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Npj health systems 2024年第1期1卷 1页

作者： James Anibal Jasmine Gunkel Shaheen Awan Hannah Huth Hang Nguyen Tram Le Jean-Christophe Bélisle-Pipon Micah Boyer Lindsey Hazen Yael Bensoussan David Clifton Bradford Wood Center for Interventional Oncology Clinical Center National Institutes of Health (NIH) Bethesda MD USA. Computational Health Informatics Lab Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford UK. Department of Bioethics National Institutes of Health (NIH) Bethesda MD USA. Department of Communication Sciences & Disorders University of Central Florida Orlando FL USA. Global Infectious Disease Program Georgetown University Washington DC USA. College of Engineering University of South Florida Tampa FL USA. Faculty of Health Sciences Simon Fraser University Burnaby BC Canada. USF Health Voice Center Department of Otolaryngology-Head & Neck Surgery University of South Florida Tampa FL USA.

Artificial intelligence (AI) methods have been proposed for the prediction of social behaviors that could be reasonably understood from patient-reported information. This raises novel ethical concerns about respect, privacy, and control over patient data. Ethical concerns surrounding clinical AI systems for social behavior verification can be divided into two main categories: (1) the potential for inaccuracies/biases within such systems, and (2) the impact on trust in patient-provider relationships with the introduction of automated AI systems for "fact-checking", particularly in cases where the data/models may contradict the patient. Additionally, this report simulated the misuse of a verification system using patient voice samples and identified a potential LLM bias against patient-reported information in favor of multi-dimensional data and the outputs of other AI methods (i.e., "AI self-trust"). Finally, recommendations were presented for mitigating the risk that AI verification methods will cause harm to patients or undermine the purpose of the healthcare system.

关键词： Ethics Machine learning Science, technology and society

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Developmental stage classification of Embryos using two-stream neural network with linear-chain conditional random field

arXiv

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arXiv 2021年

作者： Lukyanenko, Stanislav Jang, Won-Dong Wei, Donglai Struyven, Robbert Kim, Yoon Leahy, Brian Yang, Helen Rush, Alexander Ben-Yosef, Dalit Needleman, Daniel Pfister, Hanspeter Department of Informatics Technical University of Munich Germany School of Engineering and Applied Sciences Harvard University United States Department of Molecular and Cellular Biology Harvard University United States Graduate Program in Biophysics Harvard University United States University College London United Kingdom MIT United States Cornell University United States Center for Computational Biology Flatiron Institute United States Lis Maternity Hospital Tel-Aviv Sourasky Medical Center Israel Cell and Developmental Biology Tel-Aviv University Israel

The developmental process of embryos follows a monotonic order. An embryo can progressively cleave from one cell to multiple cells and finally transform to morula and blastocyst. For time-lapse videos of embryos, most existing developmental stage classification methods conduct per-frame predictions using an image frame at each time step. However, classification using only images suffers from overlapping between cells and imbalance between stages. Temporal information can be valuable in addressing this problem by capturing movements between neighboring frames. In this work, we propose a two-stream model for developmental stage classification. Unlike previous methods, our two-stream model accepts both temporal and image information. We develop a linear-chain conditional random field (CRF) on top of neural network features extracted from the temporal and image streams to make use of both modalities. The linear-chain CRF formulation enables tractable training of global sequential models over multiple frames while also making it possible to inject monotonic development order constraints into the learning process explicitly. We demonstrate our algorithm on two time-lapse embryo video datasets: i) mouse and ii) human embryo datasets. Our method achieves 98.1% and 80.6% for mouse and human embryo stage classification, respectively. Our approach will enable more profound clinical and biological studies and suggests a new direction for developmental stage classification by utilizing temporal information. Copyright © 2021, The Authors. All rights reserved.

关键词： Classification (of information)

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DEA and Fuzzy Simple Additive Weighting for Benchmarking Qualitative Data 1

DEA and Fuzzy Simple Additive Weighting for Benchmarking Qua...

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1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Abdullah, Dahlan Hartono, H. Ongko, Erianto Erliana, Cut Ita Irwansyah, Defi Iskandar, Akbar Sari, Ayu Esteka Abdussakir Yuniningsih Siregar, Nurintan Asyiah Kahar, Muhammad Syahrul Waliulu, Raditya Faisal Amalia, Ainna Kurniasih, Nuning Arifin, Muhammad Muzid, Syafiul Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Department of Economic and Business Universitas Pembangunan Nasional Veteran Surabaya Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Mathematics Education Department Universitas Muhammadiyah Sorong Sorong Indonesia Informatics Engineering Department Universitas Muhammadiyah Sorong Sorong Indonesia Islamic Institut of Miftahul Ula Kertosono Nganjuk East Java Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Information System Department Universitas Muria Kudus Indonesia

Data Envelopment Analysis (DEA) is one method that is often used in the benchmarking process. The benchmarking process with DEA is often faced with problems where there is a Decision Making Unit (DMU) both Input and Output which is not in the form of qualitative data but in the form of qualitative data. Qualitative data requires an assessment from the appraiser so that it can become quantitative data. This study will use the Fuzzy Simple Additive Weighting method to convert qualitative data into quantitative. The results of this study will produce a model that integrates DEA with Fuzzy Simple Additive Weighting. © Published under licence by IOP Publishing Ltd.

