Cognitive Services Research Articles

Role of neurofilament light chain to reduce diagnosis and improve outcomes for patients in a real‐world cognitive neuropsychiatry service: A retrospective MiND study

AbstractBackgroundIt is common for patients presenting with neuropsychiatric symptoms to endure significant diagnostic odyssey. Neurofilament light chain (NfL) has shown promise in being able to differentiate between neurodegenerative and psychiatric disorders. This study aimed to assess the clinical utility of NfL in avoiding misdiagnosis.MethodIn this retrospective study, we conducted file review of patients assessed at Neuropsychiatry, Royal Melbourne Hospital between 2009 to 2020. We included all patients (n = 212) who had a CSF NfL level at their baseline assessment. Investigators blinded to NfL levels extracted clinical information including diagnoses from the initial and follow‐up assessments using documentation available. In addition, we collected follow‐up data from external services where available. Initial and follow‐up/final diagnoses were categorised as neurodegenerative disorder (ND), psychiatric disorder (PSY), mild cognitive impairment (MCI), unclear, and not‐neurodegenerative (Not‐ND). Not‐ND were individuals who did not meet the diagnosis for PSY or MCI, and did not have worsening symptoms. Based on our previous findings, we defined an NfL>582pg/mL as a positive test result for ND.ResultSo far, we have collected information from 135 patients. Of these, 14/89 (16%) patients initially diagnosed with ND were re‐diagnosed as PSY or Not‐ND. 2/30 (7%) patients initially diagnosed with PSY were re‐diagnosed as having ND. Including MCI and unclear, 32/135 (24%) had a delayed diagnosis, which could have been reduced to 14% based on baseline CSF NfL predicting ND or Not‐ND/MC/PSY. Data analysis is ongoing and full results will be presented at the conference.ConclusionBaseline CSF NfL could have aided clinicians in a real‐world clinical setting, to reduce misdiagnosis, and increase diagnostic accuracy from 76% to 86%. An elevated level could prompt assertive investigations for neurological and neurodegenerative causes. Conversely, a low NfL, could reassure against a neurodegenerative disorder, preventing unnecessary assessments. Timely and accurate diagnosis will improve outcomes and precision care for patients and families.

Optimizing Physician Payment for a Single-Payer Healthcare System.

Current forms of payment of independent physicians in U.S. health care may incentivize more care (fee-for-service) or less care (capitation), be inequitable across specialties (resource-based relative value scale [RBRVS]), and distract from clinical care (value-based payments [VBP]). Alternative systems should be considered as part of health care financing reform. We propose a "Fee-for-Time" approach that would pay independent physicians using an hourly rate based on years of necessary training applied to time for service delivery and documentation. RBRVS overvalues procedures and undervalues cognitive services. VBP shifts insurance risk onto physicians, introducing incentives to game performance metrics and to avoid potentially expensive patients. The administrative requirements of current payment methods introduce large administrative costs and undermine physician motivation and morale. We describe a Fee-for-Time payment scenario. A combination of single-payer financing and payment of independent physicians using the Fee-for-Time proposal would be simpler, more objective, incentive-neutral, fairer, less easily gamed, and less expensive to administer than any system with physician payment based on fee-for-service using RBRVS and VBP.

Deep Learning in Air Traffic Management (ATM): A Survey on Applications, Opportunities, and Open Challenges

Currently, the increasing number of daily flights emphasizes the importance of air transportation. Furthermore, Air Traffic Management (ATM) enables air carriers to operate safely and efficiently through the multiple services provided. Advanced analytic solutions have demonstrated the potential to solve complex problems in several domains, and Deep Learning (DL) has attracted attention due to its impressive results and disruptive capabilities. The adoption of DL models in ATM solutions enables new cognitive services that have never been considered before. The main goal of this research is to present a comprehensive review of state-of-the-art Deep Learning (DL) solutions for Air Traffic Management (ATM). This review focuses on describing applications, identifying opportunities, and highlighting open challenges to foster the evolution of ATM systems. To accomplish this, we discuss the fundamental topics of DL and ATM and categorize the contributions based on different approaches. First, works are grouped based on the DL approach adopted. Then, future directions are identified based on the ATM solution area. Finally, open challenges are listed for both DL applications and ATM solutions. This article aims to support the community by identifying research problems to be faced in the future.

