Applying Machine Learning to Health Care: Improving access for all [WIE From Around the World]

2021 ◽  
Vol 15 (2) ◽  
pp. 40-42
Author(s):  
Leslie Prives
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
The World

scholarly journals Novel Corona Virus Prediction and Transmission Analysis using Machine Learning Models

2021 ◽  
Vol 309 ◽  
pp. 01034
Author(s):  
G. Karuna ◽  
K. Pravallika ◽  
Karanam Madhavi ◽  
V. Srilakshmi ◽  
K. Swaraja ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Regression Model ◽  
Polynomial Regression ◽  
Well Being ◽  
Support Vector ◽  
Learning Models ◽  
Corona Virus ◽  
The World ◽  
Machine Learning Models

Today we all are suffering from Covid-19, a novel virus and it is the most harmful disease across the world which mainly comes under the domain of health care research. Healthcare system gives importance to health states of the population or individual. Healthcare plays a vital role in promoting physical and mental health and well- being of people around the world. Efficient health care system leads to country’s economy, industrialization and development. Corona virus is dangerous animal and human pathogens and it is threatening people by spreading all over the world. Corona virus patients mostly suffer from lung infection studies have shown it clinically. We proposed detailed analysis on how to predict the expected death, recovered and confirmed cases based on the available data across the world using various machine learning models. Especially we constructed linear regression model (LRM), support vector machine model (SVMM) and polynomial regression models (PRM) and predicted future expected cases over a period of next 15 days. The error between the predicted model and official data curve is quite small in the process of transmission in data modeling. Compare to other models Polynomial regression model performs best prediction of corona positive cases. Forward prediction and backward inference of the epidemic helps to take decisions for necessary actions during Covid-19 propagation.

scholarly journals COVID-19 Diagnosis from CT Imaging using Imaging and Machine Analysis

2021 ◽  
Vol 10 (9) ◽  
pp. 80-83
Author(s):  
Gayatri A. Deochake ◽  
◽  
Vilas S. Gaikwad ◽  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Early Detection ◽  
Lung Infection ◽  
Ct Imaging ◽  
Rapid Progression ◽  
The World ◽  
Ct Data ◽  
Machine Analysis ◽  
Hiv Negative

Coronavirus (COVID-19) is spreading rapidly around the world and, as of October 2020, more than 1,966,000 people have been infected in more than 200 countries. Early detection of COVID-19 is essential for the provision and protection of HIV-negative people in adequate health care for patients. To do this, we developed an automated diagnostic program for COVID-19 from pneumonia (CPA) obtained from chest tomography (CT). We propose, in particular, the Noise Resilient method of machine learning that focuses on regions of lung infection while making diagnostic decisions. Note that the sizes of the infection sites between COVID-19 and CAP are not well measured, in part due to the rapid progression of COVID-19 after the onset of symptoms. Large amounts of CVID-19 CT data from hospitals have been used to evaluate our frameworks.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

The Great Awakening - Digitalization in Dental Healthcare during the pandemic COVID-19

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 380-384
Author(s):  
Priyanka Paul Madhu ◽  
Yojana Patil ◽  
Aishwarya Rajesh Shinde ◽  
Sangeeta Kumar ◽  
Pratik Phansopkar
Keyword(s):  
Health Care ◽  
Task Force ◽  
Care Service ◽  
Health Care Service ◽  
Care Facility ◽  
Health Care Facility ◽  
Health Care Facilities ◽  
World Health ◽  
Record System ◽  
The World

disease in 2019, also called COVID-19, which has been widely spread worldwide had given rise to a pandemic situation. The public health emergency of international concern declared the agent as the (SARS-CoV-2) the severe acute respiratory syndrome and the World Health Organization had activated significant surveillance to prevent the spread of this infection across the world. Taking into the account about the rigorousness of COVID-19, and in the spark of the enormous dedication of several dental associations, it is essential to be enlightened with the recommendations to supervise dental patients and prevent any of education to the dental graduates due to institutional closure. One of the approaching expertise that combines technology, communications and health care facilities are to refine patient care, it’s at the cutting edge of the present technological switch in medicine and applied sciences. Dentistry has been improved by cloud technology which has refined and implemented various methods to upgrade electronic health record system, educational projects, social network and patient communication. Technology has immensely saved the world. Economically and has created an institutional task force to uplift the health care service during the COVID 19 pandemic crisis. Hence, the pandemic has struck an awakening of the practice of informatics in a health care facility which should be implemented and updated at the highest priority.

