Using Predictive Algorithms to Track Real Time Health Trends

Using Predictive Algorithms to Track Real Time Health Trends

Using Predictive Algorithms to Track Real Time Health Trends
In this post we see how to build a real-time health dashboard for tracking a person’s blood pressure readings, do time series analysis, and then graph the trends over time using predictive algorithms.

We’ve shown how to use predictive algorithms to track economic development. In this tutorial, we’re going to build a real-time health dashboard for tracking a person’s blood pressure readings, do time series analysis, and then graph the trends over time using predictive algorithms. This tutorial is the starting point for creating your own personal health dashboard using time series algorithms and predictive APIs.

We’ll be creating this dashboard in Python, using the Withings API for our data, the Forecastand Simple Moving Average microservices from Algorithmia, and Plotly to graph the data.

  Why blood pressure data? A friend of mine was diagnosed with high blood pressure and was determined to lower it using data. According to CDC statistics as many as 1 in 3 Americans suffer from high blood pressure, which can contribute to a higher risk for heart disease and stroke.

I’m a Python programmer, and thought I could build a simple, serverless health dashboard to help my friend measure and understand his blood pressure.

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The first step was to establish a routine of measuring the blood pressure and logging it using a cheap blood pressure monitor and the Withings app. We’ll then use the Withings API to access our data for the health dashboard (Withings also makes a wifi-enabled blood pressure cuff for those that don’t want to manually log their data).

My friend has been logging their heart rate, systolic and diastolic blood pressure in the morning and night for the last five months. Below is a snapshot from the dashboard offered by Withings.

The graphs are OK, but we both found them confusing and not very helpful for tracking trends.

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