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General General

Prediction of the production of crops with respect to rainfall.

In Environmental research ; h5-index 67.0

Agriculture is one of the most important sectors in the Indian context. It is one of the highest employing sectors in the Indian scenario. Unlike other sectors agriculture is highly dependent on the quality and the quantity of both the external factors like rainfall, climate, pH of the soil, fertilizers and insecticides used, and internal factors like the quality of seeds. This paper predicts the production of crops as a function of rainfall for four Indian States. This knowledge can be implemented in generating a rough overview of how the production is based on rainfall and how much can a specific crop production for the amount of rainfall it receives. Two crops each belonging to four different states are chosen and the best regression model for the crop of the state is chosen. There is no research done solely on how rainfall affects crops of particular states. The proposed method of evaluation is better than other existing methods of evaluation as it evaluates all the regression techniques(Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Random Forest, and XGBRegression) for two crops of four individual states. For balanced evaluation, two states of North India and two states of South India are selected. The regression techniques are evaluated based on their Mean Squared Error.

Antony Benny

2021-Jul-05

Machine learning, Regression, Rice, Wheat

General General

MYPATH: A novel mindfulness and yoga-based peer leader intervention to prevent violence among youth experiencing homelessness.

In Journal of community psychology

Young adults experiencing homelessness (YAEH) are at elevated risk for violence victimization and perpetration. However, there are no evidence-based violence prevention interventions for homeless populations. This study is an evaluation of a novel mindfulness-based peer-leader intervention designed to reduce violence and improve mindfulness in YAEH. A social network of YAEH receiving services at a drop-in agency was recruited in Summer 2018 (n = 106) and peer-leaders identified at baseline (n = 12). Peer leaders were trained in mindfulness and yoga skills during a 1-day intensive workshop and seven 1-h weekly follow-up workshops and encouraged to share their knowledge with in-network peers. Postintervention data were collected 2 and 3 months after baseline. Two one-way repeated-measures analyses of variance (ANOVAs) tested differences in means for mindfulness and fighting. ANOVA models showed significant increases in group mean mindfulness F(2, 110) = 3.42, p < 0.05 and significant decreases in group mean violent behavior F(2, 112) = 5.23, p < 0.01 at the network level. Findings indicate a network-based, peer-leader model can be effective for influencing complex, socially conditioned attitudes and behaviors among YAEH. Additional advantages of the peer-leader model include relatively few direct-service person-hours required from providers and convenience to participants able practice skills in their relevant social contexts.

Barr Nicholas, Petering Robin, Onasch-Vera Laura, Thompson Nichole, Polsky Ryan

2021-Jul-08

homelessness, mindfulness, social networks, violence

oncology Oncology

A novel systematic approach for cancer treatment prognosis and its applications in oropharyngeal cancer with microRNA biomarkers.

In Bioinformatics (Oxford, England)

MOTIVATION : Predicting early in treatment whether a tumor is likely to respond to treatment is one of the most difficult yet important tasks in providing personalized cancer care. Most oropharyngeal squamous cell carcinoma (OPSCC) patients receive standard cancer therapy. However, the treatment outcomes vary significantly and are difficult to predict. Multiple studies indicate that microRNAs (miRNAs) are promising cancer biomarkers for the prognosis of oropharyngeal cancer. The reliable and efficient use of miRNAs for patient stratification and treatment outcome prognosis is still a very challenging task, mainly due to the relatively high dimensionality of miRNAs compared to the small number of observation sets; the redundancy, irrelevancy and uncertainty in the large amount of miRNAs; and the imbalanced observation patient samples.

RESULTS : In this study, a new machine learning-based prognosis model was proposed to stratify subsets of OPSCC patients with low and high risks for treatment failure. The model cascaded a two-stage prognostic biomarker selection method and an evidential K-nearest neighbors (EK-NN) classifier to address the challenges and improve the accuracy of patient stratification. The model has been evaluated on miRNA expression profiling of 150 oropharyngeal tumors by use of overall survival and disease-specific survival as the end points of disease treatment outcomes, respectively. The proposed method showed superior performance compared to other advanced machine-learning methods in terms of common performance quantification metrics. The proposed prognosis model can be employed as a supporting tool to identify patients who are likely to fail standard therapy and potentially benefit from alternative targeted treatments.

