Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Practice-Centered Approach to Design Cooperative Healthcare Information Systems: Data, Architectural and Organizational Challenges
Get PDFThe healthcare sector is a collaborative environment that requires a joint action for delivering care. Health professionals who work in different organizations or settings must assimilate a massive amount of data generated during the patient care journey. Electronic healthcare records offer a starting point for supporting cooperation among healthcare professionals by saving and sharing traces of the patient's medical acts. However, we claim that these records merely store and share data, which disregards how health professionals use this data to understand the patients' situations and make decisions. We argue that focusing on the cooperative practices of managing patients gives designers new insights to design future healthcare information systems supporting cooperation, and we identify challenges related to this design approach
How to Overcome Lack of Health Record Data and Privacy Obstacles in Initial Phases of Medical Data Analysis Projects
Get PDFThe lack of electronic health record data in general and especially at initial phases of medical research projects is common and is one of the main reasons for delay or failure of such projects. One of the health areas with little attention is the home care area, where patients are being supported by their families or informal caregiver at home. In this paper we present related work on medical data formats and synthetical data generation of medical health records. Furthermore, it presents an approach to generate synthetic electronic health records (HER) that are readily available; suited to research; and free of legal, privacy, security and intellectual property restrictions to be used in home care research projects. We adapted and used Synthea™, an open-source software framework that simulates the lifespans of synthetic patients to generate synthetic EHRs. This paper presents the use case of home care from the capturing of user requirements of home care patients, translating the requirements into a data model, feeding the data model into Synthea™ framework, which produces synthetical health data records mainly as QuestionnaireResponse instance of the Fast Healthcare Interoperability Resources (FHIR) to using these EHRs to build an initial machine learning data model for home care
INCSA-UNET INCSA-UNET: Spatial Attention Inception UNET for Aerial Images Segmentation
No full textBuilding segmentation from aerial images is essential in applications such as facilitating urban planning and estimating the population. Fully convolutional networks (FCNs) and especially UNET have achieved promising results in segmentation problems, after deep learning methods have significantly advanced the performance of many computer vision problems. However, in Convolutional Neural Networks (CNNs) with the standard convolution operations, there are problems such as the overfitting and precise extraction of the boundaries of the objects with different sizes and shapes. In this study, we have used Inception blocks with UNET to enhance feature extraction by implementing two-level Inception approach covering the entire encoding stage. In the proposed architecture, structured form of dropout (DropBlock) is used to prevent overfitting, and spatial/channel attention modules are applied to enhance important features by focusing key areas. We evaluate the proposed INCSA-UNET architecture on publicly available Massachusetts dataset and apply two fold cross-validation experiments for better analyzes. The experimental results show that the proposed architecture does not significantly increase the number of parameters of UNET and has a significant improvement in terms of F1 and Kappa quantitative measures
Research on UBI Auto Insurance Pricing Model Based on Parameter Adaptive SAPSO Optimal Fuzzy Controller
Get PDFAiming at the problem of “dynamic” accurate determination of rates in UBI auto insurance pricing, this paper proposes a UBI auto insurance pricing model based on fuzzy controller and optimizes it with a parameter adaptive SASPO. On the basis of the SASPO algorithm, the movement direction of the particles can be mutated and the direction can be dynamically controlled, the inertia weight value is given by the distance between the particle and the global optimal particle, and the learning factor is calculated according to the change of the fitness value, which realizes the parameter in the running process. Effective self-adjustment. A five-dimensional fuzzy controller is constructed by selecting the monthly driving mileage, the number of violations, and the driving time at night in the UBI auto insurance data. The weights are used to form fuzzy rules, and a variety of algorithms are used to optimize the membership function and fuzzy rules and compare them. The research results show that, compared with other algorithms, the parameter adaptive SAPAO algorithm can calculate more reasonable, accurate and high-quality fuzzy rules and membership functions when processing UBI auto insurance data. The accuracy and robustness of UBI auto insurance rate determination can realize dynamic and accurate determination of UBI auto insurance rates
Internet of Thing Based Confidential Healthcare Data Storage, Access Control and Monitoring Using Blockchain Technique