Effect of Fluoride Mouthwash in Iranian Schools: Treatment or Exacerbation of the Disease

2019 ◽  
Author(s):  
Mohammad Karimi
Keyword(s):  
Quality Of Life ◽  
Health Care ◽  
Oral Health ◽  
Significant Role ◽  
Oral Hygiene ◽  
Easy Access ◽  
Tooth Decay ◽  
Insufficient Attention ◽  
The World

Dental and oral health is an important part that plays a significant role in the quality of life of people in our society, especially children, but due to insufficient attention, tooth decay in the world is increasing every year. Promoting oral hygiene requires the people's easy access to primary oral health care and the use of these services should be classified.

Infodemiological patterns in searching medication errors: relationship with risk management and shift work (Preprint)

2019 ◽  
Author(s):  
Fabio Fabbian ◽  
Emanuele Di Simone ◽  
Sara Dionisi ◽  
Noemi Giannetta ◽  
Luigi De Gennaro ◽  
...  
Keyword(s):  
Health Care ◽  
Risk Management ◽  
Shift Work ◽  
Medication Errors ◽  
Medical Errors ◽  
World Health ◽  
Noncommunicable Diseases ◽  
Western World ◽  
The World ◽  
Google Search

BACKGROUND Western world health care systems have been trying to improve their efficiency and effectiveness in order to respond properly to the aging of the population and the epidemic of noncommunicable diseases. Errors in drugs administration is an actual important issue due to different causes. OBJECTIVE Aim of this study is to measure interest in online seeking medical errors information online related to interest in risk management and shift work. METHODS We investigated Google Trends® for popular search relating to medical errors, risk management and shift work. Relative search volumes (RSVs) were evaluated for the period November 2008-November 2018 all around the world. A comparison between RSV curves related to medical errors, risk management and shift work was carried out. Then we compared world to Italian search. RESULTS RSVs were persistently higher for risk management than for medication errors during the study period (mean RSVs 74 vs. 51%) and RSVs were stably higher for medical errors than shift work during the study period (mean RSVs 51 vs 23%). In Italy, RSVs were much lower than the rest of the world, and RSVs for medication errors during the study period were negligible. Mean RSVs for risk management and shift work were 3 and 25%, respectively. RSVs related to medication errors and clinical risk management were correlated (r=0.520, p<0.0001). CONCLUSIONS Google search query volumes related to medication errors, risk management and shift work are different. RSVs for risk management are higher, are correlated with medication errors, and the relationship with shift work appears to be even worse, by analyzing the entire world. In Italy such a relationship completely disappears, suggesting that it needs to be emphasized by health care authorities.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals Depression among health care students in the time of COVID-19: the mediating role of resilience in the hopelessness–depression relationship

2021 ◽  
pp. 008124632199445
Author(s):  
Tammy-lee Pretorius
Keyword(s):  
Health Care ◽  
Traumatic Event ◽  
National Level ◽  
Depression Scale ◽  
Health Care Students ◽  
Mediating Role ◽  
First Case ◽  
Hopelessness Depression ◽  
The World

COVID-19 spread rapidly across the world, and by March 2020, the first case of COVID-19 was identified in South Africa. Lockdown-related measures such as restricted movement and isolation were implemented to contain the virus. Combined with these measures, factors such as economic decline, job losses, and food shortages can cause numerous mental health sequelae such as depression. Feelings of hopelessness and helplessness as well as cases of suicide have been reported around the world due to the pandemic and the associated feelings of anxiety and depression. The aims of this study were to investigate levels of hopelessness and depression in a sample of health care students. A random sample of students ( N = 174) enrolled in a health sciences programme at the University of the Western Cape completed the Beck Hopelessness Scale, the Center for Epidemiological Studies Depression Scale, and a three-item Resilience Scale. The results revealed high levels of hopelessness and depression compared to previously reported normative data for these scales. In addition, the indirect effects of hopelessness on depression were significant, demonstrating the mediating role of resilience in the hopelessness–depression relationship. These results highlight a call for universities to take proactive measures in providing students with free and easily accessible resources to help them cope and manage stress during a traumatic event. More importantly, at a national level, preventive measures should be implemented to strengthen resilience in young adults.

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