scholarly journals Patient Readmission Prediction Due to Diabetes using Machine Learning Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 678-681
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Medical Care ◽  
Health Care Professionals ◽  
Medical Condition ◽  
Real Life ◽  
Data Sets ◽  
Machine Learning Classification ◽  
History Of ◽  
Using Data

Diabetes mellitus is a general illness of body caused due to a group of metabolic disorders and conditions where the sugar level readings over a prolonged period are very high. It affects different organs of the human body which thus harm a large number of the body's system and tissues to micro level, particularly blood veins, nerves and also skin. It usually occurs when body has malfunctioned pancreas or there is insulin resistance. As there have been huge advancements in machine learning field which is widely used in solving different real life community level problems including health care and also due to presence of vast data from different medical care centres and hospitals which can play an important role in building a machine learning model which can predict weather a person is suffering from diabetes by using data sets from different hospitals and medical care centres. We have collected data from different hospitals over past 10 years where we consider different factors that determine whether admission of a person into hospital is required or not. Depending upon the previous medical history of the person, it can be determined that weather or not the person is readmitted into the hospital and within what time period. Hence in this paper we try to construct a model where we predict weather a person is readmitted to hospital or not .Main focus is to help health care professionals, medicine practitioners and people who suffer from its symptoms improve their treatment process by predicting the chance of the person having diabetes, here by decreasing cost of treatment and enabling the concerned person to be self-aware of their medical condition.

scholarly journals Location Prediction Models using Data Mining and Machine Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 3383-3390
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Time Series Data ◽  
Real Life ◽  
Series Data ◽  
Data Sets ◽  
Location Prediction ◽  
Summary Table ◽  
User Data ◽  
Using Data

A vast availability of location based user data which is generated everyday whether it is GPS data from online cabs, or weather time series data, is essential in many ways to the user and has been applied to many real life applications such as location targeted-advertising, recommendation systems, crime-rate detection, home trajectory analysis etc. In order to analyze this data and use it to fruitfulness a vast majority of prediction models have been proposed and utilized over the years. A next location prediction model is a model that uses this data and can be designed as a combination of two or more models and techniques, but these have their own pros and cons. The aim of this document is to analyze and compare the various machine learning models and related experiments that can be applied for better location prediction algorithms in the near future. The paper is organized in a way so as to give readers insights and other noteworthy points and inferences from the papers surveyed. A summary table has been presented to get a glimpse of the methods in depth and our added inferences along with the data-sets analyzed.

Health and Medicine in the Islamic Tradition

1990 ◽  
Vol 7 (1) ◽  
pp. 89-90
Author(s):  
Dennis Michael Warren
Keyword(s):  
Health Care ◽  
Medical Care ◽  
Human Health ◽  
Well Being ◽  
Islamic Tradition ◽  
Medical Tradition ◽  
Islamic Thought ◽  
History Of ◽  
Relationship Of ◽  
The Relationship

The late Dr. Fazlur Rahman, Harold H. Swift Distinguished Service Professor of Islamic Thought at the Oriental Institute of the University of Chicago, has written this book as number seven in the series on Health/Medicine and the Faith Traditions. This series has been sponsored as an interfaith program by The Park Ridge Center, an Institute for the study of health, faith, and ethics. Professor Rahman has stated that his study is "an attempt to portray the relationship of Islam as a system of faith and as a tradition to human health and health care: What value does Islam attach to human well-being-spiritual, mental, and physical-and what inspiration has it given Muslims to realize that value?" (xiii). Although he makes it quite clear that he has not attempted to write a history of medicine in Islam, readers will find considerable depth in his treatment of the historical development of medicine under the influence of Islamic traditions. The book begins with a general historical introduction to Islam, meant primarily for readers with limited background and understanding of Islam. Following the introduction are six chapters devoted to the concepts of wellness and illness in Islamic thought, the religious valuation of medicine in Islam, an overview of Prophetic Medicine, Islamic approaches to medical care and medical ethics, and the relationship of the concepts of birth, contraception, abortion, sexuality, and death to well-being in Islamic culture. The basis for Dr. Rahman's study rests on the explication of the concepts of well-being, illness, suffering, and destiny in the Islamic worldview. He describes Islam as a system of faith with strong traditions linking that faith with concepts of human health and systems for providing health care. He explains the value which Islam attaches to human spiritual, mental, and physical well-being. Aspects of spiritual medicine in the Islamic tradition are explained. The dietary Jaws and other orthodox restrictions are described as part of Prophetic Medicine. The religious valuation of medicine based on the Hadith is compared and contrasted with that found in the scientific medical tradition. The history of institutionalized medical care in the Islamic World is traced to awqaf, pious endowments used to support health services, hospices, mosques, and educational institutions. Dr. Rahman then describes the ...

