Mining medical device logs to improve operational ... · Overview. 2. Siemens – Who we are 3...

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#TDPARTNERS16 GEORGIA WORLD CONGRESS CENTER

Mining medical device logs to improve operational efficiency at Siemens HC

Bruce Baum – SiemensMike Watzke – Teradata Labs

• Siemens Introduction• Business Problem• Technical Solution

Overview

Siemens – Who we are

Electrification, Automation and Digitalization are long-term growth fields of Siemens.

Power and Gas Wind Power and Renewables Power Generation Services

Energy Management Building Technologies Mobility

Digital Factory Process Industries and Drives Healthcare

Shifting markets drive need for answers

Business Problem

1) Business problem & data overview

2) The story so far: Classical machine learning

3) Towards pattern mining for imaging devices

4) Excursion: The Siemens compute environment

Remote Diagnostics @ Healthcare

• Siemens Healthineers medical imaging devices are used all across the world in a demanding market:

• Minimize downtimes. Just imagine…• … doctors puzzling over blurred images• … an ER room

• Minimize maintenance cost (personnel, material)

• Siemens answer: Remote monitoring & diagnostics• Goal: Exchange unplanned for planned downtime• Technology: Predictive maintenance (min. 3 days)• Critical constraint: False alarms not accepted

Data at a Glance (CT Example)

• Regulatory constraints require focus on already existing data sources:

• Device logs• Time-stamped sequence of events• >100m lines per device & year

• Parts exchange data• Calls to service center (+ exam results)• <10 faults per device & year

The story so far: Classical ML

timestamp source code text

2014-05-17 11:31:12 A 37 xxx

2014-05-17 11:31:12 B 42 yyy

2014-05-17 11:31:13 B 17 .. hi temp (37.5) in ..

Device Logs

2014-05-17 11:31:12 A 37 xxx

2014-05-17 11:31:12 B 42 yyy

2014-05-17 11:31:13 B 17 .. hi temp (37.5) in ..

device episode f1 f2 … f10000

55049 4711 37 3.45 true

55049 4712 42 ? false

55049 4713 17 3.12 true

Feature Matrix

Device Logs

Feature Extraction

• Event counts• Statistics for

extracted values• Derived features

(grouping/ scaling/ trends/ …)

• Different time bins (day/ scan/ …)

2014-05-17 11:31:12 A 37 xxx

2014-05-17 11:31:12 B 42 yyy

2014-05-17 11:31:13 B 17 .. hi temp (37.5) in ..

55049 4711 37 3.45 true

55049 4712 42 ? false

55049 4713 17 3.12 true

Analytical Models

Feature Matrix

Device Logs

Feature Extraction

Model Training

• Prediction horizon• Model selection• Feature selection

Parts Exchange Data

2014-05-17 11:31:12 A 37 xxx

2014-05-17 11:31:12 B 42 yyy

2014-05-17 11:31:13 B 17 .. hi temp (37.5) in ..

55049 4711 37 3.45 true

55049 4712 42 ? false

55049 4713 17 3.12 true

Analytical Models

Feature Matrix

Device Logs

Feature Extraction

Model Training

• Prediction horizon• Model selection• Feature selection

Parts Exchange Data

High-quality modelsfor current use cases

New offers need evenfewer false alarms

Why temporal patterns?

CT log data is a “flattened” representation of overlapping processes!

AcquirePrepare StoreProcessPatient 12345

2014-05-17 11:31:12 A 37 xxx

2014-05-17 11:31:12 B 42 yyy

2014-05-17 11:31:13 B 17 .. hi temp (37.5) in ..

First steps to pattern mining (1/2)

Domain-specific pattern-mining algorithm in Java

resilience to“stray events”from parallel processes

anytime-capability

support same-timeevents, includingrandom order of

“almost same time”

user-definedquality functions

X Scalability not sufficient for use case (transfer, processing)

First steps to pattern mining (2/2)

Using Aster nPath features, instrumented with KNIME

X Millions of nPath calls, no generation in-DB, expensive grouping operation needed!

