scholarly journals Agent-Based Simulators for Empowering Patients in Self-Care Programs Using Mobile Agents with Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Swarn Avinash Kumar ◽  
Iván García-Magariño ◽  
Moustafa M. Nasralla ◽  
Shah Nazir
Keyword(s):  
Machine Learning ◽  
Mobile Agents ◽  
Extreme Values ◽  
Self Care ◽  
Care Program ◽  
International Committee ◽  
Ease Of Use ◽  
Agent Based ◽  
Sustainable Systems ◽  
Registration Number

E-health sustainable systems can be optimized by empowering patients in self-care programs through artificial intelligence ecosystems in which both doctors and patients interact in an agile way. This work proposes agent-based simulators as a mechanism for predicting the repercussions of certain self-care programs in certain patients for finding the most appropriate ones. In order to make this easy for both doctors and patients, mobile agents are used to configure an app for each patient, and this app provides the resources to each self-care program. Mobile agents include a machine-learning module for learning which programs are the most appropriate for each patient. This approach is illustrated with two agent-based simulators for respectively reducing negative emotions such as depression and controlling heart rate variability extreme values related to stress. The resulting app was evaluated with a group of users with the Usefulness, Satisfaction and Ease of use (USE) scale and obtained 73% in usefulness, 77% in satisfaction, and 68% in ease of use. This trial is registered with According to the recommendations of the International Committee of Medical Journal Editors (ICMJE), this manuscript states that all experiments have been approved with the ethical committee CEICA from Community of Aragon (Spain) with registration number C.I.PI18/099.

scholarly journals An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

2021 ◽  
Vol 11 (5) ◽  
pp. 2057
Author(s):  
Abdallah Namoun ◽  
Ali Tufail ◽  
Nikolay Mehandjiev ◽  
Ahmed Alrehaili ◽  
Javad Akhlaghinia ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Urban Areas ◽  
Urban Environments ◽  
Ease Of Use ◽  
Transport Infrastructure ◽  
Guidance System ◽  
Planning System ◽  
Flow Density ◽  
Route Guidance ◽  
Agent Based

The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.

Effect of Self - Care Program on the Self- Esteem of Multiple Sclerosis Patients

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
H. Madani ◽  
H. Navipoor ◽  
P. Roozbayani
Keyword(s):  
Multiple Sclerosis ◽  
Self Care ◽  
Care Program ◽  
Self Esteem ◽  
The Self ◽  
Self Report ◽  
The Mean ◽  
Muscular Spasm ◽  
Multiple Sclerosis Patients ◽  
Paired T Test

Aims:According to decreased self- esteem in multiple sclerosis (MS) patients, it is necessary to utilize appropriate methods in order to improve self- esteem in MS patients. So this study was conducted on patients with MS supported by the Iranian MS society for determining the effect of self - care program on their self- esteem.Method:In this semi - experimental study 34 patients with MS who were not in the acute phase of disease were selected. The data were collected via personal questionnaires, problem list, Cooper and smith standard questionnaire for self- esteem and self report check lists. Self - care program(self - care for muscular spasm, fatigue, constipation and amnesia and …) was educated, then it was performed for one month period and the data were analyzed using paired t- test, wilcoxon, croscal - wallis and manwithney tests.Results:Application of self - care program improve the self- esteem and reduced some symptoms such as muscular spasm, fatigue, constipation and amnesia in MS patients. The mean valve of self- esteem increased from 54 before performing the program to 68 after the program ( p < 0.05).Conclusion:Using self-care program can be an effective method for improving self- esteem of MS patients.

scholarly journals Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods Study

2021 ◽  
Vol 45 (4) ◽  
Author(s):  
Stefanie Jauk ◽  
Diether Kramer ◽  
Alexander Avian ◽  
Andrea Berghold ◽  
Werner Leodolter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mixed Methods ◽  
Technology Acceptance ◽  
Health Care Professionals ◽  
User Acceptance ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Actual System ◽  
System Use ◽  
Delirium Management

AbstractEarly identification of patients with life-threatening risks such as delirium is crucial in order to initiate preventive actions as quickly as possible. Despite intense research on machine learning for the prediction of clinical outcomes, the acceptance of the integration of such complex models in clinical routine remains unclear. The aim of this study was to evaluate user acceptance of an already implemented machine learning-based application predicting the risk of delirium for in-patients. We applied a mixed methods design to collect opinions and concerns from health care professionals including physicians and nurses who regularly used the application. The evaluation was framed by the Technology Acceptance Model assessing perceived ease of use, perceived usefulness, actual system use and output quality of the application. Questionnaire results from 47 nurses and physicians as well as qualitative results of four expert group meetings rated the overall usefulness of the delirium prediction positively. For healthcare professionals, the visualization and presented information was understandable, the application was easy to use and the additional information for delirium management was appreciated. The application did not increase their workload, but the actual system use was still low during the pilot study. Our study provides insights into the user acceptance of a machine learning-based application supporting delirium management in hospitals. In order to improve quality and safety in healthcare, computerized decision support should predict actionable events and be highly accepted by users.

