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Press-Public Collaboration as Infrastructure: Tracing News Organizations and programming Publics in application programming interfaces

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AMERICAN BEHAVIORAL SCIENTIST 2013年第5期57卷 623-642页

作者： Ananny, Mike Univ So Calif Annenberg Sch Commun & Journalism Los Angeles CA 90089 USA

Understanding and evaluating systems for open collaboration depends, in part, on appreciating their normative and institutional contexts. In this article, I examine press-public collaboration by tracing how and why news organizations both distance themselves from and depend on networked actors outside the newsroom to achieve professional and organizational goals. I situate contemporary press-public networks within infrastructure scholarship, review their relationship to models of the public sphere, and trace the motivations and assumptions embedded within news organizations' application programming interfaces, software toolkits that let those outside the newsroom access and repurpose journalistic data.

关键词： open collaboration networked online journalism press-public infrastructure application programming interface news organizations

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A Unifying Framework to Enable Artificial Intelligence in High Performance Computing Workflows

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Computing in Science & Engineering 2025年第01期27卷 73-78页

作者： Jens Domke Mohamed Wahib Anshu Dubey Tal Ben-Nun Erik W. Draeger RIKEN Center for Computational Science Kobe Japan Argonne National Laboratory Lemont IL USA Lawrence Livermore National Laboratory Livermore CA USA

Current trends point to a future where large-scale scientific applications are tightly-coupled HPC/AI hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/ML can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. The current ecosystem and sparsely-connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.

关键词： High Performance Computing Ecosystems Market Research Hardware Artificial Intelligence Large Scale Integration Computational Intelligence Machine Learning High Performance Computing Research And Development Alternative Models Computer Science Interoperability application programming interface Rapid Advances Load Data Pace Of Change Tight Coupling General Platform Hardware Development Oak Ridge National Laboratory Refactoring Drawing Board Hardware Changes End Users Data Transformation programming Model Elements Of The Framework Co Design

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Deploying Patient-Facing application programming interfaces: Thematic Analysis of Health System Experiences

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JOURNAL OF MEDICAL INTERNET RESEARCH 2020年第4期22卷 e16813页

作者： Neinstein, Aaron Thao, Crishyashi Savage, Mark Adler-Milstein, Julia Univ Calif San Francisco Dept Med San Francisco CA 94143 USA Univ Calif San Francisco Ctr Digital Hlth Innovat 1700 Owens StSuite 541 San Francisco CA 94158 USA Univ Calif San Francisco Dept Med Ctr Clin Informat & Improvement Res San Francisco CA 94143 USA

Background: Health systems have recently started to activate patient-facing application programming interfaces (APIs) to facilitate patient access to health data and other interactions. Objective: This study sought to ascertain health systems' understanding, strategies, governance, and organizational infrastructure around patient-facing APIs, as well as their business drivers and barriers, to facilitate national learning, policy, and progress toward adoption. Methods: We performed a content analysis of semistructured interviews with a convenience sample of 10 health systems known to be leading adopters of health technology, having either implemented or planning to implement patient-facing APIs. Results: Of the 10 health systems, eight had operational patient-facing APIs, with organizational strategy driven most by federal policy, the emergence of Health Records on iPhone, and feelings of ethical obligation. The two priority use cases identified were enablement of a patient's longitudinal health record and digital interactions with the health system. The themes most frequently cited as barriers to the increased use of patient-facing APIs were security concerns, an immature app ecosystem that does not currently offer superior functionality compared with widely adopted electronic health record (EHR)-tethered portals, a lack of business drivers, EHR vendor hesitation toward data sharing, and immature technology and standards. Conclusions: Our findings reveal heterogeneity in health system understanding and approaches to the implementation and use of patient-facing APIs. Ongoing study, targeted policy interventions, and sharing of best practices appear necessary to achieve successful national implementation.