关键词： Data envelopment analysis

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In silico analysis of tetrahydroxycurcumin compound in tumeric (Curcuma longa) as inhibitor of SARS-CoV-2 virus replication

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AIP Conference Proceedings 2023年第1期2748卷

作者： Mohamad Amin Nanda Hilda Khikmawati Suryadi Suryadi Rifcha Fachrunnisa Ihya Fakhrurizal Amin Dina Maulina Ahya Zhilalikbar Amin Nik Ahmad Nizam Nik Malek Restu Fitriani Department of Biology Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang No.5 Malang 65145 Indonesia Special Education Department Faculty of Education Universitas Negeri Malang Jl. Semarang No.5 Malang 65145 Indonesia Faculty of Medicine Universitas Indonesia Jakarta Indonesia Biology Education Study Program Faculty of Teacher Training and Education Universitas Lampung Lampung Indonesia Department of Biosciences Faculty of Science Universiti Teknologi Malaysia Johor Malaysia Study Program of Informatics Engineering Faculty of Computer Sciencies Universitas Brawijaya Malang Indonesia Biotechnology Program Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang No.5 Malang 65145 Indonesia

There are many uses of natural ingredients to find out their potential in overcoming Coronavirus Disease 2019. One of the plants that is often used to treat various diseases, including SARS-Cov2 infection is turmeric. Turmeric contains Tetrahydroxycurcumin compounds. The aim of this research was to study the potential of Tetrahydroxycurcumin in preventing infection with the SARS-CoV-2 virus. The results showed that Tetrahydroxicurcumin has the potential to inhibit Tyrosine protein kinase ABL1, which is a protein that plays a role in the preparation of the cytoskeleton during viral infection. The binding affinity of Tetrahydroxycurcumin for this protein is -8.4 kcal/mol.

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A review of hidden markov models and recurrent neural networks for event detection and localization in biomedical signals

arXiv

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arXiv 2020年

作者： Khalifa, Yassin Mandic, Danilo Sejdić, Ervin Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering Imperial College LondonSW7 2BT United Kingdom Department of Bioengineering University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Intelligent Systems Program University of Pittsburgh PittsburghPA United States

Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals. Copyright © 2020, The Authors. All rights reserved.

关键词： Biomedical signal processing

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Reporting of Hospital Facility on Smartphone

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Journal of Physics: Conference Series 2021年第1期1807卷

作者： Jack Febrian Rusdi Sazilah Salam Nur Azman Abu Budi Sunaryo Nova Agustina Harya Gusdevi Hermansah Ari Hadhiwibowo Tuty Heryati Richki Hardi Rohmat Taufiq Muhammad Taher Jufri Ignatius Sudarsono Eko Wahyu Utomo Frans Richard Kodong Puteri Fannya Informatics Engineering Sekolah Tinggi Teknologi Bandung Bandung Indonesia. Information and Communication Technology Faculty Universiti Teknikal Malaysia Melaka Melaka Malaysia. Department of Electrical Engineering Andalas University Padang Indonesia. Rumah Sakit Umum Daerah Majalaya Kabupaten Bandung Majalaya Indonesia. Informatics Engineering Computer Science Faculty Universitas Mulya Balikpapan Indonesia. Department of Informatics Engineering Engineering Faculty University Muhammadiyah of Tangerang Tangerang 15117 Indonesia Program Studi Sistem Informasi Fakultas Teknik dan Sistem Informasi Universitas Yapis Papua Civil Engineering Department Engineering Faculty Universitas Langlangbuana Bandung Bandung Indonesia. Department of Informatics Engineering UPN 'Veteran' Yogyakarta Yogyakarta Indonesia. Department of Medical Record and Health Information Faculty of Health Universitas Esa Unggul Jakarta Indonesia.

In the context of achieving Good Corporate Governance (GCG) in hospitals, among others, it is carried out with control and supervision, including in the case of hospital facility maintenance installations. Besides that, the hospital needs to improve its services. The asset reporting system is one of the high needs in this field, so it requires a solution that can deal with existing problems. Unfortunately, the current reporting system so far is more likely to be conventional, such as by telephone. This study proposes a new solution to the hospital facility maintenance reporting process, namely by utilising smartphone-based applications that directly connected to the server. So, the information distributed to the competent parties. This solution can speed up the reporting process and actions. Also, save the historical equipment that is available from each service room — the results of this study used as a hospital facility maintenance reporting system.

