scholarly journals Application of Robotic Software on Effective Diabetes Control (GH-Method: Math-Physical Medicine)

2020 ◽  
Vol 2 (2) ◽  
pp. 27-31
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Artificial Intelligence ◽  
Big Data Analytics ◽  
Wave Theory ◽  
Diabetes Control ◽  
Innovative Technology ◽  
Time Space ◽  
Segmentation Analysis ◽  
Clinical Tools ◽  
Engineering Modeling ◽  
Robotic Software

This paper focuses on the author’s invented robotic software technology, the artificial intelligence glucometer (AIG) product, to provide a diagnosis for diabetes disease and glucose control. From 2010–2013, he self-studied internal medicine and food nutrition. In 2014, he further utilized topology concept, partial differential equation, non-linear algebra, and finite element engineering concept to develop a human metabolism’s mathematical model. It consists of 10 categories and ~500 elements with ~1.5 million collected data of his own body health, disease conditions, and lifestyle details. Starting from 2015, he focused on the root cause of diabetes, which is “glucose”. By applying wave theory, signal processing, energy theory, optical physics, structural & fluid dynamics from physics and engineering modeling; pattern and segmentation analysis, time/space/frequency domain analyses, big data analytics, machine learning and self-correction, prediction equations from mathematics and computer science, he decided to utilize his robotic software as the foundation to further build up his needed medical research and clinical tools. By using the artificial intelligence (AI) robotic software, the author’s average glucose decreased from 280 mg/dL to 118 mg/dL and his hemoglobin A1C (HbA1C or A1C) reduced from 10%+ to below 6.5%, without diabetes medications. All his diabetes complications are either under control or have subsided. This innovative technology of his robotic software for glucose prediction and diabetes control has also been proven by many other patients, who have achieved equally remarkable medical results.

scholarly journals Using Candlestick Charting and GH-Method: Math-Physical Medicine to Investigate the Differences of Glucose Measurement Results Based on both Finger-Piercing Method and Continuous Monitoring Device

2020 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Continuous Monitoring ◽  
Big Data Analytics ◽  
Glucose Measurement ◽  
Physical Medicine ◽  
Continuous Glucose Monitor ◽  
Segmentation Analysis ◽  
Measurement Results ◽  
Engineering Modeling ◽  
Monitor Device ◽  
Metabolism Model

The author developed his GH-Method: math-physical medicine (MPM) by applying mathematics, physics, engineering modeling, and computer science such as big data analytics and artificial intelligence to derive the mathematical metabolism model and three prediction tools for weight, FPG, and PPG with >30 input elements. This research paper describes glucose measurement results based on the finger-piercing method and continuous glucose monitor device using candlestick charting and segmentation analysis.

scholarly journals The healthcare technology needs you

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Sumarno Adi Subrata ◽  
Jonathan Bayuo ◽  
Busra Sahin
Keyword(s):  
Artificial Intelligence ◽  
3D Printing ◽  
Standard Treatment ◽  
Healthcare Services ◽  
Fundamental Aspect ◽  
Innovative Technology ◽  
Community Settings ◽  
Healthcare Technology ◽  
Health Related ◽  
Digital Healthcare

The growing evidence and technology in healthcare lead to an improvement in the patient's health across a continuum of services in clinical and community settings. A multidisciplinary team should work in tandem on this phenomenon. Therefore, innovative healthcare technology must be designed intensively to optimize productivity and provide new insight along with support the standard treatment for particular diseases. In the coming years, technology is needed to change the way of caring for the patient. This is a fundamental aspect because the recent technology has shaped up in front of our practice with advances in digital healthcare services, such as 3D printing, robotics, nanotechnology and even artificial intelligence (The Medical Futurist, 2021). To respond to this, updated studies should be developed and published focusing on innovative technology including in Medicine, Nursing, Pharmacy, and other health-related topics.

scholarly journals Special Issue in Artificial Intelligence

2019 ◽  
Vol 30 (1) ◽  
pp. 7-8
Author(s):  
Dora Maria Ballesteros
Keyword(s):  
Artificial Intelligence ◽  
Communication Networks ◽  
Language Processing ◽  
Autonomous Systems ◽  
Big Data Analytics ◽  
Human Robot Interaction ◽  
Support Vector ◽  
Special Issue ◽  
Science And Engineering ◽  
Robot Interaction

Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8].

