Afghanistan Physicians: per 1000 People data was reported at 0.254 Ratio in 2020. This records an increase from the previous number of 0.212 Ratio for 2019. Afghanistan Physicians: per 1000 People data is updated yearly, averaging 0.186 Ratio from Dec 1960 (Median) to 2020, with 26 observations. The data reached an all-time high of 0.298 Ratio in 2014 and a record low of 0.035 Ratio in 1960. Afghanistan Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Afghanistan – Table AF.World Bank.WDI: Social: Health Statistics. Physicians include generalist and specialist medical practitioners.;World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.;Weighted average;This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/].

Number of Doctors: Registered: Medical Council of India data was reported at 1,169.000 Person in 2014. This records a decrease from the previous number of 5,603.000 Person for 2013. Number of Doctors: Registered: Medical Council of India data is updated yearly, averaging 1,989.000 Person from Dec 2002 (Median) to 2014, with 13 observations. The data reached an all-time high of 5,603.000 Person in 2013 and a record low of 921.000 Person in 2004. Number of Doctors: Registered: Medical Council of India data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLB001: Health Human Resources: Number of Doctors: Registered.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Health worker distribution (%) - Medical doctors





  Dataset Description

This dataset provides country-level data for the indicator "3.c.1 Health worker distribution (%) - Medical doctors" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is presented in a wide format… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/health-worker-distribution-medical-doctors-for-african-countries.

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Hyper target within and outside your desired markets with GDPR and CAN-SPAM compliant healthcare email lists that get integrated into your CRM and ESPs. Balance out the sales and marketing efforts by aligning goals using email lists from the healthcare industry. Build strong business relationships with potential clients through personalized campaigns. Call Infotanks Media for a free consultation. Explore new geographies and target markets with a focused approach using healthcare email lists. Align your sales teams and marketing teams through personalized email marketing campaigns to ensure they accomplish business goals together. Add value and grow revenue to take your business to the next level of success. Double up your business and revenue growth with email lists of healthcare professionals. Send segmented campaigns to monitor behaviors and understand the purchasing habits of your potential clients. Send follow up nurturing email marketing campaigns to attract your potential clients to become converted customers. Close deals sooner with detailed information of your prospects using the healthcare email list from Infotanks Media. Reach healthcare professionals on their preferred platform of communication with the email list of healthcare professionals. Identify, capture, explore, and grow in your target markets anywhere in the world with a fully verified, validated, and compliant email database of healthcare professionals. Move beyond the traditional approach and automate sales cycles with buying triggers sent through email marketing campaigns. Use the healthcare email list from Infotanks Media to engage with your targeted potential clients and get them to respond. Increase email marketing campaign response rate to convert better! Reach out to Infotanks Media to customize your healthcare email lists. Call today!"

1. Overview This dataset is a collection of multimodal high quality image sets of medical data that are ready to use for optimizing the accuracy of computer vision models. All of the contents are sourced from Pixta AI's partner network with high quality & full data compliance.

2. Data subject The datasets consist of various models

3. X-ray datasets

4. CT datasets

5. MRI datasets

6. Mammography datasets

7. Segmentation datasets

8. Classification datasets

9. Regression datasets

10. Use case The dataset could be used for various Healthcare & Medical models:

11. Medical Image Analysis

12. Remote Diagnosis

13. Medical Record Keeping ... Each data set is supported by both AI and expert doctors review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

14. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ or contact via our email admin.bi@pixta.co.jp.

A List of UK Health Workers Who Have Died from COVID-19

Made machine-readable by hand from data from the UK newspaper "The Guardian", in this article: "Doctors, nurses, porters, volunteers: the UK health workers who have died from Covid-19" https://www.theguardian.com/world/2020/apr/16/doctors-nurses-porters-volunteers-the-uk-health-workers-who-have-died-from-covid-19

The Guardian is continuing to update the list day-by-day, as the COVID-19 pandemic continues. I do not plan to update this dataset, assuming, since the data collection biases are unknown, that nobody else will find it very interesting. I am not a copyright lawyer and do not know if this data is protected copyright, and if so, in which parts of the world.

Caveat: Creating this dataset from a newspaper article required a lot of hand work. I've done my best, but there may be mistakes.

Columns: Name age institution city: I have filled this in myself; I am ignorant of UK geography and there may well be mistakes date_of_death possible_ppe_issue: mostly blank, but I have filled in "yes" where the article mentions a person who had doubts about the adequacy of PPE (personal protective equipment) MED_SPEC: I have attempted to fill in a medical specialty from the values used on the Eurostat web site for Physicians by Medical Specialty" and "Nursing and caring professionals" tables. The idea is to be able to calculate a fraction of affected individuals by specialty.

