Machine Learning Methods with Decision Forests for Parkinson's Detection

Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson's Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve maximum detection accuracy with the lowest error rate. This article examines two recent decision forest algorithms Systematically Developed Forest (SysFor), and Decision Forest by Penalizing Attributes (ForestPA) along with the popular Random Forest to design three distinct Parkinson's detection schemes with optimum number of decision trees. The proposed approach undertakes minimum number of decision trees to achieve maximum detection accuracy. The training and testing samples and the density of trees in the forest are kept dynamic and incremental to achieve the decision forests with maximum capability for detecting Parkinson's Disease (PD). The incremental tree densities with dynamic training and testing of decision forests proved to be a better approach for detection of PD. The proposed approaches are examined along with other state-of-the-art classifiers including the modern deep learning techniques to observe the detection capability. The article also provides a guideline to generate ideal training and testing split of two modern acoustic datasets of Parkinson's and control subjects donated by the Department of Neurology in Cerrahpaşa, Istanbul and Departamento de Matemáticas, Universidad de Extremadura, Cáceres, Spain. Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson's disease detector with a little number of decision trees in the forest to score the highest detection accuracy of 94.12% to 95.00%

Role of digital health in coordinating patient care in a hub-and-spoke hierarchy of cancer care facilities : a scoping review

Background: Coordinating cancer care is complicated due to the involvement of multiple service providers which often leads to fragmentation. The evolution of digital health has led to the development of technology-enabled models of healthcare delivery. This scoping review provides a comprehensive summary of the use of digital health in coordinating cancer care via hub-and-spoke models. Methods: A scoping review of the literature was undertaken using the framework developed by Arksey and O’Malley. Research articles published between 2010 and 2022 were retrieved from four electronic databases (PubMed/MEDLINE, Web of Sciences, Cochrane Reviews and Global Health Library). The preferred reporting items for systematic reviews and meta-analyses extension for the scoping reviews (PRISMA-ScR) checklist were followed to present the findings. Result: In total, 311 articles were found of which 7 studies that met the inclusion criteria were included. The use of videoconferencing was predominant across all the studies. The number of spokes varied across the studies ranging from 1 to 63. Three studies aimed to evaluate the impact on access to cancer care among patients, two studies were related to capacity building of the health care workers at the spoke sites, one study was based on a peer review of radiotherapy plans, and one study was related to risk assessment and patient navigation. The introduction of digital health led to reduced travel time and waiting period for patients, and standardisation of radiotherapy plans at spokes. Tele-mentoring intervention aimed at capacity-building resulted in higher confidence and increased knowledge among the spoke learners. Conclusion: There is limited evidence for the role of digital health in the hub-and-spoke design. Although all the studies have highlighted the digital components being used to coordinate care, the bottlenecks, Which were overcome during the implementation of the interventions and the impact on cancer outcomes, need to be rigorously analysed

Agroforestry Systems for Soil Health Improvement and Maintenance

Agroforestry integrates woody perennials with arable crops, livestock, or fodder in the same piece of land, promoting the more efficient utilization of resources as compared to monocropping via the structural and functional diversification of components. This integration of trees provides various soil-related ecological services such as fertility enhancements and improvements in soil physical, biological, and chemical properties, along with food, wood, and fodder. By providing a particular habitat, refugia for epigenic organisms, microclimate heterogeneity, buffering action, soil moisture, and humidity, agroforestry can enhance biodiversity more than monocropping. Various studies confirmed the internal restoration potential of agroforestry. Agroforestry reduces runoff, intercepts rainfall, and binds soil particles together, helping in erosion control. This trade-off between various non-cash ecological services and crop production is not a serious constraint in the integration of trees on the farmland and also provides other important co-benefits for practitioners. Tree-based systems increase livelihoods, yields, and resilience in agriculture, thereby ensuring nutrition and food security. Agroforestry can be a cost-effective and climate-smart farming practice, which will help to cope with the climate-related extremities of dryland areas cultivated by smallholders through diversifying food, improving and protecting soil, and reducing wind erosion. This review highlighted the role of agroforestry in soil improvements, microclimate amelioration, and improvements in productivity through agroforestry, particularly in semi-arid and degraded areas under careful consideration of management practices

A distributed cancer care model with a technology-driven hub-and-spoke and further spoke hierarchy : findings from a pilot implementation programme in Kerala, India

Background: The technology enabled distributed model in Kerala is based on an innovative partnership model between Karkinos Healthcare and private health centers. The model is designed to address the barriers to cancer screening by generating demand and by bringing together the private health centers and service providers at various levels to create a network for continued care. This paper describes the implementation process and presents some preliminary findings. Methods: The model follows the hub-and-spoke and further spoke framework. In the pilot phases, from July 2021 to December 2021, five private health centers (partners) collaborated with Karkinos Healthcare across two districts in Kerala. Screening camps were organized across the districts at the community level where the target groups were administered a risk assessment questionnaire followed by screening tests at the spoke hospitals based on a defined clinical protocol. The screened positive patients were examined further for confirmatory diagnosis at the spoke centers. Patients requiring chemotherapy or minor surgeries were treated at the spokes. For radiation therapy and complex surgeries the patients were referred to the hubs. Results: A total of 2,459 individuals were screened for cancer at the spokes and 299 were screened positive. Capacity was built at the spokes for cancer surgery and chemotherapy. A total of 189 chemotherapy sessions and 17 surgeries were performed at the spokes for cancer patients. 70 patients were referred to the hub. Conclusion: Initial results demonstrate the ability of the technology Distributed Cancer Care Network (DCCN) system to successfully screen and detect cancer and to converge the actions of various private health facilities towards providing a continuum of cancer care. The lessons learnt from this study will be useful for replicating the process in other States

