Telemedicine-Based Diabetic Care Powered With Artificial Intelligence

2022 ◽  
pp. 340-353
Author(s):  
K. Bhargavi
Keyword(s):  
Artificial Intelligence ◽  
Performance Metrics ◽  
Diabetic Patients ◽  
Self Awareness ◽  
Treatment Plans ◽  
Sugar Levels ◽  
High Level ◽  
Very High ◽  
Diabetic Care ◽  
Intelligence Technique

Diabetes is one of the chronic diseases which keep increasing at an alarming rate, and the patients need to visit the clinic to routinely check their sugar levels and adjust their treatment plans. Artificial intelligence-enabled telemedicine is found to be a promising approach to monitor the health status of diabetic patients. Some of the promising artificial intelligence technologies for treating diabetic patients are a reactive machine, limited memory, theory of minds, and self-awareness. Each of these techniques is discussed with architecture, characteristics, algorithms, advantages, and applications. Performance analysis is carried out towards the performance metrics like accuracy, medical error rate, speed, and learning rate, and the performance achieved by self-awareness artificial intelligence technique is found to be better in delivering telemedicine-based care for diabetic patients with a very high level of precision and speed of operation.

Individual Prognosis of Diabetes Long-term Risks: A CBR Approach

2001 ◽  
Vol 40 (01) ◽  
pp. 46-51 ◽  
Author(s):  
A. Palaudàries ◽  
E. Plaza ◽  
E. Armengol
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Complication ◽  
Diabetic Patients ◽  
Case Based Reasoning ◽  
Artificial Intelligence Technique ◽  
Past Experiences ◽  
Risk Pattern ◽  
Case Based ◽  
Intelligence Technique

AbstractWe present DIRAS, an application to support physicians in determining the risk of complications for individual diabetic patients. The risk pattern of each diabetic patient is obtained using a Case-based Reasoning method called LID. Case-based Reasoning is an Artificial Intelligence technique based on solving new situations according to past experiences. For each patient, the LID method determines the risk of each diabetic complication according to the risk of already diagnosed patients. In addition, LID builds a description that can be viewed as an explanation of the obtained risk.

Plasminogen Activation in Diabetes Mellitus: Normalization of Blood Sugar Levels Improves Impaired Enzyme Kinetics In Vitro

1985 ◽  
Vol 54 (02) ◽  
pp. 413-414 ◽  
Author(s):  
Margarethe Geiger ◽  
Bernd R Binder
Keyword(s):  
Plasminogen Activator ◽  
Blood Sugar ◽  
Metabolic Disorders ◽  
Normal Values ◽  
Diabetic Patients ◽  
Type I ◽  
Plasminogen Activation ◽  
Lag Period ◽  
Sugar Levels

SummaryWe have demonstrated previously that fibrin enhanced plasmin formation by the vascular plasminogen activator was significantly impaired, when components isolated from the plasma of three uncontrolled diabetic patients (type I) were used to study plasminogen activation in vitro. In the present study it can be demonstrated that functional properties of the vascular plasminogen activators as well as of the plasminogens from the same three diabetic patients are significantly improved after normalization of blood sugar levels and improvement of HbAlc values. Most pronounced the Km of diabetic vascular plasminogen activator in the presence of fibrin returned to normal values, and for diabetic plasminogen the prolonged lag period until maximal plasmin formation occurred was shortened to almost control values. From these data we conclude that the observed abnormalities of in vitro fibrinolysis are not primarily associated with the diabetic disease, but might be secondary to metabolic disorders caused by diabetes.

التغير المناخي واثره في المقنن المائي لمحصول العنب في محافظة ديالى

2019 ◽  
Vol 1 (34) ◽  
pp. 391-422
Author(s):  
اشواق حسن حميد صالح
Keyword(s):  
Climate Change ◽  
Statistical Analysis ◽  
Wind Speed ◽  
Extreme Temperature ◽  
Solar Brightness ◽  
The Subject ◽  
Climate Station ◽  
The Times ◽  
High Level ◽  
Very High

Climate change and its impact on water resources is the problem of the times. Therefore, this study is concerned with the subject of climate change and its impact on the water ration of the grape harvest in Diyala Governorate. The study was based on the data of the Khanaqin climate station for the period 1973-2017, (1986-2017) due to lack of data at governorate level. The general trend of the elements of the climate and its effect on the water formula was extracted. The equation of change was extracted for the duration of the study. The statistical analysis was also used between the elements of the climate (actual brightness, normal temperature, micro and maximum degrees Celsius, wind speed m / s, relative humidity% The results of the statistical analysis confirm that the water ration for the study area is based mainly on the X7 evaporation / netting variable, which is affected by a set of independent variables X1 Solar Brightness X4 X5 Extreme Temperature Wind Speed ​​3X Minimal Temperature and Very High Level .