Attitudinal/Behavioural Questions Whether ever registered with an NHS doctor, date of last consultation, reason for not having NHS doctor, intention to register with an NHS doctor, whether respondent has regular private doctor or no doctor. Random for patients, total doctors of patients' sample Face-to-face interview Postal survey Face-to-face interviews were conducted with patients, and doctors received a postal questionnaire.

Somalia SO: Physicians: per 1000 People data was reported at 0.029 Ratio in 2014. This records a decrease from the previous number of 0.035 Ratio for 2010. Somalia SO: Physicians: per 1000 People data is updated yearly, averaging 0.037 Ratio from Dec 1960 (Median) to 2014, with 12 observations. The data reached an all-time high of 0.071 Ratio in 1984 and a record low of 0.024 Ratio in 1960. Somalia SO: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Somalia – Table SO.World Bank: Health Statistics. Physicians include generalist and specialist medical practitioners.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

Yemen YE: Physicians: per 1000 People data was reported at 0.311 Ratio in 2014. This records an increase from the previous number of 0.310 Ratio for 2009. Yemen YE: Physicians: per 1000 People data is updated yearly, averaging 0.219 Ratio from Dec 1960 (Median) to 2014, with 15 observations. The data reached an all-time high of 0.338 Ratio in 2004 and a record low of 0.007 Ratio in 1990. Yemen YE: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Yemen – Table YE.World Bank: Health Statistics. Physicians include generalist and specialist medical practitioners.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

Context

In hospitals, medical treatments and surgeries can be categorized into inpatient and outpatient procedures. For patients, it is important to understand the difference between these two types of care, because they impact the length of a patient’s stay in a medical facility and the cost of a procedure.

Inpatient Care (Incare Patient) and Outpatient Care (Outcare Patient)

The difference between an inpatient and outpatient care is how long a patient must remain in the facility where they have the procedure done.

Inpatient care requires overnight hospitalization. Patients must stay at the medical facility where their procedure was done (which is usually a hospital) for at least one night. During this time, they remain under the supervision of a nurse or doctor.

Patients receiving outpatient care do not need to spend a night in a hospital. They are free to leave the hospital once the procedure is over. In some exceptional cases, they need to wait while anesthesia wears off or to make sure there are not any complications. As long as there are not any serious complications, patients do not have to spend the night being supervised. [source of information: pbmhealth]

Content

Problem Statement In today’s world of automation, the skills and knowledge of a person could be utilized at the best places possible by automating tasks wherever possible. As a part of the hospital automation system, one can build a system that would predict and estimate whether the patient should be categorized as an incare patient or an outcare patient with the help of several data points about the patients, their conditions and lab tests.

Objective Build a machine learning model to predict if the patient should be classified as in care or out care based on the patient's laboratory test result.

Data

About the data The dataset is Electronic Health Record Predicting collected from a private Hospital in Indonesia. It contains the patient's laboratory test results used to determine next patient treatment whether in care or out care.

Attribute Information

Given is the attribute name, attribute type, the measurement unit and a brief description.

Name / Data Type / Value Sample/ Description

HAEMATOCRIT /Continuous /35.1 / Patient laboratory test result of haematocrit

HAEMOGLOBINS/Continuous/11.8 / Patient laboratory test result of haemoglobins

ERYTHROCYTE/Continuous/4.65 / Patient laboratory test result of erythrocyte

LEUCOCYTE /Continuous /6.3 / Patient laboratory test result of leucocyte

THROMBOCYTE/Continuous/310/ Patient laboratory test result of thrombocyte

MCH/Continuous /25.4/ Patient laboratory test result of MCH

MCHC/Continuous/33.6/ Patient laboratory test result of MCHC

MCV/Continuous /75.5/ Patient laboratory test result of MCV

AGE/Continuous/12/ Patient age

SEX/Nominal – Binary/F/ Patient gender

SOURCE/Nominal/ {1,0}/The class target 1.= in care patient, 0 = out care patient

Acknowledgements

This dataset was downloaded from Mendeley Data. Sadikin, Mujiono (2020), “EHR Dataset for Patient Treatment Classification”, Mendeley Data, V1, doi: 10.17632/7kv3rctx7m.1

Nigeria NG: Physicians: per 1000 People data was reported at 0.395 Ratio in 2010. This records an increase from the previous number of 0.376 Ratio for 2009. Nigeria NG: Physicians: per 1000 People data is updated yearly, averaging 0.192 Ratio from Dec 1960 (Median) to 2010, with 19 observations. The data reached an all-time high of 0.395 Ratio in 2010 and a record low of 0.017 Ratio in 1960. Nigeria NG: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Health Statistics. Physicians include generalist and specialist medical practitioners.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

The number of physicians in the United Arab Emirates was forecast to continuously increase between 2024 and 2029 by in total 8.2 thousand physicians (+23.71 percent). After the fifteenth consecutive increasing year, the number of physicians is estimated to reach 42.78 thousand physicians and therefore a new peak in 2029. Notably, the number of physicians of was continuously increasing over the past years.Depicted here is the estimated number of physicians in the geographical unit at hand. Thereby physicians include medical specialists as well as general practitioners.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of physicians in countries like Bahrain and Israel.