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Beach Topographic Change Analysis Using Multi-temporal UAV Data

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IOP Conference Series: Earth and Environmental Science 2021年第1期799卷

作者： K T Chang H M Fang S S Hsiao C S Li Associate Professor Department of Civil Engineering and Environmental Informatics Minghsin University of Science and Technology Assistant Professor Bachelor's Degree Program in Ocean Engineering and Technology National Taiwan Ocean University Professor Department of Harbor and River Engineering National Taiwan Ocean University Center of Excellence for Ocean Engineering Master Department of Harbor and River Engineering National Taiwan Ocean University

A time-serial topographic change was performed using two kinds of surveying methods, including direct measurement and indirect remote sensing technology for beach topography surveillance. Compared with reference topographic data, the root mean square error was 0.169 m. From May, 2018 to March, 2019, multi-temporal beach topographic change results indicated that the distance between baseline and shoreline varied in the range of −24.82∼9.58 meter, and weekly variates are −3.16∼0.45 m. The UAV technique can be efficiently used to detect topographic change information in the coastal area.

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User Experience Analysis on Class I LPKA Palembang Filial E-School

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Journal of Physics: Conference Series 2020年第1期1500卷

作者： D. Tri Octafian Febrianty Hendra Hadiwijaya Informatics Engineering Study Program Palembang Palcomtech Polytechnic Indonesia Accounting Study Program Palembang Palcomtech Polytechnic Indonesia Visual Communication Design Study Program Palembang Palcomtech Polytechnic Indonesia

The Class I LPKA Palembang management wishes to present academic services through the provision of systems that can better support education. Therefore, Filial e-school was built and implemented as a learning system in filial schools implemented in LPKA. The e-school system with the link address http://***/, consists of several modules: ***-based learning module, ***-based learning module, ***-based learning module, *** learning media, *** and submission assignment media, *** media. This system facilitates the learning process of LPKA students to be able to learn independently with existing media and make it easier for teachers to deliver learning material. The system development method used in this study was the Rapid Application Development (RAD) method. Testing of user experience on the use of e-school was tested using the User Experience method with the following aspects:Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. Based on the results of UEQ differences between parent schools' teachers and LPKA students on the use of filial e-school, it was found that e-school filial gave slightly higher impressions to parent schools' teachers compared to students. The average results of the UEQ questionnaire for all users of the e-school filial system showed that the stimulation and novelty aspects were felt to be higher than the other four aspects.

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Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes

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Journal of Medical Internet Research 2025年第1期27卷 e71873页

作者： Huang, Feiqing Hou, Jue Zhou, Ningxuan Greco, Kimberly Lin, Chenyu Sweet, Sara Morini Wen, Jun Shen, Lechen Gonzalez, Nicolas Zhang, Sinian Liao, Katherine P. Cai, Tianrun Xia, Zongqi Bourgeois, Florence T. Cai, Tianxi Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States Harvard-MIT Center for Regulatory Science Harvard Medical School Boston MA United States Division of Biostatistics and Health Data Science School of Public Health University of Minnesota Minneapolis MN United States Department of Biomedical Informatics Harvard Medical School Boston MA United States Department of Engineering University of Toronto Toronto ON Canada Division of Rheumatology Inflammation and Immunity Brigham and Women's Hospital Boston MA United States Department of Neurology University of Pittsburgh Pittsburgh PA United States Computational Health Informatics Program (CHIP) Boston Children's Hospital Boston MA United States Department of Pediatrics Harvard Medical School Boston MA United States

Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction. ©Feiqing Huang, Jue Hou, Ningxuan Zhou, Kimberly Greco, Chenyu Lin, Sara Morini Sweet, Jun Wen, Lechen Shen, Nicolas Gonzalez, Sinian Zhang, Katherine P Liao, Tianrun Cai, Zongqi Xia, Florence T Bourgeois, Tianxi Cai.

关键词： calibration causal inference chronic disease outcomes data imputation electronic health records longitudinal disease activity machine learning real-world evidence

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Design and Application Data Collection for integrated evaluation and monitoring

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IOP Conference Series: Materials Science and engineering 2021年第1期1088卷

作者： Muhammad Hidayatullah Agus Teguh Wahyudi Shinta Esabella Titi Andriani G Gunawan Faculty of Engineering Universitas Teknologi Sumbawa Indonesia. 84371 Office of Informatics and Statistics Communication Sumbawa Regency Indonesia. Physics Education Study Program Faculty of Teacher Training and Education Universitas Mataram Indonesia. 83125

The process of data collection of tax subjects and objects at the Regional Revenue Agency (BAPENDA) of Sumbawa Regency is still manual, where the subject and tax object data are still managed in the form of records on bookkeeping media and office application office media applications such as Microsoft Excel. The need for an application design as a form of digitizing a manual process in the governance system to improve the effectiveness and efficiency of the agency. The research aims to develop Tax Object Subject Data Collection Applications. The application is built with Web-based programming, using the PHP programming Language and Sublime Text as Tools Editor and MySQL as a data storage medium. The application built can present data collection features related to tax object subject data such as companies, restaurants, hotels, parking and ground water data and can display reports in the form of data related to tax object and object data collection to facilitate the monitoring process in BAPENDA Sumbawa Regency.

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