Global Variation of Nutritional Status in Children Undergoing Chronic Peritoneal Dialysis : A Longitudinal Study of the International Pediatric Peritoneal Dialysis Network

While children approaching end-stage kidney disease (ESKD) are considered at risk of uremic anorexia and underweight they are also exposed to the global obesity epidemic. We sought to investigate the variation of nutritional status in children undergoing chronic peritoneal dialysis (CPD) around the globe. The distribution and course of body mass index (BMI) standard deviation score over time was examined prospectively in 1001 children and adolescents from 35 countries starting CPD who were followed in the International Pediatric PD Network (IPPN) Registry. The overall prevalence of underweight, and overweight/obesity at start of CPD was 8.9% and 19.7%, respectively. Underweight was most prevalent in South and Southeast Asia (20%), Central Europe (16.7%) and Turkey (15.2%), whereas overweight and obesity were most common in the Middle East (40%) and the US (33%). BMI SDS at PD initiation was associated positively with current eGFR and gastrostomy feeding prior to PD start. Over the course of PD BMI SDS tended to increase on CPD in underweight and normal weight children, whereas it decreased in initially overweight patients. In infancy, mortality risk was amplified by obesity, whereas in older children mortality was markedly increased in association with underweight. Both underweight and overweight are prevalent in pediatric ESKD, with the prevalence varying across the globe. Late dialysis start is associated with underweight, while enteral feeding can lead to obesity. Nutritional abnormalities tend to attenuate with time on dialysis. Mortality risk appears increased with obesity in infants and with underweight in older children.Peer reviewe

Open Access Social Science Journals Indexed in DOAJ: A Critical Analysis

The primary purpose of this study is to provide a comprehensive view of social science open access journals indexed in the Directory of Open Access Journals (DOAJ). DOAJ is the most recognized and most authoritative list of scholarly open access social science journals indexed in DOAJ.Data is based on open access social science journals indexed on Directory of Open Access Journals (DOAJ). A total of 955 social science journals were selected from DOAJ database available online at http://www.doaj.org. This study found Indonesia is the highest productive country; 2012-16 is most productive year and most of the journals published under CC-BY license from last ten years

A Scientometrics Study of Research Productivity of VSS University of Technology(VSSUT) as Reflected in Scopus Database during 2015-2020

In this study we aims to measure and evaluate the quantitative growth of research productivity of VSS University of Technology, Odisha using scientometrics parameters such as annual growth rate, author productivity, institution-wide collaboration, and collaboration pattern. The research publications data of VSSUT were retrieved from Scopus from 2015 to 2020. The total publications of 1889 sample datasets are used for this study. The maximum annual growth rate of 85.91 is recorded in 2018, whereas negative value of AGR of -8.5 is recorded in the year 2016. Engineering is the top productive subject domain and S. Panda found to be the most prominent author in this given period with 121 publications. The researcher of VSSUT mostly preferred to published their research work in the form of articles (733 with 51.14%) and followed by conference paper (556 with 39.04%), Book chapter (59 with 4.14%) papers. It has been found that for VSSUT the domestic collaboration is high and there is lack of international collaboration observed. Cluster analysis of key terms indicates that VSSUT research are mainly concentrating in the core areas of control systems, particle swarm optimization, neural network, data mining and artificial intelligence. Significant amount of research were also carried out focusing on microwaves and signal processing & cloud computing. The National Institute of Technology, Rourkela found to be the top collaborating institution while USA found to be the top collaborating country of VSSUT

Machine Learning Methods with Decision Forests for Parkinson’s Detection

Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson’s Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve maximum detection accuracy with the lowest error rate. This article examines two recent decision forest algorithms Systematically Developed Forest (SysFor), and Decision Forest by Penalizing Attributes (ForestPA) along with the popular Random Forest to design three distinct Parkinson’s detection schemes with optimum number of decision trees. The proposed approach undertakes minimum number of decision trees to achieve maximum detection accuracy. The training and testing samples and the density of trees in the forest are kept dynamic and incremental to achieve the decision forests with maximum capability for detecting Parkinson’s Disease (PD). The incremental tree densities with dynamic training and testing of decision forests proved to be a better approach for detection of PD. The proposed approaches are examined along with other state-of-the-art classifiers including the modern deep learning techniques to observe the detection capability. The article also provides a guideline to generate ideal training and testing split of two modern acoustic datasets of Parkinson’s and control subjects donated by the Department of Neurology in Cerrahpaşa, Istanbul and Departamento de Matemáticas, Universidad de Extremadura, Cáceres, Spain. Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson’s disease detector with a little number of decision trees in the forest to score the highest detection accuracy of 94.12% to 95.00%