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Health Care Professionals and Data Scientists' Perspectives on a machine learning system to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study

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JOURNAL OF MEDICAL INTERNET RESEARCH 2025年 27卷 e54990页

作者： Seringa, Joana Hirata, Anna Pedro, Ana Rita Santana, Rui Magalhaes, Teresa NOVA Univ Lisbon Comprehens Hlth Res Ctr Publ Hlth Res Ctr NOVA Natl Sch Publ Hlth Ave Padre Cruz P-1600407 Lisbon Portugal NOVA Univ Lisbon NOVA Natl Sch Publ Hlth Lisbon Portugal

Background: Heartfailure (HF) isa significant global health problem, affectingapproximately64.34million peopleworldwide. The worsening of HF, also known as HF decompensation, is a major factor behind hospitalizations, contributing to substantial health care costs related to this condition. Objective: This study aimed to explore the perspectives of health care professionals and data scientists regarding the relevance, challenges, and potential benefits of using machine learning (ML) models to predict decompensation from patients with HF. Methods: A total of 13 individual, semistructured, qualitative interviews were conducted in Portugal between October 31, 2022, and June 23, 2023. Participants represented different health care specialties and were selected from different contexts and regions of the country to ensure a comprehensive understanding of the topic. Data saturation was determined as the point at which no new themes emerged from participants' perspectives, ensuring a sufficient sample size for analysis. The interviews were audio recorded, transcribed, and analyzed using MAXQDA (VERBI Software GmbH) through a reflexive thematic analysis. Two researchers (JS and AH) coded the interviews to ensure the consistency of the codes. Ethical approval was granted by the NOVA National School of Public Health ethics committee (CEENSP 14/2022), and informed consent was obtained from all participants. Results: Theparticipantsrecognizedthepotential benefitsof ML modelsforearlydetection, riskstratification, and personalized care of patients with HF. The importance of selecting appropriate variables for model development, such as rapid weight gain and symptoms, was emphasized. The use of wearables for recording vital signs was considered necessary, although challenges related to adoption among older patients were identified. Risk stratification emerged as a crucial aspect, with the model needing to identify patients at high-, medium-, and low-risk levels. Participants emphasi

关键词： heart failure machine learning system decompensation qualitative research cardiovascular diseases heart failure management interview

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Maintaining Performance of a machine learning system Against Imperfect Retraining 48

Maintaining Performance of a Machine Learning System Against...

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48th Annual IEEE International Computers, Software, and Applications Conference (COMPSAC) - Digital Development for a Better Future

作者： Wang, Zhengji Machida, Fumio Univ Tsukuba Dept Comp Sci Tsukuba Ibaraki Japan

ISBN: (纸本)9798350376975;9798350376968

machine learning systems (MLS) often consist of diverse machine learning models to attain demanding and complex objectives. Despite their advanced functionalities, MLSs encounter the inevitable challenge of performance deterioration caused by distribution changes often referred to as dataset shift. When models encountering dataset shift, retraining machine learning model with new datasets is essential to restore the performance. However, model retraining is not always perfect due to component entanglement, resulting in failures to maintain the required level of performance. To address this issue, this paper investigates the impact of imperfect retraining on the overall performance of MLSs, and accordingly propose two maintenance policies, progressive and conservative retraining policies. We consider an MLS consisting of two sequentially-dependent machine learning models and develop continuous-time Markov chains capturing the dynamics of performance degradation and retraining of machine learning models. The results of parametric sensitivity analysis demonstrate that the progressive retraining policy and conservative retraining policy could provide higher service availability in different conditions.

