Telephone Follow-up Based on Artificial Intelligence Technology Among Hypertension Patients: Reliability Study (Preprint)

2021 ◽  
Author(s):  
Siyuan Wang ◽  
Yan Shi ◽  
Jing Shen ◽  
Chen Chen ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Community Health ◽  
Large Population ◽  
Pressure Control ◽  
Community Health Services ◽  
Kappa Coefficient ◽  
Salt Consumption ◽  
Artificial Intelligence Technology ◽  
High Consistency

BACKGROUND Due to the large population of hypertensives in Shanghai, the limited manpower of community health services, and the uneven level of management services, the follow-up of hypertensives in the community is inefficient and lacks quality, especially the telephone follow-up. Improving the blood pressure control rate and management is challenging. OBJECTIVE To evaluate the efficiency and reliability of artificial intelligence (AI) telephone follow-up in the management of hypertension. METHODS During May 18 and June 30, 2020, 350 hypertensives managed by the Pengpu Community Health Service Center of Jingan District in Shanghai were recruited for follow-up, once by AI and once by a human. The second follow-up was conducted within 3~7 days (mean 5.5 days) after the first survey. Cohen's Kappa coefficient was used to evaluate the reliability of the results between the two follow-up visits. RESULTS The mean length time of AI calls was shorter (4.15 minutes) than that of manual calls (5.22 minutes). The answers related to the hypertension symptoms showed moderate to substantial consistency (Kappa coefficient: 0.482–0.642), and those related to the complications showed fair consistency (Kappa coefficient: 0.363). In terms of lifestyle, the answer related to smoking showed a very high consistency (Kappa coefficient: 0.918), while those addressing salt consumption, alcohol consumption, and exercise showed moderate to substantial consistency (Kappa coefficient: 0.405–0.640). There was substantial consistency in regular usage of medication (Kappa coefficient: 0.609). The overall satisfaction of AI and manual follow-up was 93.1% and 99.5%, respectively. CONCLUSIONS These results indicate that AI telephone follow-up takes less time and is equivalent to manual follow-up to a high degree. Residents have high satisfaction, and AI telephone follow-up is reliable for the follow-up and management of hypertension patients.

scholarly journals Construction and Exploration of an Intelligent Evaluation System for Educational APP through Artificial Intelligence Technology

2021 ◽  
Vol 16 (05) ◽  
pp. 17
Author(s):  
Juan Tang ◽  
Longli Hai
Keyword(s):  
Artificial Intelligence ◽  
Empirical Analysis ◽  
Evaluation System ◽  
Educational Objective ◽  
Analytic Hierarchy ◽  
Evaluation Indexes ◽  
Index Evaluation ◽  
Artificial Intelligence Technology ◽  
Index Evaluation System ◽  
High Consistency

To improve the evaluation accuracy of educational applications (APPs), the evaluation methods of educational APPs under artificial intelligence (AI) technology are explored. First, based on the principles of establishing evaluation indexes, the evaluation indexes for educational APPs are established. Second, an index evaluation system for educational APPs is constructed, and weights are assigned to the established evaluation indexes of educational APPs with the aid of analytic hierarchy process (AHP). Finally, the availability and effectiveness of the established evaluation system are investigated through empirical analysis. The results show: Five first-level indexes and 20 second-level indexes have been established through the existing index establishment principles, and a framework for intelligent evaluation of educational APPs has been successfully constructed; AHP is utilized to calculate the weight of each index; among the first-level indexes, the weight ratios of the educational and scientific indexes of the educational APPs are larger, whose proportion exceeds 60%; among the second-level indexes, the educational objective, educational principle, and knowledge systematization account for the highest proportion; therefore, the intelligent evaluation system of education APPs is obtained; finally, the empirical analysis has revealed that the score given by the intelligent evaluation system and the actual score of users have a high consistency, which proves that the proposed intelligent evaluation system is feasible and effective. The proposed intelligent evaluation system can be used as the basis for the design of educational APPs to improve the values of educational APPs.

scholarly journals Does parental permissiveness toward cigarette smoking and alcohol use influence illicit drug use among adolescents? A longitudinal study in seven European countries

2021 ◽  
Author(s):  
Emina Mehanović ◽  
Federica Vigna-Taglianti ◽  
Fabrizio Faggiano ◽  
Maria Rosaria Galanti ◽  
Barbara Zunino ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Drug Use ◽  
Cigarette Smoking ◽  
Longitudinal Data ◽  
Illicit Drug ◽  
Large Population ◽  
Illicit Drug Use ◽  
European Countries ◽  
The Impact

Abstract Purpose Adolescents’ perceptions of parental norms may influence their substance use. The relationship between parental norms toward cigarette and alcohol use, and the use of illicit substances among their adolescent children is not sufficiently investigated. The purpose of this study was to analyze this relationship, including gender differences, using longitudinal data from a large population-based study. Methods The present study analyzed longitudinal data from 3171 12- to 14-year-old students in 7 European countries allocated to the control arm of the European Drug Addiction Prevention trial. The impact of parental permissiveness toward cigarettes and alcohol use reported by the students at baseline on illicit drug use at 6-month follow-up was analyzed through multilevel logistic regression models, stratified by gender. Whether adolescents’ own use of cigarette and alcohol mediated the association between parental norms and illicit drug use was tested through mediation models. Results Parental permissive norms toward cigarette smoking and alcohol use at baseline predicted adolescents’ illicit drug use at follow-up. The association was stronger among boys than among girls and was mediated by adolescents’ own cigarette and alcohol use. Conclusion Perceived parental permissiveness toward the use of legal drugs predicted adolescents’ use of illicit drugs, especially among boys. Parents should be made aware of the importance of norm setting, and supported in conveying clear messages of disapproval of all substances.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

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