Dampak Konseling Kelompok Teknik Cognitive Restructuring Terhadap Resiliensi Akademik

Students with low academic resilience are influenced by factors of belief in their abilities, the ability to do things well, manage anxiety, and the ability to complete all demands. Based on preliminary studies, It demonstrates that low academic resilience students require assistance in order to become resilient throughout the academic process. Cognitive restructuring methods were used in the group counseling services that were provided as part of the assistance.. The purpose of this study was to examine the impact of cognitive restructuring technique group counseling on the academic resilience of students at SMK IBU Pakusari in the MM major. Respondents who were examined were 372 students with samples taken by 4 students by purposive sampling. Quantitative experiments using a pretest-posttest design with just one group is used in this type of study. This study's methods for gathering data include using a questionnaire. The outcomes demonstrated that the t table's results were: 5.702 > 3.182 at a significance level of 5% (df = 3 n = 4). The findings demonstrated that cognitive restructuring-based group counseling services improved students' academic resilience Keywords: Group Counseling;Cognitive restructuring technique; academic resilience

Elevating community mental health for those with long term conditions: Expanding a transdiagnostic cognitive therapy service from East Berkshire to West Berkshire

Housebound individuals with long term conditions (LTCs) face difficulties accessing services. This is compounded for those also experiencing psychological difficulties. Integrating community and mental services can facilitate access to appropriate services. Transdiagnostic Cognitive Behaviour Therapy (tCBT) is an effective intervention in supporting older individuals with LTCs presenting with physical and psychological difficulties. The Psychological Interventions in Nursing and Community (PINC) West service aimed to replicate a tCBT model developed in East Berkshire. Pre-and-post-intervention outcome measures demonstrated promising results, with reductions in depression and anxiety scores, improvements in quality of life scores and reduced use of NHS services. Suggestions are made for others planning to deliver similar services.

Application analysis of cognitive nursing in thyroid surgery patients.

The aim of the study was to explore the effect of cognitive nursing service on stress response in patients undergoing thyroid tumor surgery. From January 2018 to June 2019, 60 patients with thyroid tumor were selected. The patients were divided into control group and experimental group with 30 cases each. Cognitive nursing was used in the observation group and routine nursing was used in the control group. The scores of SDS and SAS in the observation group were significantly lower than those in the control group (p < 0.05). The nursing satisfaction of the observation group was significantly higher than that of the control group (p < 0.05). The levels of systolic blood pressure, diastolic blood pressure, angiotensin Ⅱ and cortisol in cognitive nursing group were better than those in conventional group (p < 0.05). The incidence of pain and other complications in the cognitive nursing group was lower than that in the conventional group (p < 0.05). After nursing, the anxiety and depression in the study group was (34.1 ± 4.9) and (18.1 ± 5.1) respectively; the anxiety and depression of the control group were (42.8 ± 7.3) and (25.4 ± 5.9) respectively; the anxiety and depression in the study group were significantly lower than those in the control group (p < 0.05). The improvement of diastolic pressure, systolic pressure and heart rate in the study group was better than that in the control group (p < 0.05). The application of cognitive nursing can effectively improve the patients' cognition of disease and treatment, reduce the patients' bad mood, improve the treatment compliance, reduce the occurrence of stress reaction, and improve the safety of anesthesia and operation. Cognitive nursing intervention provides guarantee for patients' prognosis recovery, helps patients recover and discharge as soon as possible, and also has high application value, which is worth promoting and applying in major hospitals.

Assessing preparedness for Alzheimer disease-modifying therapies in Australasian health care systems.

Assessing preparedness for Alzheimer disease-modifying therapies in Australasian health care systems.

Differences in Factors Influencing Deprescribing between Primary Care Providers: Cross-Sectional Study

Deprescribing is a notable approach to improve medication management, but few healthcare systems recognize it. To introduce a new practice, it is important to examine the factors influencing the provision of a new or elaborate cognitive service within the desired setting. This study explores the perceived barriers and facilitators of deprescribing by primary healthcare providers, and identifies the factors associated with a willingness to suggest deprescribing. A cross-sectional survey was conducted (in Croatia, between October 2021 and January 2022) using a validated comprehensive healthcare providers' opinions, preferences, and attitudes towards deprescribing (CHOPPED) questionnaire. A total of 419 pharmacists and 124 physicians participated. Participants showed a high willingness to deprescribe, with significantly higher scores in physicians than in pharmacists (5.00 (interquartile range-IQR 5-5) vs. 4.00 (IQR 4-5), p < 0.001). Pharmacists had significantly higher scores in seven out of ten factors (knowledge, awareness, collaboration facilitators, competencies facilitators, healthcare system facilitators, collaboration barriers, competencies barriers) while in the remaining three factors (patient facilitators, patient and healthcare system barriers) there was no difference in scores. The strongest positive correlation with willingness to suggest deprescribing was found with the collaboration and healthcare system facilitators factors for pharmacists (G = 0.331, p < 0.001, and G = 0.309, p < 0.001, respectively), and with knowledge, awareness, and patient facilitators factors for physicians (G = 0.446, p = 0.001; G = 0.771, p < 0.001; and G = 0.259, p = 0.043, respectively). Primary healthcare providers are willing to suggest deprescribing but face different barriers and facilitators. For pharmacists, the most important facilitators were extrinsic, while for physicians they were more intrinsic and patient related. The stated results provide target areas which one could focus upon to help to engage healthcare providers in deprescribing.