Dominican Republic DO: Physicians: per 1000 People data was reported at 1.494 Ratio in 2011. This records an increase from the previous number of 1.078 Ratio for 2008. Dominican Republic DO: Physicians: per 1000 People data is updated yearly, averaging 1.076 Ratio from Dec 1960 (Median) to 2011, with 12 observations. The data reached an all-time high of 2.156 Ratio in 1997 and a record low of 0.121 Ratio in 1960. Dominican Republic DO: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank: Health Statistics. Physicians include generalist and specialist medical practitioners.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

Analysis of ‘World Bank WDI 2.12 - Health Systems’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/danevans/world-bank-wdi-212-health-systems on 21 November 2021.

--- Dataset description provided by original source is as follows ---

World Bank - World Development Indicators: Health Systems

This is a digest of the information described at http://wdi.worldbank.org/table/2.12# It describes various health spending per capita by Country, as well as doctors, nurses and midwives, and specialist surgical staff per capita

Content

Notes, explanations, etc. 1. There are countries/regions in the World Bank data not in the Covid-19 data, and countries/regions in the Covid-19 data with no World Bank data. This is unavoidable. 2. There were political decisions made in both datasets that may cause problems. I chose to go forward with the data as presented, and did not attempt to modify the decisions made by the dataset creators (e.g., the names of countries, what is and is not a country, etc.).

Columns are as follows: 1. Country_Region: the region as used in Kaggle Covid-19 spread data challenges. 2. Province_State: the region as used in Kaggle Covid-19 spread data challenges. 3. World_Bank_Name: the name of the country used by the World Bank 4. Health_exp_pct_GDP_2016: Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.

1. Health_exp_public_pct_2016: Share of current health expenditures funded from domestic public sources for health. Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health.

2. Health_exp_out_of_pocket_pct_2016: Share of out-of-pocket payments of total current health expenditures. Out-of-pocket payments are spending on health directly out-of-pocket by households.

3. Health_exp_per_capita_USD_2016: Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year.

4. per_capita_exp_PPP_2016: Current expenditures on health per capita expressed in international dollars at purchasing power parity (PPP).

5. External_health_exp_pct_2016: Share of current health expenditures funded from external sources. External sources compose of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. External sources either flow through the government scheme or are channeled through non-governmental organizations or other schemes.

6. Physicians_per_1000_2009-18: Physicians include generalist and specialist medical practitioners.

7. Nurse_midwife_per_1000_2009-18: Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses.

8. Specialist_surgical_per_1000_2008-18: Specialist surgical workforce is the number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100,000 population.

9. Completeness_of_birth_reg_2009-18: Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.

10. Completeness_of_death_reg_2008-16: Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country.

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Inspiration

Does health spending levels (public or private), or hospital staff have any effect on the rate at which Covid-19 spreads in a country? Can we use this data to predict the rate at which Cases or Fatalities will grow?

--- Original source retains full ownership of the source dataset ---

Ukraine UA: Physicians: per 1000 People data was reported at 3.000 Ratio in 2014. This records a decrease from the previous number of 3.506 Ratio for 2013. Ukraine UA: Physicians: per 1000 People data is updated yearly, averaging 3.686 Ratio from Dec 1980 (Median) to 2014, with 28 observations. The data reached an all-time high of 4.407 Ratio in 1995 and a record low of 2.951 Ratio in 2003. Ukraine UA: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ukraine – Table UA.World Bank: Health Statistics. Physicians include generalist and specialist medical practitioners.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

The purpose of the study: to analyse Lithuanian residents opinion about peculiarities of communication between doctors and patients. Major investigated questions: respondents were asked when was the last time they went to see a doctor. It respondents visited a doctor in the last 12 months, they were asked to indicate how long was the time period between registration to see a doctor and actual visit. It was analysed how long respondents had to wait at their last visit from the time appointed or from the time when they got to a health institution to the time a doctor got to them. It was questioned what was the main purpose for respondents last visit to a doctor. After question block was presented, respondents were asked for what complains, illnesses they visited a doctor the last time. Respondents were asked to evaluate a doctor whom they visited the last time. After question block was presented, peculiarities of doctor interaction with respondents (patients) at the time of their last visit was analysed. Respondents were asked if they think that a doctor can take care of their health. It was questioned how likely is that respondents would search for information about health using electronic tools. Respondents were asked if they are currently using medicines which were prescribed by a doctor to whom they visited the last time. Respondents, who uses prescribed medicines, were asked to indicate how often they use them exactly like doctor prescribed. Socio-demographic characteristics: gender, age, monthly family income per one family member, employment.

