Hosted currently at https://segunak.github.io/Mortality-Analysis/.
- To present a statistical based analysis of mortality in the United States.
- To summarize the trends and characteristics of mortality in the United States over the past decade, taking into account key factors such as race, age, and sex in relation to the cause of a persons death.
- To present a modeled example of how mortality data could be used to analyze medical documents, namely doctor patient transcriptions, with the goal of identifying medical conditions in patients that are directly tied to leading causes of death.
Every year, the center for disease control and prevention (CDC) provides detailed statistics on deaths and their underlying causes in the United States. This mortality data is used by various industries in medicine, health, insurance, and technology to provide better services to their customers and generate more helpful products for their users. The data provided by the CDC serves as the basis for a number of prominent research studies, and is frequently cited in prestigious academic and professional papers around the world. As data science & health enthusiasts, we took on the task of preparing a report that provides a detailed analysis of mortality in the United States. Specifically, our report provides an analysis of the CDC's annually published mortality data, starting in 2005 and ending in 2015. After drawing conclusions from the raw data, we provide statistically backed prognostications on where mortality rates in the United States may be headed in the future, and how that data could be practically applied to the operations of health conscious organizations.
Furthermore, in the spirit of highlighting the value of mortality data in medicine, we performed a cross analysis connecting mortality data to sample medical transcriptions (of doctor-patient visits). This juxtaposition will drew relationships between the leading causes of death and descriptions of patient wellness, as taken from notes written by doctors during their visits. The medical transcriptions used in our analysis were entirely fabricated, as real doctor-patient notes are private and protected by HIPPA regulations. Our goal was simply to provide an example of what value could be generated if a firm were to legally have access to doctor-patient notes.
All in all, the efforts put forth in completing this project were a part of the exploration and practice of data science and analytics.