Australian community pharmacy service provision factors, stresses and strains: A qualitative study

IntroductionDespite the desire of pharmacists to provide new and more clinically focused services, strain on the community pharmacist workforce is a known barrier to their service provision. Causes are unclear, although the impact of increased workload, as well as broader role-related and systemic causes have been suggested. AimsTo (1) explore the role strain, stress and systemic factors affecting Australian community pharmacists' provision of cognitive pharmacy services (CPS), using the Community Pharmacist Role Stress Factor Framework (CPRSFF), and (2) adapt the CPRSFF to the local setting. MethodsSemi-structured interviews were conducted with Australian community pharmacists. Transcripts were analysed with the framework method to verify and adapt the CPRSFF. Thematic analysis of particular codes identified personal outcomes and causative patterns in perceived workforce strain. ResultsTwenty-three registered pharmacists across Australia were interviewed. CPS role benefits included: helping people, and increased competency, performance, pharmacy financial return, recognition from the public and other health professionals, and satisfaction. However, strain was worsened by organisational expectations, unsupportive management and insufficient resources. This could result in pharmacist dissatisfaction and turnover in jobs, sector or careers. Two additional factors, workflow and service quality, were added to the framework. One factor, “View of career importance versus partner's career”, was not apparent. ConclusionThe CPRSFF was found to be valuable in exploring the pharmacist role system and analysing workforce strain. Pharmacists weighed up positive and negative outcomes of work tasks, jobs and roles to decide task priority and personal job significance. Supportive pharmacy environments enabled pharmacists to provide CPS, which increased workplace and career embeddedness. However, workplace culture at odds with professional pharmacist values resulted in job dissatisfaction and staff turnover.

이해관계자 반응 분석을 통한 인지·신체 복합 훈련기기 개발 및 서비스 설계방안 도출 : 고령자를 중심으로

본 연구의 목적은 이해관계자의 반응 분석을 통해 ‘고령자 중심 인지•신체 복합 훈련기기 개발 및 서비스 설계방안’을 도출하는 것이다. 이에 따라 훈련기기 측면과 프로그램 측면 그리고 훈련기기의 서비스 측면에서 이해관계자의 반응을 분석하였다. 이를 위해 포커스그룹 인터뷰 방법으로 전문가 5명과의 인터뷰를 시행 후 질적 분석을 시행하였다. 결과는 4개의 범주에서 9개의 하위요인이 도출되었다. 도출된 4개의 범주는 ‘사용자 유형에 대한 정의’, ‘대상자에 적합한 훈련 콘텐츠 개발’, ‘훈련기기의 적합한 활용 방식’, ‘서비스의 적합한 운영 모델 개발’이었고, 9개의 하위항목은 ‘사용자 유형 및 방향 정의’, ‘복합훈련에 효과적인 콘텐츠 구성’, ‘사용자 데이터 기반 개인 맞춤 훈련 제공’, ‘고령자의 인지를 고려한 정보 제공’, ‘지속적 서비스 사용을 위한 흥미 유발 및 동기부여’, ‘효율적 활용이 가능한 훈련기기 선정’, ‘고령자 편의성을 고려한 훈련기기 인터페이스 설계’, ‘사용자 데이터 연계를 통한 서비스 확장’, ‘서비스 접근 및 지속 운영을 위한 방안’이다. 이 연구의 결과를 바탕으로 인지•신체 복합 훈련기기를 개발하고 서비스를 제공한다면, 고령자의 건강을 위해 지역사회 내에서 활용할 수 있을 것이다.