Problems in the Health Care of Dying Old Men: Complaints by their Widows

1995 ◽  
Vol 31 (2) ◽  
pp. 121-141 ◽  
Author(s):  
Maria M. Talbott
Keyword(s):  
Health Care ◽  
Quality Of Care ◽  
Medical Care ◽  
Health Care Professionals ◽  
Lack Of Control ◽  
Old Men

Complaints of older widows regarding their husbands' health care are investigated in this study. Sixty-four older widows were interviewed several years after their husbands' deaths. The deaths occurred in the early 1980s. Forty-six percent reported problems in the health care their husbands had received. Widows whose husbands had not known in advance that they were going to die were more likely to complain about their husbands' medical care than widows whose husbands had known in advance. Complaints were also related to the frequency of several symptoms of grief. The widows' complaints about their husbands' care focus on quality of care, perceived insensitivity on the part of health care professionals, lack of control over the death, and the organization of services.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

Nurses and Prescriptive Authority: A Legal and Economic Analysis

1989 ◽  
Vol 15 (2-3) ◽  
pp. 245-299 ◽  
Author(s):  
Elizabeth Harrison Hadley
Keyword(s):  
Health Care ◽  
Drug Therapy ◽  
Economic Efficiency ◽  
Health Care Professionals ◽  
Legal Authority ◽  
Economic Implications ◽  
Health Care Professions ◽  
Prescriptive Authority ◽  
Complementary Role ◽  
History Of

This article identifies twenty-six jurisdictions where nurses have been granted legal authority to prescribe drugs. The jurisdictions are divided into two groups: those where nurses have authority to prescribe without the supervision of a physician and can therefore function as substitutes for physicians; and those where nurses may prescribe only in collaboration with a supervising physician, and are thereby limited to functioning in a complementary role.The issue of prescriptive authority is discussed within the context of regulating the practice of nursing, and more generally, the health care professions. The article reviews the history of Nurse Practice Acts, focusing upon the Connecticut statute and the economic implications of this statutory approach. It is argued that the law should promote the use of nurses as substitutes for physicians whenever appropriate.The article concludes with a two-part proposal for reform: an “authorized prescriber” statute requiring health care professionals desiring to prescribe drugs to pass an examination testing their knowledge of pharmacology and drug therapy; and the elimination of the “unauthorized practice” provisions of the statutes regulating all health care professions. The proposal promotes economic efficiency by eliminating artificial constraints on the substitutability of labor in the provision of health services.