O12 Artificial intelligence-based risk prediction for death after emergency laparotomy using multi slice contrast enhanced computerised tomography

2021 ◽  
Vol 108 (Supplement_5) ◽  
Author(s):  
S Rahman ◽  
S Body ◽  
M Ligthart ◽  
P May-Miller ◽  
P Pucher ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Risk Prediction ◽  
Automated Analysis ◽  
Mortality Prediction ◽  
Elastic Net ◽  
Emergency Laparotomy ◽  
Cross Sectional ◽  
Post Surgery ◽  
Preoperative Ct ◽  
Clinical Tools

Abstract Introduction Emergency laparotomy has a considerable mortality risk, with more than one in ten patients not surviving to discharge. Preoperative risk prediction using clinical tools is well established, however implemented variably. Preoperative CT is undertaken almost universally and contains granular data beyond diagnostics, including body composition, disease severity and other abstract features with the potential to enhance risk prediction. In this study we established the value of features extracted in an automated fashion from pre-operative CT in predicting 90-day post-surgery mortality. Method Anonymised CTs were collated from patients undergoing emergency laparotomy at ten hospitals in Southern England (2016–2017). For each case, axial portal venous abdominal/pelvic series were analysed using a pre-trained neural network, with each image converted into a matrix of numerical features. An elastic-net regression model to predict 90-day mortality was trained using these features and evaluated by bootstrapping with 1000 resampled datasets. Result A total of 136,709 images from 274 cases were available for analysis with a mean of 503 per case. Mortality within 90 days occurred in 34 cases (12.4%) with an average NELA mortality prediction of 8.5%. On internal (bootstrap) validation, the elastic net model derived from CT yielded excellent performance (AUC 0.903 95%CI 0.897–0.909), significantly in excess of the NELA risk calculator (AUC 0.809 95%CI 0.736–0.875), with a broader prediction range (0.01%-89.71%). Conclusion Artificial intelligence techniques applied to routinely performed cross-sectional imaging predicts emergency laparotomy mortality with greater accuracy than clinical data alone. Integration of these automated tools may be possible in the future. Take-home Message Automated analysis of CT can accurately predict risk of mortality after emergency laparotomy.

Big Data and Machine Learning

2018 ◽  
pp. 225-239
Author(s):  
Fernando Enrique Lopez Martinez ◽  
Edward Rolando Núñez-Valdez
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Complete Solution ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Healthcare Organizations ◽  
High Level

IoT, big data, and artificial intelligence are currently three of the most relevant and trending pieces for innovation and predictive analysis in healthcare. Many healthcare organizations are already working on developing their own home-centric data collection networks and intelligent big data analytics systems based on machine-learning principles. The benefit of using IoT, big data, and artificial intelligence for community and population health is better health outcomes for the population and communities. The new generation of machine-learning algorithms can use large standardized data sets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. This chapter shows a high-level implementation of a complete solution of IoT, big data, and machine learning implemented in the city of Cartagena, Colombia for hypertensive patients by using an eHealth sensor and Amazon Web Services components.

Predictive Analysis for Digital Marketing Using Big Data

2017 ◽  
pp. 259-283
Author(s):  
Balamurugan Balusamy ◽  
Priya Jha ◽  
Tamizh Arasi ◽  
Malathi Velu
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Big Data ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Predictive Analysis ◽  
Cognitive Computing ◽  
Internet Resources ◽  
To Come

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2022 ◽  
pp. 406-428
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

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