关键词： Markov chain dataset shift machine learning system retraining service availability

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machine learning system to assess rice crop change detection from satellite-derived RGVI due to tropical cyclones using remote sensing dataset

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REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2024年 35卷

作者： Shamsuzzoha, Md Shaw, Rajib Ahamed, Tofael Univ Tsukuba Grad Sch Life & Environm Sci Tennodai 1-1-1 Tsukuba 3058572 Japan Patuakhali Sci & Technol Univ Fac Environm Sci & Disaster Management Dept Emergency Management Dumki 8602 Patuakhali Bangladesh Keio Univ Grad Sch Media & Governance Endo 5322 Fujisawa 2520882 Japan Univ Tsukuba Inst Life & Environm Sci Tennodai 1-1-1 Tsukuba 3058572 Japan

Tropical cyclones strike massive devastation of agricultural crops in South Asian countries practically every year. For assessing impacts on primary land use and land cover (LULC) classes of Patuakhali district in Bangladesh, this study utilized the Landsat-8 image dataset to compute change detection indices of a consistent cyclone landfall period in the late rainy season (October and November). Different stage of cyclones based on their wind speed (km/h) were also analyzed to explore agricultural crop change detection due to the tropical cyclone (TC) Bulbul (made landfall on November 9th, 2019). Three years later, cyclone Sitrang made landfall on October 24th, 2022, and was employed for the test results that the Bulbul's rice growth vegetation index (RGVI)-CD trained in the study's machine learning (ML) system. Therefore, the objective of this study was to determine rice crop changes from RGVI due to a cyclone (Bulbul) to evaluate growth change status by another cyclone (Sitrang) using machine learning system. In Patuakhali district, the rice crops were damaged mainly and affected in classification as moderately changed detected (MCD) 26.07%, very changed detected (VCD) 48.83%, and extremely changed detected (ECD) 17.73% in total agricultural lands area (1548.93 km2). It was also significantly monitored for the rice crops to change as rice growth vegetation index (RGVI)-CD related to cyclone wind speed (km/h), DEM, distance from the rivers, and shorelines. From the result, the symmetric regression of coefficient (R2) of (i) linear regression, (ii) fine tree, (iii) support vector machine, (iv) ensemble boosted trees, and (v) trilayered neural network were obtained 0.735, 0.734, 0.737, 0.756 and 0.736 as good-fitted test outcomes respectively. Applying the five machine learning models that were created from cyclone Sitrang, it was discovered that the test set in this system was accurate to the observed forecast with acceptable RMSE and MAE values. Therefore, study ca

关键词： Agricultural lands Change detection machine learning system Satellite remote sensing and GIS Rice crop Rice growth vegetation index Tropical cyclones

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Security Threats and Defensive Approaches in machine learning system Under Big Data Environment

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WIRELESS PERSONAL COMMUNICATIONS 2021年第4期117卷 3505-3525页

作者： Chen Hongsong Zhang Yongpeng Cao Yongrui Bhargava, Bharat Univ Sci & Technol Beijing USTB Dept Comp Sci Beijing 100083 Peoples R China Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China

Under big data environment, machine learning has been rapidly developed and widely used. It has been successfully applied in computer vision, natural language processing, computer security and other application fields. However, there are many security problems in machine learning under big data environment. For example, attackers can add "poisoned" sample to the data source, and big data process system will process these "poisoned" sample and use machine learning methods to train model, which will directly lead to wrong prediction results. In this paper, machine learning system and machine learning pipeline are proposed. The security problems that maybe occur in each stage of machine learning system under big data processing pipeline are analyzed comprehensively. We use four different attack methods to compare the attack experimental *** security problems are classified comprehensively, and the defense approaches to each security problem are analyzed. Drone-deploy MapEngine is selected as a case study, we analyze the security threats and defense approaches in the Drone-Cloud machine learning application envirolment. At last,the future development drections of security issues and challenages in the machine learning system are proposed.

关键词： machine learning system Big data pipeline Security threats Defensive approaches Case study

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Lessons Learned from Applying Requirements and Design Techniques in the Development of a machine learning system for Predicting Lawsuits Against Power Companies

Lessons Learned from Applying Requirements and Design Techni...

