lenamax2355's starred repositories
cheatsheets
Posit Cheat Sheets - Can also be found at https://posit.co/resources/cheatsheets/.
1804_python_healthcare
pdf, py, and jupyter notebook files for https://pythonhealthcare.org/
healthyr_notebooks_materials
Scroll down this page for installation instructions, or see this poster:
documentation
Content for healthcare.ai, old posts, some hosted notebooks
clinical-decision-support
The aim of the project is to predict the mortality, disease progression and remaining length of stay in the ICU based on patients' admission EHR data. This will give hospitals advanced notice on the needs of their patients, and help physicians make safer decisions for their high-risk patients.
HealthCare-Analytics-Projects
These are projects for healthcare analytics. The projects are based on open data on health care.
HealthCareCaseStudyClouderaChallenge
Health Care Case Study Cloudera Challenge
PredictingHeartFailure
Application of serum creatinine and ejection fraction data along with common EHR patient data to predict patients at high risk for heart failure
EHR-data-Expected-Hospitalization-Time
Regression model on EHR to predict hospitalization time using TensorFlow
HealthCareStaffOptimization
Health Care Staff Optimization Problem (Data Science)
Investigate-Data
this project make EDA process for the health care dataset
DataInsights
Analytic Insights on Health Care Application
Data_health_care-
Health related repository
Health-Care-Competition
Women in Data Science Challenge
HealthCare-Data-Analysis-
Heathcare-Data Analysis
Stroke_probability_Health_care_data
This project demonstrates the Stroke probability using the Health Care Data. Basically this is a Decision Tree classification problem in which PySpark is used
EHR_Data_Analysis
EHR data analysis
emr_data_regression_model
Diabetes Drug Testing Project: Predicting hospitalization time with a regression/classification model
HealthCareAnalysis
In this analysis, I used a polynomial regression model to predict infant morality rate. I cleaned up some time series data and used them as features in the model.
Python-DataAnalysis-Health-Care-organsation---Report-Automation
Python codes for automation of reports and handling large data
Health-Care---Hackathon
The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data, health camps and records and the treatment of chronic diseases. MedCamp organizes health camps in several cities with low work life balance. They reach out to working people and ask them to register for these health camps. For those who attend, MedCamp provides them facility to undergo health checks or increase awareness by visiting various stalls (depending on the format of camp). MedCamp has conducted 65 such events over a period of 4 years and they see a high drop off between “Registration” and Number of people taking tests at the Camps. In last 4 years, they have stored data of ~110,000 registrations they have done. One of the huge costs in arranging these camps is the amount of inventory you need to carry. If you carry more than required inventory, you incur unnecessarily high costs. On the other hand, if you carry less than required inventory for conducting these medical checks, people end up having bad experience.
2020-Predicting_Patients_Mortality_Using_EHR_Data
This is the final project for big data analytics from Xiaoyue Zhang, Alex Wan, Chu Yu
HealthCareAnalytics
SMU Data Science Bootcamp
panda_workout_US_healthcare
Exploring Health Care data using Pandas: Where would you seek treatment?
HealthCare-Data-Science-Capstone
Problem Statement NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) research creates knowledge about and treatments for the most chronic, costly, and consequential diseases. The dataset used in this project is originally from NIDDK. The objective is to predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Build a model to accurately predict whether the patients in the dataset have diabetes or not. Dataset Description The datasets consists of several medical predictor variables and one target variable (Outcome). Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and more.