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A Survey on Explainable Artificial Intelligence for Cybersecurity

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (ai) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (Xai) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, Xai has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in Xai for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current Xai methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

Leveraging Antiviral Lectins for Developing Glycan-Based Vaccines by Employing Standard and ai-Driven Approaches

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Leveraging Antiviral Lectins for Developing Glycan-Based Vac...

Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Ayushi Goyal Razia Sultana Sarosh Saeed Piyush Bhati Varsha Kumawat Rishabh Kumar Neetu Vishwas Tripathi Mohd Jawed Khan Abdul Arif Khan Kanu Priya Rana Zaidi Siya Ram Gunjan Garg Mohd. Tashfeen Ashraf School of Biotechnology Gautam Buddha University Greater Noida India Government Institute of Medical Sciences Greater Noida India Department Computer Science & Engineering (AI&ML) GNIT Greater Noida India Division of Microbiology Indian Council of Medical Research National AIDS Research Institute Pune India Department of Life Sciences Sharda School of Basic Sciences and Research Sharda University Greater Noida India Department of Biochemistry School of Chemical & Life Sciences New Delhi India Division of Life Sciences School of Sciences Indira Gandhi National Open University New Delhi India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

In last three decades, astounding research data on antiviral lectins has been generated and their potential in playing a significant role in controlling emerging viruses like SARS-CoV-2 is being acknowledged. With recent research revealing the change in conformation of spike protein after fusion with the host cell, it is believed that vaccines developed against epitopes of pre-fusion conformation may not be as effective as was earlier anticipated. Besides, on the other hand, ai based tools can address the issue of struggling with rapidly-changing viruses by enabling precise glycan modeling, optimizing delivery methods, and improving host defence through glycan editing. This work focuses on the diverse physiological roles of lectins, with functional details of various antiviral lectins and the role various members of this class may play in strengthening the interaction with glycan based viral epitopes. Besides, ai-driven techniques discussed herein may provide significant mileage in facilitating the development of glycan-based vaccines that employ lectins with specificity against the viral epitopes. The recently characterized envelope glycans of SARS-CoV-2 are being discussed as a case in point.

关键词： Proteins Hands Nanoparticles Protein engineering Fluids Fuses Physiology Vaccines Coronaviruses Standards

Wireless,multimodal sensors for continuous measurement of pressure,temperature,and hydration of patients in wheelchair

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npj Flexible Electronics 2023年第1期7卷 479-493页

作者： Seokjoo Cho Hyeonseok Han Hyunwoo Park Sung-Uk Lee Jae-Hwan Kim Sung Woo Jeon Mengqiu Wang Raudel Avila Zhaoqian Xi Kabseok Ko Minsu Park Jungyup Lee Myungwoo Choi Je-Sang Lee Weon Gi Min Byeong-Ju Lee Soyeong Lee Jungrak Choi Jimin Gu Jaeho Park Min Seong Kim Junseong Ahn Osman Gul Chankyu Han Gihun Lee Seunghwan Kim Kyuyoung Kim Jeonghyun Kim Chang-Mo Kang Jahyun Koo Sung Soo Kwak Sungbong Kim Dong Yun Choi Seokwoo Jeon Hyung Jin Sung Yong Bae Park Minkyu Je Young Tae Cho Yong Suk Oh Inkyu Park Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Advanced 3D Printing Technology Development Division Korea Atomic Energy Research InstituteDaejeon 34057Republic of Korea Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian 116023P.R.China Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA DUT-BSU Joint Institute Dalian University of TechnologyDalian 116023China Ningbo Institute of Dalian University of Technology Ningbo 315016China Department of Electronics Engineering Kangwon National UniversityChuncheon 24341Republic of Korea Querrey-Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Sibel Health Inc NilesIL 60714USA Department of Materials Science and Engineering KAIST Institute for The Nanocentury(KINC)Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of planning and development Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of rehabilitation medicine Pusan national university hospitalBusan 49241Republic of Korea Department of AI Convergence Network Ajou UniversitySuwon 16499Republic of Korea Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897Republic of Korea Korea Photonics Technology Institute Gwangju 61007Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02841Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02841Republic of Korea Center