Digital services of regional centers for scientific and technical information in China

The article aims to study the current level of digital services of the regional subsystem of the National System of Scientific and Technical Information of the People's Republic of China and to determine its optimization directions. A content analysis of 28 provincial institutes of scientific and technical information’s official sites was carried out; the most powerful of them were identified in terms of resource and service potential, the level of organization of corporate cooperation based on consolidated digital platforms of multifunctional user service. It is proved that the level of efficiency of digital services of regional scientific and technical information systems directly depends on the level of the province’s economic development and the ability of its government to finance and technologically equip information industry centers activities, and to establish sustainable interaction of all subjects of the information market. Summarizing the results of the content analysis made it possible to identify reserves for improving the service capabilities of the Chinese information industry’s regional clusters, to design vectors for diversifying consulting, expert-analytical, cognitive services of provincial institutes of scientific and technical information, the development of integrated innovation-oriented intelligent service platforms operating based on artificial intelligence technologies.

Importance of telemedicine in mild cognitive impairment and Alzheimer disease patients population during admission to emergency departments with COVID-19.

In March 2020, the World Health Organization declared a global pandemic due to the new coronavirus SARS-CoV-2, and several governments have planned a national quarantine to control the spread of the virus. Acute psychological effects during hospitalization in frail elderly individuals with special needs, such as patients with dementia, have been little studied. The greatest distress manifested by these kinds of patients was isolation from their families during hospitalization. Thus, structured video call interventions were carried out to family caregivers of patients diagnosed with dementia during their hospitalization in the COVID-19 ward. The purpose of this quasi-experimental study was to assess changes in cognitive and behavioral symptoms in both patients and caregivers. All study participants underwent psychological assessments. Specifically, the psychological well-being states of patients and their caregivers were measured at admission (T0) and discharge (T1) using psychometric tests and clinical scales. Each participant received an electronic device to access video calls in addition meetings were scheduled with the psychologist and medical team to keep caregivers updated on the health status of their relatives. A psychological support and cognitive rehabilitation service was also provided. Significant differences were found in all clinical variables of the caregiver group. Results showed a significant relationship in the quality of life score between the patient and caregiver groups. The results of this study has highlighted the importance of maintaining significantly effective relationships during the hospitalization period of patients admitted to COVID wards.

Towards a cognitive engineering of transactional services in IoT based systems

Cognitive computing is the capability of a system to mimic the ability of human brain to learn and adapt from the surroundings. Cognitive systems have decision-making capabilities based on new information, actions and outcomes. Similarly, Internet of Things (IoT) aims at making things smart, and enabling them to perform complex tasks. Reliability and flexibility are persistent challenges in the IoT context where the promise is managing a multitude of devices and delivering real-time responses for critical smart applications. A limited number of studies examine these challenges while considering cognitive capabilities of things and have failed to handle thing’s specificities in terms of communication bandwidth, power availability and storage capacity. Following the service oriented architecture (SOA), the functionality can be encapsulated as services. Thus, automating the management of transactional services in smart IoT ecosystem can be fulfilled through the coupling of transactional properties to cognitive things. This paper provides a comprehensive approach to alleviate reliability restrictions in cognitive IoT service compositions. The concept of cognitive faculty (CF) is introduced to leverage transactional properties of services and can be customized to specific requirements of IoT applications. A proof-of-concept is included in this paper based on a self-monitoring IoT application for diabetic patients.

International Journal of e-Collaboration (IJeC)

In researching cognitive or motor learning aspects of activity control, motor imagery (MI) is a widely used model. Research has shown that motor imagery training can aid in memorizing motor functions because of the functional associations it shares with physical movement. Because of the high level of contact in these sports, players are more likely to sustain finger injuries. As a group of machine learning techniques, cognitive web services are designed to solve AI-related challenges. Because they are modular, cognitive web services can be easily integrated into any program, making AI more accessible to everyone. Some performers return to play early with defensive splinting, taping, and casting depending on the damage and the position played. Other injuries, predominantly in performers necessitating the full use of their hand for their position, require a more extended rehabilitation period and lengthy time away from the field. Therefore, this paper proposes the motor imagery-based finger rehabilitation system (MIFRS) in sports injury rehabilitation.

Cognitive Web Service-Based Learning Analytics in Education Systems Using Big Data Analytics

In the field of education, digital learning plays an important part. For each passing day, digital learning is displacing the traditional method of education. An accurate analysis of a student's qualities improves their academic performance. With the advancement of technology and big data, there are many applications for big data analytics, including education. Huge volumes of academic information are being generated, and discovering a technique to harness and analyze this information effectively is a challenging issue among many educational organizations. In this paper, educational clustering big data mining system (ECBDMS) has been proposed. The cognitive web service based learning analytic(CWS-LA) system is integrated to securely categorize and provide ease of access to the data. ECBDMS has been found to improve performance gains of 92.8%, prediction ratios of 88.6%, clustering error ratios of 2.3 percent, learning percentages of 94%, and forecasting accuracy of 97.1 percent when compared to other existing methods.