The Siemens Smart Data Lab

Data analytics

Data presentation

Data Warehouse

Hadoop

Data integration

… and others

4 nodes148 virtual units11 TB storage

Per node:• 24 cores• 256 GB RAM

Technical Solution

1. Overview

2. Sequence mining algorithm and implementation

3. Experimental data and demographics

4. Data preparation

5. Sequence mining training

6. Pattern scoring

7. Findings

Overview

• Hypothesis: temporal sequences of events can be used to provide early warning of failures?

• Test hypothesis with an experiment• Computed Tomography (CT) device logs• New sequence mining machine learning algorithm• Pattern scoring function

• Prior work: With the large volume of data and large pattern search space standard sequence mining approaches failed to work

Related Sequence Mining Work

• Frequent pattern mining (FP-growth, FrequentPaths) and Association Rules

• Frequency is not necessarily correlated to failure• Subgroup Discovery: related to above but allows for a more

flexible definition of the quality metric (frequency, unexpected, discriminating, ..)

• Temporal ordering of events is not considered

• Pattern Matching: requires patterns as inputs, in this context a function such as nPath would be more appropriate for scoring

Sequence Mining Definitions

Devices

FailureTime

BDFF A

ABACDABFEBDBACCEBDFAC

Event alphabet (A-F)

Sequence 1

Sequence 2

Events Pattern

Sequence Mining Algorithm

• Supervised machine learning algorithm, data classes are categorized based on time to failure

• Exhaustive iterative breadth first search of event pattern space. Search space size is an exponential function of depth

• Matching based on solving a Constraint Satisfaction Problem• Searching space pruning

• Quality Metric, Positive Predictive Value (PPV)• Sequence match counts• Prior matched sequences<->patterns• Terminate expansion of patterns with PPV = 1.0 (monotonic)

Sequence Mining Algorithm

Iteration 2, 32 Patterns

Iteration 1, 31 Patterns|Events| = 3Search Space

PPV = 0.93, matched 93 class 1 and 7 class 0Level 4 Pruning Example

PPV = 0.48, matched 48 class 1 and 52 class 0

. . . . . .

Algorithm Matching

• Matching a pattern to a sequence is based on solving a Constraint Satisfaction Problem

• Is (BDF) a subsequence of input sequence?• Constraints to be solved:

• Time(B,D) < Forward Gap OR Time(D,B) < Backward GapAND • Time(D,F) < Forward Gap OR Time(F,D) < Backward Gap

• Other matching approach would be a finite automata

Teradata DBS Implementation

PatternsSequences

Sequences (~Map)

Patterns(Vector)

Expand/ CSPMatch

Global (by pattern) Score and Prune

DuplicatedHashed

Sequence : Pattern

Nth instance of Global Score and Prune

Nth instance of Local Expand and Match

AMP 1 AMP NHashed

Experiment Data

• 350,000,000 CT device events• Data Record: {device, time, event,

class label}• Training data set (60% sample),

Validation data set (40%)• ~2,000 distinct Events

Experiment Data Demographics

• Event distribution

• X: Daily event count per device

• Y: frequency of specific daily event count

Experiment Data Preparation

• Generating Episodes

• Additional transformations• Timestamp to Epoch • Event string to numeric identifier• Event pruning; know error events, only events that

occur in failure window

Experiment Model Training

Sequences

Quality Metrics

Iterative Search Execution

Search Control

Patterns

Pattern <-> Sequences

Inputs Outputs

Depth=15~2B patterns

Experiment Model Scoring

• Metrics: precision and recall• Precision (PPV) = TP / (TP + FP)• Recall = TP / (TP + FN)

• Device and Episode match counts

Sequence MatchedFP TP

FNTN Sequence Not Matched

Experiment Findings

50 Iterations of Train and Score using 60%/40% Samples

21 NAs

Experiment Findings

• Very high precession values can be achieved at the expense of recall.

• Configurable PPV per search depth iteration is useful• Common subpattern elimination

• Results from events occurring at same epoch and backward / zero delta support

• Episode support metric contributes to precision• Additional use cases being considered

• sensor data from power generation devices

Thank You

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mike.watzke@teradata.com

Mining medical device logs to improve operational ... · Overview. 2. Siemens – Who we are 3...

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