Applying Machine Learning to Live Patient Data

®© 2017 MapR Technologies 1 ® 1 MapR Confidential © 2017 MapR Technologies

®

Applying Machine Learning to Live Patient Data Carol McDonald (@caroljmcdonald) & Joseph Blue (@joebluems) March 15, 2017

®© 2017 MapR Technologies 2 ® 2 MapR Confidential

Data-Driven Experience


The Promise of Big Data in Healthcare

SMARTER BIGGER FASTER


Life moves pretty fast. If you don't stop and look around once in a

while, you could miss it. Ferris Bueller, Fictional High School Student


Reading an EKG

P

Q

R

S

T atrial

depolarization

ventricular depolarization

ventricular repolarization


Windowing the EKG for Clustering

window length = 32, step size = 2


Displaying Centroids

Showing 25 of K=400 centroids

Begin reconstruction


Reconstructing the Signal

1 2

1

2

+

window length = 32, step size = 16


Diagnosing the Anomalies

residuals


Putting it all together…

shape catalog

input reconstruct

encoder t-digest

error

quantile estimator

®© 2017 MapR Technologies 11 ® 11 MapR Confidential © 2016 MapR Technologies © 2017 MapR Technologies

Use Case Architecture


Lots of things are producing Streaming Data

Data Collection Devices

Smart Machinery Phones and Tablets Home Automation

RFID Systems Digital Signage Security Systems Medical Devices


Consumers

MapR Cluster

Topic: Admission / Server 1



Consumers

Consumers

Partition

1

Partition

2

Partition

3

6 5 4 3 2 1

3 2 1

5 4 3 2 1

Producers

Producers

Producers

Streams capture unbounded sequences of events

Old Message

New Message

Events are delivered in the order they are received, like a queue.

Kafka API Kafka API


Stream Topics Organize Events into Categories

Consumers

Consumers

Consumers

Producers

Producers

Producers

MapR-FS

Kafka API Kafka API

Unlike a queue messages are not deleted, allows processing of same event for different views


Predictive Analytics

Machine Learning

Algorithms

Test Model Predictions

Model Evaluation

Predictive Model Predictions

Model Building

Model scoring

Featurization

Historical Data

+ + +

+ + +

New Data

Stream

Topic


Stream Processing Architecture Serve Data Collect Data Data Sources Stream Processing

Derive features

process

Batch Processing

Model

build model update model

Machine-learning

Models

Devices

Feature extraction

Stream

Topic

Images

HL7

Social Media

lab

Stream

Topic


// put data in a vector val vrdd = rdd.map(line => Vectors.dense(line.split('\t').map(_.toDouble))) //window and normalize each record.... // call Kmeans , which returns the model val model = KMeans.train(processed, 300, 10) model.save(sc, "/user/user01/data/anomaly-detection-master")

Build Model


Use the Model with Streaming Data


Use Case: Real Time Anomaly Detection

real-time monitoring

read

EKG data

Spark processing enrich with cluster normalized data

Spark Streaming

Stream

Topic

Stream

Topic

17.9200 12.8000 38.4000 {”c":120,"colA":[17.92, 12.88, ..],"colB":[17.91, 12.89, 0...]}


Create a DStream

DStream: a sequence of RDDs representing a stream of data

val model = KMeansModel.load(ssc.sparkContext, modelpath) val messagesDStream = KafkaUtils.createDirectStream[String, String]( ssc, LocationStrategies.PreferConsistent, consumerStrategy )

batch time 0 to 1

batch time 1 to 2

batch time 2 to 3

dStream

Stored in memory as an RDD

Stream

Topic


Process DStream // get message values from key,value val valuesDStream: DStream[String] = messagesDStream.map(_.value()) valuesDStream.foreachRDD { rdd => val producer = KafkaProducerFactory.getOrCreateProducer(conf) .... // enrich message with model val cluster = model.predict(processed) .... val record = new ProducerRecord(topicp, "key", message) // send enriched message producer.send(record) } }


Process DStream

dStream RDDs

batch time 2 to 3

batch time 1 to 2

batch time 0 to 1

ValueDStream RDDs

Transformed RDDs

map map map Stream

Topic


Use Case: Real Time Anomaly Detection

real-time monitoring

read

Spark processing enrich with cluster normalized data

Spark Streaming

Stream

Topic

Vert.x HTTP

Event bus

WebSocket Event Bus Framework

{”c":120,"colA":[17.92, 12.88, ..],"colB":[17.91, 12.89, 0...]}

{”c":120,"colA":[17.92, 12.88, ..],"colB":[17.91, 12.89, 0...]}



Resources •  EKG basics - http://en.wikipedia.org/wiki/Electrocardiography •  Source data -

http://physionet.org/physiobank/database/apnea-ecg/ •  K-Means basics -

http://www.coursera.org/learn/machine-learning/lecture/93VPG/k-means-algorithm

•  Code repositories –  Streaming: http://github.com/caroljmcdonald/sparkml-streaming-ekg –  UI: http://github.com/caroljmcdonald/mapr-streams-vertx-dashboard

•  t-digest for anomalies - http://github.com/tdunning/t-digest


e-book available courtesy of MapR

https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection

A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman (published by O’Reilly)


MapR Blog mapr.com/blog


Q & A

@mapr

Engage with us!

mapr-technologies

Carol McDonald (@caroljmcdonald) Joseph Blue (@joebluems)

Software

Applying Machine Learning to Live Patient Data