Application of Mobile Learning in Higher English Education Systems Using Cognitive Web Services

Students choose from various learning pathways and resources while using mobile learning, which creates a universal learning environment. Machine learning techniques developed to handle AI-related challenges are known as cognitive web services. Adaptive education methods and good knowledge path design can help achieve the goal of studying whenever. In addition, mobile learning devices' display capabilities are becoming a critical determinant of students' attention and time to proficiency. Getting the necessary mobile learning components is now a hot issue. Current mobile learning environments do not include cognitive learning services to be self-adaptive to improvise the standard of the intended service. Student Performance ratio 92.11%, Students' efficiency ratio 89.9, Professional teaching ratio 95.23%, Error rate 43.86%, English learning ratio 93.32%, Interactive ratio 92.5% and Learning flexibility ratio 94.86% are obtained as the experimental results for the suggested method, which proves that the system is more efficient.

Artificial Intelligence-Based English Vocabulary Test Research on Cognitive Web Services Platforms

Automated interaction between agents necessitates the ability of these agents to discover and select from a set of similar (or identical) services. As a result, trust is used to assess the quality of different cognitive web services. Therefore, this paper proposes artificial intelligence-based English vocabulary test research (AI-EVTR) to overcome the student's requirement. Further, pre-test behavior analysis has been introduced to enhance the apps. Before and after the test period, both groups took a pre-test behavior analysis and post-test log analysis “Vocabulary Test in English.” The experimental group used an app-assisted English vocabulary questionnaire to share their points; they were not extremely motivated in the app-assisted approach using machine learning. Statistical approaches comprising independent samples were used to analyze the acquired data. The experimental group greatly improved between the pre-test and post-test in spelling. Language learning on the website can be an option.

Design of Automatic Education Classification Management System in Cognitive Web Services Platforms Using Machine Learning Techniques

Adequate learning resources help students develop their instructional methods, beliefs, attitudes, and general abilities to go beyond a superficial understanding of a subject. It is found that lack of knowledge, inadequate search skills, and lacks of time were all obstacles that hindered library patrons from easily accessing educational resources. It is discovered that patrons of libraries were unable to easily access educational resources because of a lack of knowledge, poor search skills, and a lack of time. Design of automatic education classification management system (D-AECMS) is a proposal in this paper to create and implement cloud-based educational resources and management strategies that support economic development in the classroom. Predictive model-based quality inspection in industrial manufacturing using machine learning techniques and edge cloud computing technology is now possible.

Supply Chain Finance Assistance for Small and Medium-Sized Enterprises Using Cognitive Web Services

When it comes to offering loans to small and medium-sized enterprises, the supply chain finance industries will produce cash flow and commodities (SMEs). The supply management is implemented with cognitive web service. Under the terms of information exchange, a credit risk assessment will be performed for supply chain finance with data analytics. In support vector machine technology, parameters are chosen using a genetic algorithm. To analyze the credit risk of support vector machines, a BP neural network was used to link the evolutionary algorithm with supply chain finance (GA-SVM-BPNN-SCF). Using a genetic algorithm and a support vector machine has overall classification accuracy equal to the BP neural network method. In addition, the role of the supply chain (SC) in mediating the link between SCF adoption, and the importance of supply chain effectiveness (SCE) is discussed. This research helps marketers and professionals better understand how to use SCF in their enterprises to reduce risk and improve SCF by providing data and connecting with financial institutions.

Artificial Intelligence-Enabled Interactive System Modeling for Teaching and Learning Based on Cognitive Web Services

The future of modern education and web-based learning is inherently associated with the advancement in modern technologies and computing capacities of new smart machines, such as artificial intelligence (AI). AI is a high-performance computing environment powered by special processors that use cognitive computing for machine learning and data analytics. There are major challenges in online or web-based learning, such as flexibility, student support, classification of teaching, and learning activities. Hence, this paper proposes smart web-based interactive system modeling (SWISM)based on artificial intelligence for teaching and learning. The paper aimed to categorize learners according to their learning skills and discover how to enable learners with machine learning techniques to have appropriate, quality learning objects. Furthermore, local weight, linear regression, and linear regression methods have been introduced to predict the student learning performance in a cloud platform.