Relations between carbon removal rates, biofilm size and density of a novel anaerobic reactor: the inverse turbulent bed

2000 ◽  
Vol 41 (4-5) ◽  
pp. 253-260 ◽  
Author(s):  
P. Buffière ◽  
R. Moletta
Keyword(s):  
Removal Rate ◽  
Removal Rates ◽  
Carbon Removal ◽  
Biofilm Growth ◽  
Loading Rates ◽  
Biomass Activity ◽  
High Turbulence ◽  
The One ◽  
High Level ◽  
Very High

An anaerobic inverse turbulent bed, in which the biogas only ensures fluidisation of floating carrier particles, was investigated for carbon removal kinetics and for biofilm growth and detachment. The range of operation of the reactor was kept within 5 and 30 kgCOD· m−3· d−1, with Hydraulic Retention Times between 0.28 and 1 day. The carbon removal efficiency remained between 70 and 85%. Biofilm size were rather low (between 5 and 30 μm) while biofilm density reached very high values (over 80 kgVS· m−3). The biofilm size and density varied with increasing carbon removal rates with opposite trends; as biofilm size increases, its density decreases. On the one hand, biomass activity within the reactor was kept at a high level, (between 0.23 and 0.75 kgTOC· kgVS· d−1, i.e. between 0.6 and 1.85 kgCOD·kgVS · d−1).This result indicates that high turbulence and shear may favour growth of thin, dense and active biofilms. It is thus an interesting tool for biomass control. On the other hand, volatile solid detachment increases quasi linearly with carbon removal rate and the total amount of solid in the reactor levels off at high OLR. This means that detachment could be a limit of the process at higher organic loading rates.

Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Abdulrahman Takiddin ◽  
Jens Schneider ◽  
Yin Yang ◽  
Alaa Abd-Alrazaq ◽  
Mowafa Househ
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Scoping Review ◽  
Performance Metrics ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Evaluation Metrics ◽  
Cancer Type ◽  
Computer Algorithms ◽  
Scoping Reviews

BACKGROUND Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, Artificial Intelligence (AI) tools are being used, including shallow and deep machine learning-based techniques that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE The aim of this study is to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examines the reliability of the selected papers by studying the correlation between the dataset size and number of diagnostic classes with the performance metrics used to evaluate the models. METHODS We conducted a systematic search for articles using IEEE Xplore, ACM DL, and Ovid MEDLINE databases following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. The study included in this scoping review had to fulfill several selection criteria; to be specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were conducted by two reviewers independently. Extracted data were synthesized narratively, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS We retrieved 906 papers from the 3 databases, but 53 studies were eligible for this review. While shallow techniques were used in 14 studies, deep techniques were utilized in 39 studies. The studies used accuracy (n=43/53), the area under receiver operating characteristic curve (n=5/53), sensitivity (n=3/53), and F1-score (n=2/53) to assess the proposed models. Studies that use smaller datasets and fewer diagnostic classes tend to have higher reported accuracy scores. CONCLUSIONS The adaptation of AI in the medical field facilitates the diagnosis process of skin cancer. However, the reliability of most AI tools is questionable since small datasets or low numbers of diagnostic classes are used. In addition, a direct comparison between methods is hindered by a varied use of different evaluation metrics and image types.

Towards a Customized¬ Awareness Campaign to Educate the Public about COVID-19: Development and Validation of a Messaging Campaign to Promote Precautionary Behavior Using Geospatial Intelligence in Saudi Arabia (Preprint)

2020 ◽  
Author(s):  
Mayda Alrige ◽  
Hind Bitar Bitar ◽  
Maram Meccawi ◽  
Balakrishnan Mullachery
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Content Validity ◽  
Awareness Campaign ◽  
Artificial Intelligence Technique ◽  
The Public ◽  
High Infection ◽  
Local Map ◽  
The City ◽  
Intelligence Technique

BACKGROUND Designing a health promotion campaign is never an easy task, especially during a pandemic of a highly infectious disease, such as Covid-19. In Saudi Arabia, many attempts have been made toward raising the public awareness about Covid-19 infection-level and its precautionary health measures that have to be taken. Although this is useful, most of the health information delivered through the national dashboard and the awareness campaign are very generic and not necessarily make the impact we like to see on individuals’ behavior. OBJECTIVE The objective of this study is to build and validate a customized awareness campaign to promote precautionary health behavior during the COVID-19 pandemic. The customization is realized by utilizing a geospatial artificial intelligence technique called Space-Time Cube (STC) technique. METHODS This research has been conducted in two sequential phases. In the first phase, an initial library of thirty-two messages was developed and validated to promote precautionary messages during the COVID-19 pandemic. This phase was guided by the Fogg Behavior Model (FBM) for behavior change. In phase 2, we applied STC as a Geospatial Artificial Intelligence technique to create a local map for one city representing three different profiles for the city districts. The model was built using COVID-19 clinical data. RESULTS Thirty-two messages were developed based on resources from the World Health Organization and the Ministry of Health in Saudi Arabia. The enumerated content validity of the messages was established through the utilization of Content Validity Index (CVI). Thirty-two messages were found to have acceptable content validity (I-CVI=.87). The geospatial intelligence technique that we used showed three profiles for the districts of Jeddah city: one for high infection, another for moderate infection, and the third for low infection. Combining the results from the first and second phases, a customized awareness campaign was created. This awareness campaign would be used to educate the public regarding the precautionary health behaviors that should be taken, and hence help in reducing the number of positive cases in the city of Jeddah. CONCLUSIONS This research delineates the two main phases to developing a health awareness messaging campaign. The messaging campaign, grounded in FBM, was customized by utilizing Geospatial Artificial Intelligence to create a local map with three district profiles: high-infection, moderate-infection, and low-infection. Locals of each district will be targeted by the campaign based on the level of infection in their district as well as other shared characteristics. Customizing health messages is very prominent in health communication research. This research provides a legitimate approach to customize health messages during the pandemic of COVID-19.

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