Machine Learning and Internet of Things The Future of Medical Prevention

Introduction

Dataiku

•  Founded in 2013 •  60 + employees •  Paris, New-York, London, San Francisco

Data Science Software Editor of Dataiku DSS

DESIGN

Load and prepare your data

PREPARE Build your

models

MODEL Visualize and share

your work

ANALYSE

Re-execute your workflow at ease

AUTOMATE Follow your production

environment

MONITOR Get predictions

in real time

SCORE PRODUCTION

Epilepsy Stats and figures

1-­‐3%   of  the  popula/on  

15.5  billion   Euros  /  year  spent  trea/ng  seizures  

6   Types  of  epilepsy  

Dozens   of  exis/ng  treatments  

Days  to  weeks   of  hospital  /me  required  to  diagnose  

Epilepsy + IoT Faster, more comfortable diagnosis

+

EEG Seizures on an EEG

EEG Spikes on an EEG

Goals Improve Epilepsy Diagnosis

1.  Allow at-home EEG recording via wearable device

2.  Detect seizures automatically

3.  Detect spikes automatically

4.  Shorten time-to-diagnosis for patients with epilepsy

Ageing Stats and figures

x3   Over  last  60  years  

12  million   fall  every  year  in  the  U.S  

700  million   People  older  than  60  in  2006  

28.5  %   of  this  popula/on  leave  alone  in  the  EU  

Third  leading   Cause  of  death  :  strokes.  

3/4   of  all  strokes  happen  to  people  over  65  

Safe Aging with Sphere Improve Falling Detection

Credit Aakansh Gupta : http://datascience.blog.uhuru.co.jp/machine-learning/safe-aging-with-iot-and-machine-learning/

Goals Improve Falling Detection

1.  Predict falls and detect strokes so that help may be summoned

2.  Analyse eating behaviour - including whether people are taking

prescribed medication

3.  Detect periods of depression or anxiety and intervene using a computer

based therapy

A data science workflow Six steps to a predictive model

Data Exploration &

Understanding

Data Preparation Model Creation

Evaluation Deployment Data

Acquisition

Dataset 1

Scored dataset

Model as an API

Iteration 1 Iteration 2

Iteration n

Creating a predictive model is an highly iterative process. Data Science Studio enables its users to create and manage these projects from end-to-end. This process is not industry specific, and can be applied to many use cases.

Dataset 2

Dataset n

Business/Problem Understanding

Adapted from the CRISP-DM methodology

Data Acquisition / Preparation

The concept We’re taking a digital sample of an analog signal

Data Collected True Signal

The Data

1024 Hz x 24 channels = 353 Mb / (hour x patient)

20 patients X 24 hours = 170 Gb

Nightly transfers of data from device to cloud (via wifi)

We want to scale to hundreds of patients with

days of data

Epilepsy

The Data •  Accelerometer - Sampled at 20 Hz;

•  RGB-D - Bounding box information

•  Environmental - The values of passive infrared (PIR) sensors

Safe Aging

The Data Needs

•  Interpolation •  Missing data, synchronization fail

•  Smart Sampling •  Zoom at different frequency levels

•  Different sensors -> different frequency.

-> how to merge ?

•  Aggregation

Time Series as Relational Data

Time Stamp 10001 10002 10003 10004 10005

Sensor1 40 - - 43 42

Sensor2 - 50 55 20 -

Sensor3 30 34 60 - 40

Aggregation

Resampling

Interpolation

Time Series as JSON

{ "sensor1": { 10001: 40, 10004: 43, 10005: 42 }, "sensor2": { 10002: 50, 10003: 55, 10004: 20 } … }

Aggregation

Resampling

Interpolation

Time Series as Time Series Time Series Database

Aggregation

Resampling

Interpolation

Signal Processing Lots of libraries, lots of options

Rename Generate Rolling mean Rolling max Rolling min Rolling median Wavelet decomposition STL decomposition Peak detection Low pass filter High pass filter Convolution Correlation Short-time FFT

Implemented with common interface

+

+

Machine Creation and Evaluation

Machine learning Features

•  Descriptive features Epilepsy   Pa/ent  informa/on  :  EMR…  

Safe  aging   EMR,  age,  height  

•  Time series features

Epilepsy   Current  values,  previous  values,  correla/ons,  Fourier,  Wavelets,  …  

Safe  aging   Current  values,  previous  values,    rolling  averages,  …  

Machine learning Features

•  More data Means less feature engineering

Safe  aging  (lot’s  of  values)    

Xgboost  on  current  and  previous  values:  Let  the  model  find  the  interac/ons  

Epilepsy  (millions  of  lines)   RNN,  LSTM.  Network  Architecture  =  Feature  engineering  

Training, Testing, Validation 4 patients, 4 readings from each patient

Split 1: Awesome performance 4 patients, 4 readings from each patient

Training Testing

AUC = 0.94

Split 2: OK performance 4 patients, 4 readings from each patient

Training Testing

AUC = 0.70

Worries About our spike detection model

Poor generalization to new patients

What about new devices?

What about different doctors creating annotations?

Solution: more patients, more doctors, more devices

Worries About our position detection model

Average generalization to new patients

What about new devices?

What about different home / rooms ?

Data Solution : more patients, more houses, more devices

Practical Solution : warm start with house + person. Expensive

Deploy

Deploy

•  Model Deployment Epilepsy  Diagnosis   Batch  scoring  on  all  record  a_er  X  days  

Epilepsy  Spike  detec:on   Batch  scoring  (used  for  diagnosis)  

Epilepsy  seizure  detec:on   Real  /me  scoring  

Safe  aging   Real  /me  scoring  every  second  

Theory

•  Maintain your feature flow !

Deploy

•  Don’t underestimate real life conditions

•  Anomalies

•  Headset in wrong position

•  Bracelet in wrong hand

•  Hardware / sensors deficiency

Practice

Summary

Summary

•  IoT devices can improve early detection (epilepsy, fall,…) •  IoT devices produce lots of data – use databases made for IoT •  Standard workflow – acquire, visualize, prepare, model – can be replicated for IoT devices using open source

software •  Differences between patients remains a challenge for prediction algorithms

IoT devices for medical applications

@prrgu/errez