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Human Interface and the Management of Information (HIMI) part of International Conference on Human-Computer Interaction (HCII)

作者： Rivero, Luis Portela, Carlos Boaro, Jose Santos, Pedro Rego, Venicius Braz, Geraldo, Jr. Paiva, Anselmo Alves, Erika Oliveira, Milton Moraes, Renato Mendes, Marina Univ Fed Maranhao Programa Posgrad Ciencia Comp Sao Luis Maranhao Brazil Univ Fed Maranhao Dept Informat Sao Luis Maranhao Brazil Equatorial Energia SA Sao Luis Maranhao Brazil

ISBN: (纸本)9783030783204;9783030783211

machine learning (ML) has shown great potential for automating several aspects of everyday life and business. Experiences reports on developing ML systems, however, focus mainly on how to install, configure and maintain an ML system, but do not focus on the requirements engineering process and the design of the user interface that will present the data to the user. In this paper, we report the lessons learned from applying different requirements and design methods in one of the stages of a large development project (18 months). Its goal was to develop and evaluate aWeb based application embedding an ML model that would produce data on the probability of lawsuits based on features such as: number of complaints, electrical damage to appliances and power shutdowns. At all, the design team applied the following techniques: (a) Interviews and document analysis, to identify the major reasons for filling a lawsuit;(b) Personas, to define which type of clients could file a lawsuit;(c) Scenarios, to define which interaction (conversations) would be automatically triggered by a chatbot to try to solve client problems, avoiding a lawsuit;and (d) Prototype evaluation, to define the interaction and type of data that would be available to the lawyers through aWeb application. Through the lessons learned within this paper and by providing details for its replication, we intend to encourage software companies to combine requirements and design approaches for cost-effective user centered design, especially in decision support intelligent systems.

关键词： Decision support systems machine learning system Power company Combined design approaches

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Using Diversities to Model the Reliability of Two-Version machine learning systems

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2024年第3期12卷 810-825页

作者： Machida, Fumio Univ Tsukuba Dept Comp Sci Tsukuba Ibaraki 3058577 Japan

The N-version machine learning system (MLS) is an architectural approach to reduce error outputs from a system by redundant configuration using multiple machine learning (ML) modules. Improved system reliability achieved by N-version MLSs inherently depends on how diverse ML models are employed and how diverse input data sets are given. However, neither error input spaces of individual ML models nor input data distributions are obtainable in practice, which is a fundamental barrier to understanding the reliability improvement by N-version architectures. In this paper, we introduce two diversity measures quantifying the similarities of ML models' capabilities and the interdependence of input data sets causing errors, respectively. The defined measures are used to formulate the reliability of an elemental N-version MLS called dependent double-modules double-inputs MLS. The system is assumed to fail when two ML modules output errors simultaneously for the same classification task. The reliabilities of different architecture options for this MLS are comprehensively analyzed through a compact matrix representation form of the proposed reliability model. The theoretical analysis and numerical results show that the architecture exploiting two diversities achieves preferable reliability under reasonable assumptions. Intuitive relations between diversity parameters and architecture reliabilities are also demonstrated through numerical examples.

关键词： Reliability Computer architecture Data models Software reliability Sensors Reliability engineering Numerical models Diversity machine learning system redundant architecture reliability software fault-tolerance

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Empirical Architecture Comparison of Two-input machine learning systems for Vision Tasks

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FORMAL ASPECTS OF COMPUTING 2024年第4期36卷 1-19页

作者： Wakigami, Kazuya Machida, Fumio Phung-duc, Tuan Univ Tsukuba Dept Comp Sci Tsukuba Ibaraki Japan Univ Tsukuba Dept Policy & Planning Sci Tsukuba Ibaraki Japan

As machine learning models have been deployed in many vision systems, including autonomous vehicles and robots, designing architectures for machine learning systems (MLSs) has emerged as a critical concern. Previous studies have shown that enhancing the reliability of MLS outputs can be achieved by comparing multiple inference results on distinct inputs. Nevertheless, the architectures facilitating multiple inferences incur non-negligible performance overhead and energy consumption that have been less investigated. This article delves into the trade-offs among reliability, performance, and energy efficiency of architectures for two-input MLSs through real experiments conducted on image classification and object detection tasks. Specifically, we scrutinize the comparison between parallel- and shared-type architectures of two-input MLSs for vision tasks. The experiments confirm that the shared-type architecture can achieve a shorter response time and smaller energy consumption by using a shared machine learning module for both image classification and object detection tasks. However, the parallel-type architecture can benefit the redundant machine learning modules for improving throughput and fault tolerance. Our empirical results also show the service time distributions of image classification and object detection tasks fit well with a log-normal distribution and a mixture of the Gaussian model, respectively.