关键词： application programming interface consumer health information electronic health record health information exchange health policy patient engagement

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Enhancing greenhouse management with interpretable AI: A natural language interface for advanced and optimization-based control

Smart Agricultural Technology

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Smart Agricultural Technology 2025年 11卷

作者： Ramesh Arvind Naagarajan Stefan Streif Chair of Automatic Control and System Dynamics Chemnitz University of Technology Chemnitz Germany Department of Bioresources Fraunhofer Institute for Molecular Biology and Applied Ecology Giessen Germany

As climate change and resource scarcity threaten global food security, greenhouse systems are becoming critical for sustainable agriculture. Advanced control, such as Model Predictive Control (MPC), effectively regulates temperature, humidity, and CO 2 to enhance crop growth and resource efficiency. However, the widespread adoption of such advanced control systems is limited by their lack of interpretability, as growers struggle to understand complex control decisions, particularly during rapid environmental changes. In this work, a Natural Language Generation (NLG) interface has been developed to bridge this gap and transform MPC control decisions into clear, actionable explanations for greenhouse growers. This interface integrates Large Language Models (LLMs) with greenhouse control systems and mathematical constraints, providing a step toward making AI-driven agriculture more accessible. This integration addresses the need for interpretable AI systems in modern agricultural applications. The system allows growers to interact with the control system, query decisions, and receive contextually relevant explanations through Retrieval Augmented Generation (RAG) mechanisms and instruction prompting techniques. The Adaptive RAG (ARAG) framework was evaluated using semantic similarity, information retrieval, and contextual relevance metrics, demonstrating a 12.1% improvement in BERTScore, over baseline methods. These results highlight the system's ability to deliver accurate, well-structured explanations without compromising control performance. By improving the interpretability and accessibility of AI-powered greenhouse automation, this research advances the development of sustainable greenhouse practices that can adapt to the challenges of climate change and resource scarcity. The proposed system represents a significant step toward transforming traditional greenhouse control into more interpretable solutions for modern agriculture.

关键词： AI Artificial Intelligence API application programming interface ARAG Adaptive Retrieval Augment Generation DNN Deep Neural Networks EVT Extreme Value Theorem GPT Generative Pre-Trained Transformers LLM Large Language Models ML Machine Learning NLG Natural Language Generation NLP Natural Language Processing NMPC Nonlinear Model Predictive Control RAG Retrieval Augment Generation Artificial Intelligence Instructional prompting Large language models Model predictive control Natural language generation Retrieval augmented generation

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LLMonFHIR

JACC: Advances

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JACC: Advances 2025年第6期4卷 101780-101780页

作者： Paul Schmiedmayer Adrit Rao Philipp Zagar Lauren Aalami Vishnu Ravi Aydin Zahedivash Dong-han Yao Arash Fereydooni Oliver Aalami Stanford Mussallem Center for Biodesign Stanford University Stanford California USA Department of Pediatrics Stanford University Stanford California USA Department of Emergency Medicine Stanford University Stanford California USA Department of Surgery Stanford University Stanford California USA

Background To improve healthcare quality and empower patients, federal legislation requires nationwide interoperability of electronic health records (EHRs) through Fast Healthcare Interoperability Resources (FHIR) application programming interfaces. Nevertheless, key barriers to patient EHR access—limited functionality, English, and health literacy—persist, impeding equitable access to these benefits. Objectives This study aimed to develop and evaluate a digital health solution to address barriers preventing patient engagement with personal health information, focusing on individuals managing chronic cardiovascular conditions. Methods We present LLMonFHIR, an open-source mobile application that uses large language models (LLMs) to allow users to “interact” with their health records at any degree of complexity, in various languages, and with bidirectional text-to-speech functionality. In a pilot evaluation, physicians assessed LLMonFHIR responses to queries on 6 SyntheticMass FHIR patient datasets, rating accuracy, understandability, and relevance on a 5-point Likert scale. Results A total of 210 LLMonFHIR responses were evaluated by physicians, receiving high median scores for accuracy (5/5), understandability (5/5), and relevance (5/5). Challenges summarizing health conditions and retrieving lab results were noted, with variability in responses and occasional omissions underscoring the need for precise preprocessing of data. Conclusions LLMonFHIR's ability to generate responses in multiple languages and at varying levels of complexity, along with its bidirectional text-to-speech functionality, give it the potential to empower individuals with limited functionality, English, and health literacy to access the benefits of patient-accessible EHRs.