This dataset enables studying the relationship between a country's economic and social factors — such as GDP per capita, government health expenditure, Human Development Index (HDI), World Happiness Index, and density of doctors per population — with several key health indicators, like alcohol consumption, life expectancy, child mortality, non-communicable disease mortality, obesity prevalence, and undernourishment rates. It covers 50 countries from 2002 to 2021.A dashboard is also provided to facilitate the study, including plots for comparison of any selected variables for any of the available years, countries and geographic regions.

This dataset and dashboard has been created as part of a data management project for university IQS, Ramon Llull.

These datasets were collected to fulfil the requirement of University coursework.

The complete source code and paper are available on GitHub. Click here.

About Dataset

These datasets contain the information of the World Development Indicator (WDI) provided by the world bank, the non-communicable mortality rate, the suicide rate and the number of health workforce data by the World Health Organization (WHO).

Implementation

• Data Cleaning and Preparation
• Exploratory Data Analysis
• Machine Learning Prediction - Random Forest
• Deep Learning Prediction - Neural Network

Analysis of ‘COVID-19 Healthy Diet Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mariaren/covid19-healthy-diet-dataset on 12 November 2021.

--- Dataset description provided by original source is as follows ---

“Health requires healthy food."

Roger Williams (1603 – 1683)

In the past couple months, we’ve witnessed doctors, nurses, paramedics and thousands of medical workers putting their lives on the frontline to save patients who are infected. And as the battle with COVID-19 continues, we should all ask ourselves – What should we do to help out? What can we do to protect our loved ones, those who sacrifice for us, and ourselves from this pandemic?
These questions all relate back to the CORD-19 Open Research Dataset Challenge Task Question: “What do we know about non-pharmaceutical interventions?”
And my simple answer is : We need to protect our families and our own healths by adapting to a healthy diet.

Inspiration and Research Objectives

The USDA Center for Nutrition Policy and Promotion recommends a very simple daily diet intake guideline: 30% grains, 40% vegetables, 10% fruits, and 20% protein, but are we really eating in the healthy eating style recommended by these food divisions and balances?
In this dataset, I have combined data of different types of food, world population obesity and undernourished rate, and global COVID-19 cases count from around the world in order to learn more about how a healthy eating style could help combat the Corona Virus. And from the dataset, we can gather information regarding diet patterns from countries with lower COVID infection rate, and adjust our own diet accordingly.
In each of the 4 datasets below, I have calculated fat quantity, energy intake (kcal), food supply quantity (kg), and protein for different categories of food (all calculated as percentage of total intake amount). I've also added on the obesity and undernourished rate (also in percentage) for comparison. The end of the datasets also included the most up to date confirmed/deaths/recovered/active cases (also in percentage of current population for each country).

Acknowledgements

• Data for different food group supply quantities, nutrition values, obesity, and undernourished percentages are obtained from Food and Agriculture Organization of the United Nations FAO website To see the specific types of food included in each category from the FAO data, take a look at the last dataset Supply_Food_Data_Description.csv.

• Data for population count for each country comes from Population Reference Bureau PRB website

• Data for COVID-19 confirmed, deaths, recovered and active cases are obtained from Johns Hopkins Center for Systems Science and Engineering CSSE website

• The USDA Center for Nutrition Policy and Promotion diet intake guideline information can be found in ChooseMyPlate.gov

Note: I will update and push new versions of the datasets weekly. (Current version include COVID data from the week of 02/06/2021) Click here to see my data cleaning/preprocessing code in R

If you like this dataset, please don't forget to give me an upvote! 👍

--- Original source retains full ownership of the source dataset ---

Croatia HR: Physicians: per 1000 People data was reported at 3.610 Ratio in 2021. This records a decrease from the previous number of 6.959 Ratio for 2020. Croatia HR: Physicians: per 1000 People data is updated yearly, averaging 2.425 Ratio from Dec 1980 (Median) to 2021, with 37 observations. The data reached an all-time high of 6.959 Ratio in 2020 and a record low of 1.673 Ratio in 1980. Croatia HR: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Croatia – Table HR.World Bank.WDI: Social: Health Statistics. Physicians include generalist and specialist medical practitioners.;World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.;Weighted average;This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/].