This is a guest post by Chris Hannam, a professional Python and Java developer. Want to contribute your own how-to post? Let us know contact us here.

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 Forecast and Simple Moving Average microservices from Algorithmia, and Plotly to graph the data.

tl;dr here’s the GitHub repo for running Algorithmia tasks against Withings data using Python.

What Gets Measure, Gets Managed

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.

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. I also wanted to be able to use predictive algorithms to forecast the future based on the past.

Here’s how we’ll build our own health dashboard instead.

Python, APIs, and Graphing

I set up a basic Flask app to fetch the blood pressure data from the Withings API, process the data, and graph it client side. To access the data, I used a Withings Python library (available on PyPi). For graphing, I choose Plot.ly. In just a few lines in the HTML you can quickly create powerful graphs.

The first task was to extract the raw data from Withings. Using the Python lib made this pretty simple. Where it got a little tricky was converting the fetched into something Plotly could graph. I went for a simple approach to build a string of text to render in a template using Jinja2 as part of Flask.

We’ll define our function to fetch the data from the Withings API (I’ve removed some code for brevity, but the repo has everything you need to get started). We call the Withings API to get our measurement data, and then iterate through the response to sort through the measurement dates and times. We’ll build up both an object to be used for graphing, as well as one of raw data we can pass to Algorithmia to run their predictive algorithms on.

def _fetch_withings(): results = [] client = WithingsApi(creds) readings = { # analysis of past readings 'past': { 'x': '', 'diastolic': '', 'systolic': '', 'pulse': '', 'simple_moving_average' : { 'diastolic': '', 'pulse': '', 'systolic': '', } } } measures = client.get_measures() last_reading_date = measures[-1].date counter = 1 raw_readings = { 'systolic': [], 'diastolic': [], 'pulse': [], } for measure in measures: if measure.systolic_blood_pressure and measure.diastolic_blood_pressure: next_date = last_reading_date + timedelta(days=counter) # sort out date times readings['past']['x'] += '"' + measure.date.strftime('%Y-%m-%d %H:%M:%S') + '",' readings['future']['x'] += '"' + next_date.strftime('%Y-%m-%d %H:%M:%S') + '",' readings['past']['systolic'] += str(measure.systolic_blood_pressure) + ',' readings['past']['diastolic'] += str(measure.diastolic_blood_pressure) + ',' # keep ints for for sending to ALGORITHMIA raw_readings['systolic'].append(measure.systolic_blood_pressure) raw_readings['diastolic'].append(measure.diastolic_blood_pressure) if measure.heart_pulse and measure.heart_pulse > 30: raw_readings['pulse'].append(measure.heart_pulse) readings['past']['pulse'] += str(measure.heart_pulse) + ',' counter += 1 return readings

As with most simple projects, Bootstrap made the perfect tool for rendering the HTML with the graphs embedded into the normal row layout.

To build the graphs, we create an object in the following format in our Flask app when fetching the Withings data:

readings = { # predictions 'future' : { 'x': '', 'diastolic': '', 'systolic': '', 'pulse': '', 'simple_moving_average' : { 'diastolic': '', 'pulse': '', 'systolic': '', } }, # analysis of past readings 'past': { 'x': '', 'diastolic': '', 'systolic': '', 'pulse': '', 'simple_moving_average' : { 'diastolic': '', 'pulse': '', 'systolic': '', } } }

And then, to generate the graph we pass Plot.ly the x and y coordinates from our data. We use x as the index, and y as the diastolic, systolic, or pulse value like so:

HISTORIC_FUTURE = document.getElementById('historic_future_graph'); Plotly.plot(HISTORIC_FUTURE, [ { name: 'Systolic', x: [{{readings.past.x|safe}}], y: [{{readings.past.systolic|safe}}] }, { name: 'Diastolic', x: [{{readings.past.x|safe}}], y: [{{readings.past.diastolic|safe}}] }, { name: 'Pulse', x: [{{readings.past.x|safe}}], y: [{{readings.past.pulse|safe}}] }, { name: 'Systolic Future', x: [{{readings.future.x|safe}}], y: [{{readings.future.systolic|safe}}] }, { name: 'Diastolic Future', x: [{{readings.future.x|safe}}], y: [{{readings.future.diastolic|safe}}] }, { name: 'Pulse Future', x: [{{readings.future.x|safe}}], y: [{{readings.future.pulse|safe}}] } ], { margin: { t: 0 } });

Now that we have our graphs, we can see that our blood pressure data has some unpredictable peaks, which makes trends hard to spot. I’ve used R for time series data in the past, but have never used anything in Python. This is where Algorithmia comes in.