关键词： artificial intelligence digital health large language model literacy mobile application API application programming interface EHR electronic health record FHIR Fast Healthcare Interoperability Resources LLM large language model RAG retrieval augmented generation

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Process Re-Engineering and Data Integration Using Fast Healthcare Interoperability Resources for the Multidisciplinary Treatment of Lung Cancer

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JMIR cancer 2025年 11卷 e53887页

作者： Ching-Hsiung Lin Bing-Yen Wang Sheng-Hao Lin Pei Hsuan Shih Chin-Jing Lee Yung Ting Huang Shih Chieh Chen Mei-Lien Pan Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan. Division of Thoracic Surgery Department of Surgery Changhua Christian Hospital Changhua Taiwan. Department of Information Systems Changhua Christian Hospital Changhua Taiwan. Project Management Office for Intelligence Healthcare Kaohsiung Medical University Chung-Ho Memorial Hospital Kaohsiung Taiwan. Roche Diagnostics Ltd Taiwan Taipei Taiwan. Roche Information Solution APAC Taipei Taiwan. Institute of Hospital and Health Care Administration National Yang Ming Chiao Tung University No. 155 Section 2 Linong Street Beitou District Taipei Taiwan 886 2-28267276.

Multidisciplinary team (MDT) meetings play a critical role in cancer care by fostering collaboration between different health care professionals to develop optimal treatment recommendations. However, meeting scheduling and coordination rely heavily on manual work, making information-sharing and integration challenging. This results in incomplete information, affecting decision-making efficiency and impacting the progress of MDT. This project aimed to optimize and digitize the MDT workflow by interviewing the members of an MDT and implementing an integrated information platform using the Fast Healthcare Interoperability Resources (FHIR) standard. MDT process re-engineering was conducted at a central Taiwan medical center. To digitize the workflow, our hospital adopted the NAVIFY Tumor Board (NTB), a cloud-based platform integrating medical data using international standards, including Logical Object Identifiers, Names, and Codes, Systemized Nomenclature of Medicine-Clinical Terms, M-code, and FHIR. We improved our hospital's information system using application programming interfaces to consolidate data from various systems, excluding sensitive cases. Using FHIR, we aggregated, analyzed, and converted the data for seamless integration. Using a user experience design, we gained insights into the lung cancer MDT's processes and needs. We conducted 2 phases: pre- and post-NTB integration. Ethnographic observations and stakeholder interviews revealed pain points. The affinity diagram method categorizes the pain points during the discussion process, leading to efficient solutions. We divided the observation period into 2 phases: before and after integrating the NTB with the hospital information system. In phase 1, there were 83 steps across the 6 MDT activities, leading to inefficiencies and potential delays in patient care. In phase 2, we streamlined the tumor board process into 33 steps by introducing new functions and optimizing the data entry for pathologists. We conv

关键词： API Fast Healthcare Interoperability Resources HIS ICT MDT application programming interface cancer clinic data integration decision support digital tools digitize eHealth health care health care professionals hospital hospital information system information and communication technology information system lung lung cancer medical data multidisciplinary cancer care multidisciplinary team multidisciplinary team meetings patient care platform process re-engineering treatment tumor board

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Malware Detection Algorithm Based on the Attention Mechanism and ResNet

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Chinese Journal of Electronics 2020年第6期29卷 1054-1060页

作者： WANG Lele WANG Binqiang ZHAO Peipei LIU Ruyi LIU Jiangang MIAO Qiguang Institute of Information Technology Information Engineering University School of Computer Science and Technology Xidian University Nanjing Institute of Information Technology

The cost of misclassifying a malware program as normal is often higher than that of misclassifying a normal program as ***,how to improve the detection accuracy of malware programs is a very important *** paper proposes a deep learning malware program detection algorithm based on attention *** 2 Vec model is used to map the application programming interface(API) into word vectors,and all word vectors of each sample are arranged into a matrix with the same *** this basis,residual network is used to extract features of *** features are input into the attention mechanism to learn the similarity between ***,the features are weighted with the similarity to obtain the new features with better *** new features and the original features are added element by element to obtain the sample features more suitable for ***,samples are classified by *** show that the classification effect of the proposed method is better than that of the traditional machine learning method.