Individuals who are unable to walk independently spend most of the day in a *** population is at high risk for developing pressure injuries caused by ***,early diagnosis and prevention of these injuries still remain ***,we introduce battery-free,wireless,multimodal sensors and a movable system for continuous measurement of pressure,temperature,and hydration at skin *** device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity,a temperature sensor for measuring skin temperature,and a galvanic skin response sensor for measuring skin hydration *** movable system enables power harvesting,and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body *** evaluations and numerical simulations of the devices,together with clinical trials for wheelchair patients,demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

关键词： injuries modal mounted

Finite-time consensus protocols for multi-dimensional multi-agent systems

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Control Theory and Technology 2020年第4期18卷 419-430页

作者： Jieqiang Wei Bart Besselink Junfeng Wu Henrik Sandberg Karl H.Johansson Ericsson Global AI Accelerator Torshamnsgatan 21StockholmSweden Bernoulli Institute for Mathematics Computer Science and Artificial IntelligenceUniversity of GroningenP.O.Box 4079700 GroningenAKThe Netherlands College of Control Science and Engineering Zhejiang University310027 HangzhouChina Division of Decision and Control Systems KTH Royal Institute of Technology10044 StockholmSweden

A fnite.-time consensus protocol is proposed for multi -dimensional multi- agent systems, using direction peserving signumcontrols. Flipp solutions and nonsmooh analysis tehniques are adopted to handle discontinuities. Suficient and ncessaryconditions are provided to guarantee infinte time convergence and boundedness of the solutions. It turns out that the numberof agents which have cotinuous contol law plays an ssenan role in fnite-tine conerence In adidio it is shown thatthe unit bals itoduced bylp, norms. where p ∈[1,∞] , are inariat for the closed lop.

关键词： Multi-agent systems Network consensus Finite time convergence Nonsmooth analysis

On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality

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arXiv 2024年

作者： Hu, Jerry Yao-Chieh Wu, Weimin Lee, Yi-Chen Huang, Yu-Chao Chen, Minshuo Liu, Han Center for Foundation Models and Generative AI Northwestern University EvanstonIL60208 United States Department of Computer Science Northwestern University EvanstonIL60208 United States Department of Physics National Taiwan University Taipei106319 Taiwan Physics Division National Center for Theoretical Sciences Taipei106319 Taiwan Department of Industrial Engineering & Management Sciences Northwestern University EvanstonIL60208 United States Department of Statistics and Data Science Northwestern University EvanstonIL60208 United States

We investigate the approximation and estimation rates of conditional diffusion transformers (DiTs) with classifier-free guidance. We present a comprehensive analysis for "in-context" conditional DiTs under four common data assumptions. We show that both conditional DiTs and their latent variants lead to the minimax optimality of unconditional DiTs under identified settings. Specifically, we discretize the input domains into infinitesimal grids and then perform a term-by-term Taylor expansion on the conditional diffusion score function under Hölder smooth data assumption. This enables fine-grained use of transformers’ universal approximation through a more detailed piecewise constant approximation and hence obtains tighter bounds. Additionally, we extend our analysis to the latent setting under the linear latent subspace assumption. We not only show that latent conditional DiTs achieve lower bounds than conditional DiTs both in approximation and estimation, but also show the minimax optimality of latent unconditional DiTs. Our findings establish statistical limits for conditional and unconditional DiTs, and offer practical guidance toward developing more efficient and accurate DiT models. © 2024, CC BY-NC-SA.