Coordinating Human and Machine Learning for Effective Organization Learning

MIS Quarterly ◽  
2021 ◽  
Vol 45 (3) ◽  
pp. 1581-1602
Author(s):  
Timo Sturm ◽  
◽  
Jin Gerlacha ◽  
Luisa Pumplun ◽  
Neda Mesbah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organizational Learning ◽  
Organization Learning ◽  
Agent Based ◽  
Scarce Resources ◽  
Turbulent Environments ◽  
Explorative Learning ◽  
New Ideas ◽  
Effective Organization ◽  
Stock Of Knowledge

With the rise of machine learning (ML), humans are no longer the only ones capable of learning and contributing to an organization’s stock of knowledge. We study how organizations can coordinate human learning and ML in order to learn effectively as a whole. Based on a series of agent-based simulations, we find that, first, ML can reduce an organization’s demand for human explorative learning that is aimed at uncovering new ideas; second, adjustments to ML systems made by humans are largely beneficial, but this effect can diminish or even become harmful under certain conditions; and third, reliance on knowledge created by ML systems can facilitate organizational learning in turbulent environments, but this requires significant investments in the initial setup of these systems as well as adequately coordinating them with humans. These insights contribute to rethinking organizational learning in the presence of ML and can aid organizations in reallocating scarce resources to facilitate organizational learning in practice.

scholarly journals OpSeF: Open source Python framework for collaborative instance segmentation of bioimages

2020 ◽  
Author(s):  
Tobias M. Rasse ◽  
Réka Hollandi ◽  
Péter Horváth
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Complex Analysis ◽  
Ease Of Use ◽  
Problem Definition ◽  
Training Data ◽  
Post Processing ◽  
Gpu Clusters ◽  
User Tasks ◽  
Instance Segmentation

AbstractVarious pre-trained deep learning models for the segmentation of bioimages have been made available as ‘developer-to-end-user’ solutions. They usually require neither knowledge of machine learning nor coding skills, are optimized for ease of use, and deployability on laptops. However, testing these tools individually is tedious and success is uncertain.Here, we present the ‘Op’en ‘Se’gmentation ‘F’ramework (OpSeF), a Python framework for deep learning-based instance segmentation. OpSeF aims at facilitating the collaboration of biomedical users with experienced image analysts. It builds on the analysts’ knowledge in Python, machine learning, and workflow design to solve complex analysis tasks at any scale in a reproducible, well-documented way. OpSeF defines standard inputs and outputs, thereby facilitating modular workflow design and interoperability with other software. Users play an important role in problem definition, quality control, and manual refinement of results. All analyst tasks are optimized for deployment on Linux workstations or GPU clusters, all user tasks may be performed on any laptop in ImageJ.OpSeF semi-automates preprocessing, convolutional neural network (CNN)-based segmentation in 2D or 3D, and post-processing. It facilitates benchmarking of multiple models in parallel. OpSeF streamlines the optimization of parameters for pre- and post-processing such, that an available model may frequently be used without retraining. Even if sufficiently good results are not achievable with this approach, intermediate results can inform the analysts in the selection of the most promising CNN-architecture in which the biomedical user might invest the effort of manually labeling training data.We provide Jupyter notebooks that document sample workflows based on various image collections. Analysts may find these notebooks useful to illustrate common segmentation challenges, as they prepare the advanced user for gradually taking over some of their tasks and completing their projects independently. The notebooks may also be used to explore the analysis options available within OpSeF in an interactive way and to document and share final workflows.Currently, three mechanistically distinct CNN-based segmentation methods, the U-Net implementation used in Cellprofiler 3.0, StarDist, and Cellpose have been integrated within OpSeF. The addition of new networks requires little, the addition of new models requires no coding skills. Thus, OpSeF might soon become both an interactive model repository, in which pre-trained models might be shared, evaluated, and reused with ease.

scholarly journals Mindfulness-based self-care education for healthcare professional students in Japan

2020 ◽  
Vol 7 (1) ◽  
pp. 36
Author(s):  
Yusuke Takamiya ◽  
Shizuma Tsuchiya
Keyword(s):  
Healthcare Professional ◽  
Professional Training ◽  
Self Care ◽  
Care Program ◽  
The Body ◽  
University Education ◽  
High Rate ◽  
First Year ◽  
Professional Students ◽  
Care Education

[Background] Recent studies have consistently shown that medical students experience a high rate of psychological symptoms. In this situation, teaching mindfulness in medical school has the potential to prevent student burnout. However, there are few consistent educational programs in medical schools throughout Japan.[Method] Since 2015, Showa University (Tokyo) has practiced an intensive self-care program based on mindfulness for 600 first-year healthcare professional students in the schools of medicine, dentistry, pharmacy, nursing, and rehabilitation. The target objectives of this program were as follows: understand the needs of self-care, enhance self-awareness, evaluate evidence of mindfulness for mental diseases, and practice formal/informal mindfulness-based activities. This program consisted of a 90-minute lecture, followed by consecutive reflective activities, including completing personal journals and portfolios. The students were required to plan how to make use of what they learned in this course. The students were asked to complete a questionnaire upon completion of the course.[Results] The questionnaire indicated that more than 90% of the students were satisfied with the program, and about 25% started regular mindfulness-based practices such as meditation and breathing methods aimed to reduce test anxiety. Descriptions from the e-portfolio showed that the participants understood evitable stressors and the importance of the body-mind relationship.[Conclusion] Mindfulness-based self-care education can encourage healthcare students to understand the necessity of self-care during the early stages of their professional training. This program for the first year students will be followed by a course on Professionalism for healthcare professional students during their subsequent years of university education.  

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