关键词： application program interfaces feature extraction invasive software learning (artificial intelligence) pattern classification text analysis machine learning method API deep learning malware program detection accuracy normal program malware detection algorithm sample features word vectors application programming interface word2Vec model attention mechanism Malware program Word2Vec Residual network structure Self-attention mechanism

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Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators

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Journal of Modern Power Systems and Clean Energy 2023年第2期11卷 401-411页

作者： Tannan Xiao Ying Chen Jianquan Wang Shaowei Huang Weilin Tong Tirui He Department of Electrical Engineering Tsinghua UniversityBeijing 100084China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China Wuxi Power Supply Company of State Grid Wuxi 214125China

With the rapid development of artificial intelligence(AI),it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and *** explore the interaction mechanism of power system dynamic simulations and AI,a general design for AI-oriented power system dynamic simulators is proposed,which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces(APIs).With the support of APIs,simulation-assisted AI and AIassisted simulation form a comprehensive interaction mechanism between power system dynamic simulations and AI.A prototype of this design is implemented and made public based on a highly efficient electromechanical *** of this prototype are carried out in four scenarios including sample generation,AI-based stability prediction,data-driven dynamic component modeling,and AI-aided stability control,which prove the validity,flexibility,and efficiency of the design and implementation for AI-oriented power system dynamic simulators.

关键词： Power system dynamic simulator artificial intelligence application programming interface parallel computing

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Classification with costly features in hierarchical deep sets

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MACHINE LEARNING 2024年第7期113卷 4487-4522页

作者： Janisch, Jaromir Pevny, Tomas Lisy, Viliam Czech Tech Univ Fac Elect Engn Artificial Intelligence Ctr Dept Comp Sci Prague Czech Republic

Classification with costly features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features are sequentially acquired to maximize accuracy while minimizing the acquired features' cost. However, existing approaches can only process data that can be expressed as vectors of fixed length. In real life, the data often possesses rich and complex structure, which can be more precisely described with formats such as XML or JSON. The data is hierarchical and often contains nested lists of objects. In this work, we extend an existing deep reinforcement learning-based algorithm with hierarchical deep sets and hierarchical softmax, so that it can directly process this data. The extended method has greater control over which features it can acquire and, in experiments with seven datasets, we show that this leads to superior performance. To showcase the real usage of the new method, we apply it to a real-life problem of classifying malicious web domains, using an online service.

关键词： Classification with costly features Deep reinforcement learning Deep sets Hierarchical multiple-instance learning Hierarchical softmax Policy decomposition application programming interface Budget Classification Structured data

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Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2018年 16卷 224-230页

作者： Gordon, William J. Catalini, Christian Brigham & Womens Hosp 41 Ave Louis Pasteur 209 Boston MA 02115 USA Massachusetts Gen Hosp 55 Fruit St Boston MA 02114 USA Harvard Med Sch 25 Shattuck St Boston MA 02115 USA MIT Sloan Sch Management 30 Mem Dr Cambridge MA 02142 USA Natl Bur Econ Res 1050 Massachusetts Ave Cambridge MA 02138 USA

Interoperability in healthcare has traditionally been focused around data exchange between business entities, for example, different hospital systems. However, there has been a recent push towards patient-driven interoperability, in which health data exchange is patient-mediated and patient-driven. Patient-centered interoperability, however, brings with it new challenges and requirements around security and privacy, technology, incentives, and governance that must be addressed for this type of data sharing to succeed at scale. In this paper, we look at how blockchain technology might facilitate this transition through five mechanisms: (1) digital access rules, (2) data aggregation, (3) data liquidity, (4) patient identity, and (5) data immutability. We then look at barriers to blockchain-enabled patient-driven interoperability, specifically clinical data transaction volume, privacy and security, patient engagement, and incentives. We conclude by noting that while patient-driving interoperability is an exciting trend in healthcare, given these challenges, it remains to be seen whether blockchain can facilitate the transition from institution-centric to patient-centric data sharing. (c) 2018 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Clinical data Interoperability Data exchange Blockchain application programming interface

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