Get PDFInternet of Things plays a significant role in multiple sectors like agriculture, manufacturing and healthcare for collecting information to automation. The collected information is in different diversity and consists of confidential and non-confidential information. Secure handling of confidential data is a crucial task in cloud computing like storage, access control and monitoring. The blockchain based storage technique provides immutable data storage, efficient access control and dynamic monitoring to confidential data. Thus, the secure internet of things data storage, access control and monitoring using blockchain technique is proposed in this work. The patients health information that are in different formats are pruned by a decision tree algorithm and it classifies the confidential data and non-confidential data by the fuzzy rule classification technique. Depending on data owner's willing, the fuzzy rule is framed and the confidential and non-confidential data collected by internet of things sensors are classified. To provide confidentiality to confidential data, Attribute Based Encryption is applied to confidential data and stored in an off-chain mode of blockchain instead of entire data encryption and storage. The non-confidential data is stored in a plaintext form in cloud storage. When compared to support vector machine, K-nearest neighbor and Naive Bayes classification techniques, the proposed fuzzy rule based confidential data identification produces greater than 96 % of accuracy based on data owner willing and confidential data storage takes lesser than 20 % of storage space and processing time in an entire data storage. Additionally, the blockchain performances like throughput, network scalability and latency is optimized through minimal block size and transactions. Thus, our experimental results show that the proposed blockchain based internet of things data storage, access control and monitoring technique provides better confidentiality and access control to confidential data than the conventional cloud storage technique with lesser processing time
Repairing Process Models with Non-Free-Choice Constructs Based on Token Replay
Get PDFA method of repairing process models with non-free-choice constructs is proposed based on logical Petri nets, aiming at the problem of low precision in the existing repair methods. An extended successor matrix of transitions is determined according to the distance between any two transitions. There are two types of choice-construct transitions. One is a non-free-choice construct transition, and the other is a general choice construct transition. The type of choice-construct transitions can be determined based on the extended successor matrix and the relationship between the front and back sets of transitions. The location of the deviations is calculated by an improved replaying method. Finally, a model can be repaired according to remaining-token places and missing-token places. Based on the experiments on real event logs, the method proposed in this paper has a better performance in fitness, precision, and simplicity compared with its peers
Topic Extraction in Social Networks
Get PDFThe number of Twitter users is increasing and the quantity of produced data is growing. Using this big data to analyze user behavior has become a very active field. The two key challenges of this paper are extracting data from Twitter and extracting topics from user tweets. The proposed approach uses data crawling to collect data from Twitter and a bunch of natural language processing techniques to extract information from the so collected data and build a dataset. Thereafter, we use K-means clustering and Latent Dirichlet Allocation to extract the prevalent topics from this dataset, as they are the most common in the literature. Our proposal is generic, it can be reused by scientists to annotate any text collection
Home Health Care Scheduling Problem Under Uncertainty: Robust Optimization Approaches
Get PDFThis paper deals with the home health care service (HHCS) which is defined as a set of medical, paramedical and social services delivered to patients in their domicile rather than in hospital. To support decision making in HHC, optimization models have been used. However, several of those models are deterministic and do not address the dynamical and uncertainty aspects of the system and variability of some patient data. The HHC scheduling problems are facing more and more complex and specific constraints. These constraints have to be respected, meanwhile the problem objective is optimized under parameters uncertainties. This paper aims to formulate a model that integrated home care scheduling problem while taking into account human aspect -- the behavior of patients, and also another aspect like travel time uncertainty and dynamic behavior of involved medical team and social actors in HHC. Robust approaches are adopted to model and handle this uncertainty
Agent-Based Approach for Connected Vehicles and Smart Road Signs Collaboration
Get PDFRoad traffic is drastically increasing in big cities around the world. In order to enable a flexible management of this traffic, Intelligent Transportation System (ITS) solutions are relying on emergent ubiquitous, mobile, and communication technologies, particularly to intelligently deal with the limited capacities of the existing road infrastructures. While intelligence is left to the autonomous and connected vehicles as well as to the ITS, the road infrastructure has been mostly playing a passive role (as a source of data). Road signage, in particular, are in best cases dynamic but do not play an active role in monitoring traffic and incidents. We propose in this paper to build Smart Road Signs (SRS) that can collaborate with Connected Vehicles in order to monitor traffic and warn drivers about any incident or danger. Our SRSs are meant to operate autonomously in order to detect road traffic problems, share appropriate information with vehicles in the vicinity, and display relevant messages based on the ongoing contextual situation. To meet our goals, we rely on Multi-Agent Systems to design SRSs as proactive components in the ITS landscape. We also rely on agent mobility in order to strengthen the collaboration with the connected vehicles
Secure and Efficient Blockchain Scheme for the Internet of Bikes
Get PDFBlockchain has recently emerged as an auspicious technology for enabling vulnerable data to be exchanged anonymously and securely within Intelligent Transportation System (ITS). Furthermore, Blockchain can be used as an access control mechanism to present a decentralized solution to the distributed authentication problem in the Internet of Bikes (IoB). Although several Blockchain access control mechanisms have been proposed to address the security concerns in IoB, most of them are still vulnerable to some active attacks, especially the cloning attacks. Therefore, this paper proposes a new Trust-Based Access Control Blockchain System (TBACS) to address the cloning attack based on using a secure Trusted Digital Ticket (DGT). The simulations of our solution through the Hyperledger Fabric are showing relevant results in terms of communication overhead and the detection probability of cloning attacks