammatory diseases (IMIDs) are diverse and the incidence trends of IMIDs from specific causes are rarely studied. The study aims to investigate the pattern and trend of IMIDs from 1990 to 2019. Methods: We collected detailed information on six major causes of IMIDs, including asthma, inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, psoriasis, and atopic dermatitis, between 1990 and 2019, derived from the Global Burden of Disease study in 2019. The average annual percent change (AAPC) in number of incidents and age standardized incidence rate (ASR) on IMIDs, by sex, age, region, and causes, were calculated to quantify the temporal trends. Findings: In 2019, rheumatoid arthritis, atopic dermatitis, asthma, multiple sclerosis, psoriasis, inflammatory bowel disease accounted 1.59%, 36.17%, 54.71%, 0.09%, 6.84%, 0.60% of overall new IMIDs cases, respectively. The ASR of IMIDs showed substantial regional and global variation with the highest in High SDI region, High-income North America, and United States of America. Throughout human lifespan, the age distribution of incident cases from six IMIDs was quite different. Globally, incident cases of IMIDs increased with an AAPC of 0.68 and the ASR decreased with an AAPC of −0.34 from 1990 to 2019. The incident cases increased across six IMIDs, the ASR of rheumatoid arthritis increased (0.21, 95% CI 0.18, 0.25), while the ASR of asthma (AAPC = −0.41), inflammatory bowel disease (AAPC = −0.72), multiple sclerosis (AAPC = −0.26), psoriasis (AAPC = −0.77), and atopic dermatitis (AAPC = −0.15) decreased. The ASR of overall and six individual IMID increased with SDI at regional and global level. Countries with higher ASR in 1990 experienced a more rapid decrease in ASR. Interpretation: The incidence patterns of IMIDs varied considerably across the world. Innovative prevention and integrative management strategy are urgently needed to mitigate the increasing ASR of rh

关键词： Global burden of disease study Immune-mediated inflammatory disease Incidence Trend

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