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Digital Health during COVID-19: Informatics Dialogue with the World Health Organization

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Yearbook of Medical Informatics 2021年第1期30卷 13-16页

作者： Koch, Sabine Hersh, William R. Bellazzi, Riccardo Leong, Tze Yun Yedaly, Moctar Al-Shorbaji, Najeeb Health Informatics Centre Department of Learning Informatics Management and Ethics Karolinska Institutet Stockholm Sweden Department of Medical Informatics & Clinical Epidemiology School of Medicine Oregon Health & Science University Portland OR United States Department of Electrical Computer and Biomedical Engineering University of Pavia Italy IRCCS ICS Maugeri Pavia Italy Department of Computer Science School of Computing National University of Singapore and AI Singapore Singapore Information Society Division African Union Commission Addis Ababa Ethiopia eHealth Development Association Amman Jordan

Background: On December 16, 2020 representatives of the International Medical Informatics Association (IMIA), a Non-Governmental Organization in official relations with the World Health Organization (WHO), along with its International Academy for Health Sciences Informatics (IAHSI), held an open dialogue with WHO Director General (WHO DG) Tedros Adhanom Ghebreyesus about the opportunities and challenges of digital health during the COVID-19 global pandemic. Objectives: The aim of this paper is to report the outcomes of the dialogue and discussions with more than 200 participants representing different civil society organizations (CSOs). Methods: The dialogue was held in form of a webinar. After an initial address of the WHO DG, short presentations by the panelists, and live discussions between panelists, the WHO DG and WHO representatives took place. The audience was able to post questions in written. These written discussions were saved with participants’ consent and summarized in this paper. Results: The main themes that were brought up by the audience for discussion were: (a) opportunities and challenges in general;(b) ethics and artificial intelligence;(c) digital divide;(d) education. Proposed actions included the development of a roadmap based on the lessons learned from the COVID-19 pandemic. Conclusions: Decision making by policy makers needs to be evidence-based and health informatics research should be used to support decisions surrounding digital health, and we further propose next steps in the collaboration between IMIA and WHO such as future engagement in the World Health Assembly. © 2021 Thieme Medical Publishers, Inc.. All rights reserved.

关键词： Artificial Intelligence COVID-19 Global Health Medical Informatics World Health Organization

Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

A Cross-institutional Evaluation on Breast Cancer Phenotyping NLP Algorithms on Electronic Health Records

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arXiv 2023年

作者： Zhou, Sicheng Wang, Nan Wang, Liwei Sun, Ju Blaes, Anne Liu, Hongfang Zhang, Rui MS Institute for Health Informatics University of Minnesota MinneapolisMN United States School of Statistics University of Minnesota MinneapolisMN United States Department of AI and Informatics Research Mayo Clinic RochesterMN United States Department of Computer Science & Engineering University of Minnesota MinneapolisMN United States Department of Medicine University of Minnesota MinneapolisMN United States Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States

Objective: The generalizability of clinical large language models is usually ignored during the model development process. This study evaluated the generalizability of BERT-based clinical NLP models across different clinical settings through a breast cancer phenotype extraction task. Materials and Methods: Two clinical corpora of breast cancer patients were collected from the electronic health records from the University of Minnesota and the Mayo Clinic, and annotated following the same guideline. We developed three types of NLP models (i.e., conditional random field, bi-directional long short-term memory and CancerBERT) to extract cancer phenotypes from clinical texts. The models were evaluated for their generalizability on different test sets with different learning strategies (model transfer vs. locally trained). The entity coverage score was assessed with their association with the model performances. Results: We manually annotated 200 and 161 clinical documents at UMN and MC, respectively. The corpora of the two institutes were found to have higher similarity between the target entities than the overall corpora. The CancerBERT models obtained the best performances among the independent test sets from two clinical institutes and the permutation test set. The CancerBERT model developed in one institute and further fine-tuned in another institute achieved reasonable performance compared to the model developed on local data (micro-F1: 0.925 vs 0.932). Conclusions: The results indicate the CancerBERT model has the best learning ability and generalizability among the three types of clinical NLP models. The generalizability of the models was found to be correlated with the similarity of the target entities between the corpora. © 2023, CC BY.

关键词： Natural language processing systems