Adding Predictive Algorithms

I needed to make sense of the data as easily as possible. I explored a few services that did machine learning and data analysis. Most were limited to text classification, expensive, or not available as a serverless API.

Then I found Algorithmia, which has a large library of algorithms that run as microservices on your data. You call the algorithm, pass in your data, they run the algorithm over it, and return the results in realtime. They have a Python library, and since it’s an API, it fit perfectly with this serverless project.

I chose two predictive algorithms for this project:

Using Simple Moving Average

I used simple moving average to smooth out the data, and make it easier to spot trends in the graph. Using the moving average also helped reduce the fluctuations and noise in the raw data.

First, we define our simple moving average function:

def _get_simple_moving_average(data): string = '' raw = [] reply = SIMPLE_MOVING_AVERAGE.pipe(data) for reading in reply.result: string += str(int(reading)) + ',' raw.append(reading) string = string[:-1] return string, raw

Then, as part of _fetch_withings() from above, we pass our systolic, diastolic, and pulse data to the function like so:

# simple moving average of existing data readings['past']['simple_moving_average']['diastolic'], average_diastolic = _get_simple_moving_average(raw_readings['diastolic']) readings['past']['simple_moving_average']['systolic'], average_systolic = _get_simple_moving_average(raw_readings['systolic']) readings['past']['simple_moving_average']['pulse'], average_pulse = _get_simple_moving_average(raw_readings['pulse'])

This creates a smoothed out chart of our vitals data. Then we use the forecast algorithm to project the trends into the future.

Using Forecast

This is where things get really fun. I had about five months of data to work with, and, generally speaking, the more data you have the better!

I first defined my forecast function:

def _get_forecast(data): string = '' raw = [] reply = FORECAST.pipe(data) for reading in reply.result: string += str(int(reading)) + ',' raw.append(reading) string = string[:-1] return string, raw

Again, as part of the _fetch_withings() function, I pass in the data as an array to the forecast algorithm. We could stop here, but the forecasted data would be prone to spikes and fluctuations. So, once that’s complete, I run the moving average algorithm on the forecasted data to smooth the results out:

if FORECAST_ON_AVERAGE: readings['future']['diastolic'], future_diastolic = _get_forecast(average_diastolic) readings['future']['systolic'], future_systolic = _get_forecast(average_systolic) readings['future']['pulse'], future_pulse = _get_forecast(average_pulse) else: # populate the standard graphs and get the raw data to feed into thenext algorithm readings['future']['diastolic'], future_diastolic = _get_forecast(raw_readings['diastolic']) readings['future']['systolic'], future_systolic = _get_forecast(raw_readings['systolic']) readings['future']['pulse'], future_pulse = _get_forecast(raw_readings['pulse']) # simple moving average of future data readings['future']['simple_moving_average']['diastolic'], average_diastolic = _get_simple_moving_average(future_diastolic) readings['future']['simple_moving_average']['systolic'], average_systolic = _get_simple_moving_average(future_systolic) readings['future']['simple_moving_average']['pulse'], average_pulse = _get_simple_moving_average(future_pulse)

Here’s the graphed results below showing the five months of systolic, diastolic, and pulse data, with a five month forecast for each:

And, here is the output from the forecast algorithm, but this time using the simple moving average data instead of the raw data:

Much better! Blood pressure data is hard to work from as can be quite erratic at times. There are a number of algorithms for smoothing and normalize the data, which I intend to use to improve the predictions in the future. For instance, I could have used Linear Detrend to focus the analysis on the fluctuations in the data, or Autocorrelate to analyze the seasonality of the time series. I could even use Outlier Detection to remove unusual data points in the raw data, which could indicate bad readings.

Conclusion

The main takeaway is that it appears my friends blood pressure isn’t going to get worse, and should stay within an acceptable range for the next few months.

And, thanks to their new health dashboard, my friend now has a set of graphs they can take to their doctor when discussing long term treatment. Blood pressure is something that can be influenced by a range of factors so regular reviews are important for long term management.

This was my first attempt at making forecasts and understanding the many, many ideas behind this kind data processing. I have barely scratched the service with what can be done with the data.

Tools used:

Want to build your own dashboard? Get the code from this Github repo.