Kabuki Actors Study

2001 ◽  
Vol 16 (3) ◽  
pp. 94-98
Author(s):  
Marc Brodsky
Keyword(s):  
Health Care ◽  
Health Status ◽  
Medical Condition ◽  
Pain Severity ◽  
Health Issues ◽  
Medical Problems ◽  
Medical Practitioners ◽  
Starting Point ◽  
History Of

The Kabuki Actors Study set out to explore the health status of Kabuki actors, their performance-related medical problems, and the nature and extent of their health care. Two hundred sixteen Kabuki performers voluntarily completed an anonymous three-page survey addressing their health issues. Thirty-eight percent of the actors reported a history of at least one significant medical condition, and 88% of them identified at least one musculoskeletal or nonmusculoskeletal problem associated with performance. Sixty-nine percent of the performers had visited a physician over the preceding year, and 30% of them had consulted nonphysician medical practitioners. Kabuki actors, the Kabuki management, and physicians can use the findings of this study as a starting point to investigate why these injuries occur and how to prevent and treat them. Pain severity scales or other measurable outcomes of therapy can be used to compare the efficacies of physician and nonphysician treatments.

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

Racial disparities and medical care accessibility in cancer statistics.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18510-e18510
Author(s):  
Noha Soror ◽  
Amany Keruakous
Keyword(s):  
Health Care ◽  
Medical Care ◽  
Cancer Diagnosis ◽  
Health Care Disparities ◽  
Financial Burden ◽  
P Value ◽  
Annual Income ◽  
History Of ◽  
Prevalence Of Cancer ◽  
History Of Cancer

e18510 Background: Cancer is one of the leading causes of death worldwide. It continues to be the second leading cause of death in the United States despite all national efforts aiming to reduce cancer burden and mortality. Delays in medical care and subsequently age-appropriate screening leads to increased cancer burden which reflect on the overall prognosis. Medical care accessibility has been a challenge that is reported by approximately one third of the USA adult population. We aimed to identify health care disparities and its correlation with prevalence of cancer as well as delays in medical care due to financial challenges among Texas residents. Methods: We analyzed data from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. We measured specific health care disparities including patient’s gender, age, race/ethnicity, body mass index (BMI), annual income, alcohol consumption, history of cancer, and delays in medical care due to financial burden, among respondents to the 2017 BRFSS survey from Texas. We computed the difference between our comparison groups using chi-square test for categorical variables and t-test for continuous variables. Results: We analyzed the differences in health care disparities among respondents with and without history of cancer. We report results from 11,165 adult respondents who reside in Texas, among which nine percent were diagnosed with cancer. We noticed a higher proportion of females than males among participants with a history of cancer (64% females p < 0.0001). Age did differ between both groups, with the majority of participants with cancer are aged 50 years and older. Interestingly, BMI did not differ between both groups (p-value = 0.6930). Although annual income did not differ between both groups, twelve percent of participants with cancer diagnosis suffered from delays in medical care due to financial burden. Racial disparities were statistically different between participants with or without cancer (p < .0001). Seventy seven percent of patients with cancer diagnosis were White and non-Hispanic with a cancer prevalence rate of 12% in that racial group. On a stratified analysis to compute the relationship between delayed medical care due to financial burden and cancer diagnosis among all ethnic groups, it was not statistically different (p-value = 0.1063). We showed that prevalence of cancer among multiracial participants and other racial minorities was higher in the group of participants who reported delays in medical care due to financial burden (11% versus 7%). Conclusions: Racial and ethnic disparities could affect accessibility to medical services. Race is a significant variable that is associated with cancer, with higher prevalence of cancer in White and non-Hispanic. Delayed medical care due to financial burden is more pronounced in multiracial population and racial minorities and should be targeted in future quality improvement projects.

Need and opportunities for professional education of nursing specialists

2021 ◽  
pp. 25-37
Author(s):  
Larisa Arkadievna Karaseva
Keyword(s):  
Health Care ◽  
Medical Care ◽  
Professional Education ◽  
Health Care Professionals ◽  
Professional Growth ◽  
Nursing Practice ◽  
Theory Development ◽  
Management Research ◽  
Education Management ◽  
Experimental Base

The task of educating health care professionals is to create an educational and experimental base to support practice, education, management, research, and theory development in order to preserve and improve the health of the population. The article summarizes the principles of education that contribute to the professional growth of specialists, ensuring the safety and competence of medical care by improving nursing practice.

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