关键词： Energy consumption machine learning system performance reliability simulation

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Dealing with Explainability Requirements for machine learning systems 47

Dealing with Explainability Requirements for Machine Learnin...

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47th IEEE-Computer-Society Annual International Conference on Computers, Software, and Applications (COMPSAC)

作者： Li, Tong Han, Lu Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9798350326970

Explainability has recently been recognized as an increasingly important quality requirement for machine learning systems. Various methods have been proposed by machine learning researchers to explain the results of machine learning techniques. However, analyzing and operationalizing such explainability requirements is knowledge-intensive and timeconsuming. This paper proposes an explainability requirements analysis framework using contextual goal models, aiming at systematically and automatically deriving appropriate explainability methods. Specifically, we comprehensively survey and analyze existing explainability methods, associating them with explainability requirements and emphasizing the context for applying them. In such a way, we can automatically operationalize explainability requirements into concrete explainability methods. We conducted a case study with ten participants to evaluate our proposal. The results illustrate the framework's usability for satisfying the explainability requirements of machine learning systems.

关键词： Explainability requirements Contextual goal model machine learning system

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Bayesian Network Reliability Modeling for Three-version machine learning systems 53

Bayesian Network Reliability Modeling for Three-version Mach...

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53rd Annual IEEE/IFIP International Conference on Dependable systems and Networks (DSN)

作者： Wen, Qiang Machida, Fumio Univ Tsukuba Dept Comp Sci Tsukuba Ibaraki Japan

ISBN: (纸本)9798350325454

machine learning (ML) is widely used in intelligent software systems. However, the uncertain outputs from ML models can lead to undesirable consequences in safety-critical applications. To improve system reliability, we propose N-version ML architectures combining multiple inputs with multiple ML models to decide the system output by voting. The reliability of N-version ML systems can be characterized by two diversity measures;input diversity and model diversity. In this study, we consider Bayesian Networks (BNs) for modeling the reliability of N-version ML systems outputs through multiple dependent diversity parameters. We present a preliminary BNs reliability model for a three-version ML system. Finally, we discuss the potential extension of the approach and issues for modeling large-scale systems.

关键词： diversity Bayesian networks machine learning system N-version programming reliability

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Reliability modeling for three-version machine learning systems through Bayesian networks

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RELIABILITY ENGINEERING & system SAFETY 2025年 260卷

作者： Wen, Qiang Machida, Fumio Univ Tsukuba Dept Comp Sci 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan

machine learning (ML) is extensively employed in AI-powered systems including safety-critical applications such as autonomous vehicles. The outputs from ML models are sensitive to real-world input data and error-prone, thereby improving the reliability of ML systems' outputs has become a critical challenge in ML system design. In this paper, we introduce N-version ML architectures to enhance the ML system reliability and propose Bayesian Networks (BNs) models to evaluate the reliability of system outputs targeting three-version ML systems. The proposed BN reliability models allow us to formulate five distinct types of three-version ML architectures that are composed of diverse models and diverse input data sources. To validate the BN reliability models with real samples from ML systems, we conduct empirical studies on traffic sign recognition tasks and evaluate prediction performance. As a result, we find the prediction residuals between the observed reliability and the predicted reliability by the BN reliability models are less than 0.015 across all data sets, which is much better than the prediction performance by the baseline model. In addition, in comparison to the previous reliability models without exploiting BNs, the proposed models exhibit an advantage in reliability prediction, except for the triple model with single input architecture.

关键词： Bayesian networks Diversity machine learning system N-version programming Reliability

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