Graph-Based Machine Learning for Automated Health Care Services

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Graph-Based Machine Learning for
Automated Health Care Services
Rhicheek Patra, Oracle Labs Zürich
Email: rhicheek.patra@oracle.com
Twitter: @rhpatra, LinkedIn: @rhicheek-patra
Swiss Conference on Data Science 2019

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Program Agenda
Benefits of graph models
HealthCare: Background & Problem Statement
KG-RNN: Overview
Empirical Validation
Concluding Remarks
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Program Agenda
KG-RNN: Overview
Concluding Remarks
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Your Data is a Graph!
• Represent your dataset as a
graph
– Entities are vertices
– Relationships are edges
• Annotate your graph
– Labels identify vertices and edges
– Properties describe vertices and
edges
Patient
name = ‘Jerald’
Admission
id = 1234
venue = ‘Triemli’
was_admitted
date = 01-23
Diagnosis
Procedure
diagnosed _with

Benefits of Graph Models
• Some of graph benefits
– Intuitive data model
– Aggregate information over heterogeneous data sources
– Fast query over multi-hop relationships
– Data visualization and interactive exploration

• A fast, parallel, in-memory graph analytics framework
• Offers 35+ built-in, native graph analytics algorithms
• Provides a graph-specific query language (PGQL)
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SELECT v, e, v2
FROM graph
MATCH (v)-[e]->(v2)
WHERE v.first_name ='Jerald’
AND v.last_name = 'Hilpert'
GROUP BY … ORDER BY … LIMIT …
Parallel Graph AnalytiX (PGX)

Graph in Various Domains
Health Science Financial Services
Patient
Admissions
Diagnosis
Procedures
Customer
Account
Address
Alert
Institution
ICIJ
Worldcheck

Program Agenda
KG-RNN: Overview
Concluding Remarks
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Knowledge Graphs
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• Represent information as entities, properties and
relations between entities
• Allows to naturally combine information from
different sources
• Lot of activity and research around leveraging
knowledge graphs in Machine Learning

Sample: Healthcare Knowledge Graph
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Sample: Retail Knowledge Graph
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Features of the HealthCare Knowledge Graph
• New relations updated in the Knowledge Graph (KG) over time
– Due to unseen disease-symptom analysis over new medical studies
– Due to incoming Electronic Medical Records (EMRs) for new medical admissions
• Dynamic properties of entities (a.k.a. “admissions” in the KG)
– Patient Admissions vary over long duration (few hours to few weeks)
– During the admission, patient goes through lots of different
• Medications: e.g., Aspirin (500mg/day)
• Lab tests: e.g., pH=7.4 at 15:35
• How do we construct such KGs?
– Internal information (e.g., admissions) aggregated over hospital databases
– External information (e.g., disease-symptoms) collected from public resources
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What is the Learning Objective?
• Given an admission with all the dynamic medical inputs (e.g., medications,
lab results), how can we predict the diagnoses related to this admission?
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1. Hypertension
2. Heart disease

Program Agenda
KG-RNN: Overview
Concluding Remarks
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How do we achieve this Learning objective?
• Model the problem as a temporal sequence one (split by N-hour intervals)
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Now we can train a Recurrent Neural Network for this objective!

Recurrent Neural Network and how do we employ it?
• Used in multiple sequence-based applications
– Stock Market Prediction
– Machine Translation
• Recurrent Neural Network requires the temporal states of the input x
(which is admission in our case)
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Input to the RNN: Admission Timeline Chunks
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Admission Training (Admission Encoder Module)
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AggregatorAggregator
Chunk 1 Chunk N

Where can the Knowledge Graph benefit in our objective?
• To feed the relations between previous “relevant” admissions (based on
some admission properties, e.g., Pre-diagnosis) and their diagnoses.
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Relevant Admission Extraction
• Nearest Neighbor problem
– Given an input Admission, find the top-K nearest Admission in the healthcare
knowledge graph
• Approach: Weighted Personalized PageRank
– Weights
• Disease – Symptom: Importance of Symptom for specific disease
• Admission – Symptom/Disease: Cosine Similarity w.r.t. Pre-diagnosis and Symptom/Disease
– Take top-K admissions as the most relevant one w.r.t. WPPR score
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Relevant Admission Extraction
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How do we employ the neighbors information? – KG-RNN
• Encoding for Input Admission
– Obtain admission encoding for the input admission (similar to the baseline)
• Aggregation of neighbors diagnoses information
– Aggregate the diagnoses information of the neighbors and concatenate with the
encoding of the input admission
• Diagnosis Prediction
– Employ the concatenated vector for the diagnoses prediction
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KG-RNN Prediction Module
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Input Admission
Final Encoding
Neighbor 1 Final
Encoding
Neighbor M Final
Encoding
Prediction Module
Diagnoses

Program Agenda
KG-RNN: Overview
Concluding Remarks
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Dataset: MIMIC-III
• Available at https://mimic.physionet.org/
• MIMIC is a relational database containing data of patients who stayed
within the intensive care units at Beth Israel Deaconess Medical Center
• Input: Admissions
• Output: Multi-label multi-class classification task
– We focus on top-50 most frequent ones
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Type Patients Admissions Input events Output events Lab tests Prescriptions
Count 46,520 58,976 21,000,000 4,500,000 28,000,000 4,000,000

Results
• Incorporating Neigbors information boosts quality of KG-RNN on MIMIC-III
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Metric Average Model Score
AUROC
Macro
Baseline 85.24%
KG-RNN 86.29% (+1.05%)
Micro
Baseline 90.55%
KG-RNN 91.03% (+0.48%)
Properties
3 hours per chunk
200 chunks per admission
25 events per type and per chunk
10 neighbors sampled per input admission

Sample example of Neighbor-boosting
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• Baseline does not detect any
diagnosis
• Neighbor helped pushing
the confidence of KG-RNN
upward for 2nd diagnosis
• Reveals how much KG-RNN
relies on its neighbors

Prediction Timeline
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• Our approach provides real-time
diagnosis probabilities during an on-
going diagnosis leading to better
medications and treatments.

Program Agenda
KG-RNN: Overview
Concluding Remarks
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Summary
• Novel approach for processing evolving Knowledge Graphs with GraphML
• Multiple Applications in HealthCare, Retail, Finance, Security
• Online articles for detailed read
– https://blogs.oracle.com/ai/introduction-to-knowledge-graphs-in-healthcare
– https://blogs.oracle.com/ai/graph-machine-learning-for-enhanced-healthcare-
services
• Tools
– PGX: https://docs.oracle.com/cd/E56133_01/latest/index.html
– PGQL: http://pgql-lang.org/
– Github repository: https://github.com/oracle/pgx-samples
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