Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 5 de 5
Comput Math Methods Med ; 2022: 7631271, 2022.
Article in English | MEDLINE | ID: covidwho-1723964


The diagnosis of new diseases is a challenging problem. In the early stage of the emergence of new diseases, there are few case samples; this may lead to the low accuracy of intelligent diagnosis. Because of the advantages of support vector machine (SVM) in dealing with small sample problems, it is selected for the intelligent diagnosis method. The standard SVM diagnosis model updating needs to retrain all samples. It costs huge storage and calculation costs and is difficult to adapt to the changing reality. In order to solve this problem, this paper proposes a new disease diagnosis method based on Fuzzy SVM incremental learning. According to SVM theory, the support vector set and boundary sample set related to the SVM diagnosis model are extracted. Only these sample sets are considered in incremental learning to ensure the accuracy and reduce the cost of calculation and storage. To reduce the impact of noise points caused by the reduction of training samples, FSVM is used to update the diagnosis model, and the generalization is improved. The simulation results on the banana dataset show that the proposed method can improve the classification accuracy from 86.4% to 90.4%. Finally, the method is applied in COVID-19's diagnostic. The diagnostic accuracy reaches 98.2% as the traditional SVM only gets 84%. With the increase of the number of case samples, the model is updated. When the training samples increase to 400, the number of samples participating in training is only 77; the amount of calculation of the updated model is small.

Diagnosis, Computer-Assisted/methods , Fuzzy Logic , Support Vector Machine , Algorithms , Artificial Intelligence/statistics & numerical data , COVID-19/diagnosis , Computational Biology , Diagnosis, Computer-Assisted/statistics & numerical data , Humans , SARS-CoV-2
Med Arch ; 75(1): 50-55, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1236907


BACKGROUND: Consumers' willingness to use health chatbots can eventually determine if the adoption of health chatbots will succeed in delivering healthcare services for combating COVID-19. However, little research to date has empirically explored influential factors of consumer willingness toward using these novel technologies, and the effect of individual differences in predicting this willingness. OBJECTIVES: This study aims to explore (a) the influential factors of consumers' willingness to use health chatbots related to COVID-19, (b) the effect of individual differences in predicting willingness, and (c) the likelihood of using health chatbots in the near future as well as the challenges/barriers that could hinder peoples' motivations. METHODS: An online survey was conducted which comprised of two sections. Section one measured participants' willingness by evaluating the following six factors: performance efficacy, intrinsic motivation, anthropomorphism, social influence, facilitating conditions, and emotions. Section two included questions on demographics, the likelihood of using health chatbots in the future, and concerns that could impede such motivation. RESULTS: A total of 166 individuals provided complete responses. Although 40% were aware of health chatbots and only 24% had used them before, about 84% wanted to use health chatbots in the future. The strongest predictors of willingness to use health chatbots came from the intrinsic motivation factor whereas the next strongest predictors came from the performance efficacy factor. Nearly 39.5% of participants perceived health chatbots to have human-like features such as consciousness and free will, but no emotions. About 38.4% were uncertain about the ease of using health chatbots. CONCLUSION: This study contributes toward theoretically understanding factors influencing peoples' willingness to use COVID-19-related health chatbots. The findings also show that the perception of chatbots' benefits outweigh the challenges.

Artificial Intelligence/statistics & numerical data , Attitude to Health , COVID-19/prevention & control , Consumer Behavior/statistics & numerical data , Telemedicine/statistics & numerical data , Adult , COVID-19/epidemiology , Humans , Male , Social Media , Social Perception , Surveys and Questionnaires
Pharmacology ; 106(5-6): 244-253, 2021.
Article in English | MEDLINE | ID: covidwho-1206096


INTRODUCTION: The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Modern information retrieval techniques combined with artificial intelligence (AI) appear as one of the key strategies for COVID-19 living evidence management. Nevertheless, most AI projects that retrieve COVID-19 literature still require manual tasks. METHODS: In this context, we pre-sent a novel, automated search platform, called Risklick AI, which aims to automatically gather COVID-19 scientific evidence and enables scientists, policy makers, and healthcare professionals to find the most relevant information tailored to their question of interest in real time. RESULTS: Here, we compare the capacity of Risklick AI to find COVID-19-related clinical trials and scientific publications in comparison with and PubMed in the field of pharmacology and clinical intervention. DISCUSSION: The results demonstrate that Risklick AI is able to find COVID-19 references more effectively, both in terms of precision and recall, compared to the baseline platforms. Hence, Risklick AI could become a useful alternative assistant to scientists fighting the COVID-19 pandemic.

Artificial Intelligence/trends , COVID-19/therapy , Data Interpretation, Statistical , Drug Development/trends , Evidence-Based Medicine/trends , Pharmacology/trends , Artificial Intelligence/statistics & numerical data , COVID-19/diagnosis , COVID-19/epidemiology , Clinical Trials as Topic/statistics & numerical data , Drug Development/statistics & numerical data , Evidence-Based Medicine/statistics & numerical data , Humans , Pharmacology/statistics & numerical data , Registries