全球十大死亡因素

[wanghaisheng]

Child and Adolescent Health From 1990 to 2015Findings From the Global Burden of Diseases, Injuries, and Risk Factors 2015 Study Global Health Estimates 2015: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2015. Geneva, World Health Organization; 2016.

[wanghaisheng]

Disease prediction has become important in a variety of applications such as health insurance, tailored health communication and public health. Disease prediction is usually performed using publically available datasets such as HCUP, NHANES or MDS that were initially designed for health reporting or health cost evaluation but not for disease prediction. In these datasets, medical diagnoses are traditionally arranged in "diagnose-related groups" (DRGs). In this paper we compare the disease prediction based on crisp DRG features with the results obtained employing a new set of features that consist of the fuzzy membership of patient diagnoses in the DRG groups. The fuzzy membership features were computed using an ICD-9 ontological similarity approach. The prediction results obtained on a subset of 9,000 patients from the 2005 HCUP data representing three diseases (diabetes, atherosclerosis and hypertension) using two classifiers (random forest and SVM trained on 21,000 samples) show significant (about 10%) improvement as measured by the area under the ROC curve (AROC). https://github.com/scimk/website/blob/20349ad0c2057d2ad3c550861ea92068cbb0c3ed/content/publication/disease-prediction-ontology.md https://github.com/himajavadaga/Prudential-Life-Insurance-Kaggle-Dataset

https://github.com/kkandhas/Survival-Rate-prediction-of-Breast-Cancer-Patients-SAS-

https://github.com/MattD18/Healthcare-Information-System Our project goal is to develop a tool to provide hospital patients with predictions for their expected outcome, duration of stay at the hospital, and total cost, given their specific diagnosis. To do this we will be examining hospital discharge data from the year 2012, which also includes demographic information such as patient age, gender, and race, as well as hospital information like geographic location. We think that this is a relevant dataset to examine because we feel there may likely be a correlation between these demographic factors and our target predictators.

https://github.com/easwerc/HealthInsurancePlan https://xcitech.github.io/tutorials/travelers/ https://github.com/TorrBorr/health-care-cost-estimator

[wanghaisheng]

health care cost prediction health cost evaluation Disease risk prediction Payment or premium adjustment Health Risk Assessment

Health Insurance Plan

How to Use
Compare Plans
Predict Clinic Visits & Expenses

[wanghaisheng]

Identifying Future High Cost Individuals within an Intermediate Cost Population https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874657/ http://www.aha.org/content/00-10/MillimanReport030410.pdf http://cs229.stanford.edu/proj2012/Lui-EmployerHealthInsurancePremiumPrediction.pdf https://www.nhpf.org/library/issue-briefs/IB798_CostSharing.pdf https://www.actuary.org/pdf/health/Risk_Adjustment_Issue_Brief_Final_5-26-10.pdf Health Risk Assessment https://www.calpers.ca.gov/docs/board-agendas/201302/pension/item-9a-attach-1.pdf

[wanghaisheng]

CRisk Adjustment of Insurance Premiums in the United States and Implications for People with Disabilitieshttps://www.ncbi.nlm.nih.gov/books/NBK11417/