He Shenghua, Lian Chunfeng, Thorstad Wade, Gay Hiram, Zhao Yujie, Ruan Su, Wang Xiaowei, Li Hua

2021-Apr-29

Pathology Pathology

Toward Artificial Intelligence-Driven Pathology Assessment for Hematologic Malignancies.

In Cancer discovery ; h5-index 105.0

In this issue of Blood Cancer Discovery, Brück and colleagues applied unsupervised and supervised machine learning to bone marrow histopathology images from patients with myelodysplastic syndrome (MDS). Their study provides new insights into the pathobiology of MDS and paves the way for increased use of artificial intelligence for the assessment and diagnosis of hematologic malignancies.See related article by Brück et al., p. 238.

Elemento Olivier

2021-May

General General

Patient perceptions on data sharing and applying artificial intelligence to healthcare data: a cross sectional survey.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Considerable research is being conducted as to how artificial intelligence (AI) can be effectively applied to healthcare. However, for it to be successful, large amounts of health data are required for the training and testing of algorithms. As such, there is a need to understand the perspectives and viewpoints of patients regarding the use of their health data in AI research.

OBJECTIVE : To survey a large sample of patients to identify current awareness of health data research, opinions and views on data sharing for the purposes of AI research, and viewpoints on the use of AI technology on healthcare data.

METHODS : A cross-sectional survey with patients was conducted at a large multi-site teaching hospital in the United Kingdom. Data were collected on patient and public views about sharing health data for research and the use of AI on health data.

RESULTS : A total of 408 participants completed the survey. Respondents had low levels of prior knowledge of AI in general. Most were comfortable with sharing health data with the NHS (77.9%) or universities (65.7%), but far fewer with commercial organisations such as technology companies (26.4%). The majority endorsed AI research on healthcare data (87.4%) and healthcare imaging (86.4%) in a university setting, providing that concerns about privacy, re-identification of anonymised health care data and consent processes were addressed.

CONCLUSIONS : There is significant variation in patient perception, levels of support, and understanding of health data research and AI. There is a need for greater public engagement and debate to ensure the acceptability of AI research and its successful integration into clinical practice in the future.

CLINICALTRIAL :

Aggarwal Ravi, Farag Soma, Martin Guy, Ashrafian Hutan, Darzi Ara

2021-Jul-05

Ophthalmology Ophthalmology

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection During the COVID-19 Pandemic: A Systematic Review and Bibliometric Analysis.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Glaucoma leads to irreversible blindness. Globally, it is the second most common retinal disease that leads to blindness, slightly less common than cataracts. Therefore, there is a great need to avoid the silent growth of this disease using recently developed generative adversarial networks (GANs).

OBJECTIVE : This paper aims to introduce generative adversarial network technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome to implement this kind of technology.

METHODS : To organize this review comprehensively, articles and reviews were collected using the following keywords: ("Glaucoma", "optic disc", "blood vessels") and ("receptive field", "loss function", "GAN", "Generative Adversarial Network", "Deep learning", "CNN", "convolutional neural network" OR encoder). The records were identified from five highly reputed databases: IEEE Xplore, Web of Science, Scopus, Science Direct, and PubMed. These libraries broadly cover the technical and medical literature. Among the last five years of publications, only papers within the specified duration were included because the target GAN technique was invented in 2014 by Goodfellow and the publishing date of the collected papers was not earlier than 2016. Duplicate records were removed, and irrelevant titles and abstracts were excluded. In addition, we excluded papers that used optical coherence tomography (OCT) and visual field images, except for those with two-dimensional images. A large-scale systematic analysis was performed, and then a summarized taxonomy was generated. Furthermore, the results of the collected articles were summarized and added to multimedia appendix 1 and a visual representation of the results on a T-shaped matrix diagram. This study was conducted between March 2020 and November 2020.

RESULTS : We found 59 articles after conducting a comprehensive survey of the literature. Among the 59 articles, 29 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. Twenty-nine journal articles discuss recent advances in generative adversarial networks, practical experiments, and analytical studies of retinal disease.

CONCLUSIONS : Recent deep learning techniques, namely, generative adversarial networks, have shown encouraging retinal disease detection performance. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants in further improving and strengthening future work. Finally, new directions for this research have been identified.

CLINICALTRIAL :

Saeed Ali Q, Sheikh Abdullah Siti Norul Huda, Che-Hamzah Jemaima, Abdul Ghani Ahmad Tarmizi

2021-Jul-05