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AAAI-19-GAMENet revised

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GAMENet
Graph Augmented MEmory Networks for
Recommending Medication Combination
3/13/19
GAMENet
Outline
 Recommending Medication Combinations
 Existing Works
 Graph Augmented MEmory Networks (GAMENet)
 Experiments
2
GAMENet
Outline
 Recommending Medication Combinations
 Existing Works
 Graph Augmented MEmory Networks (GAMENet)
 Experiments
3
Medication Errors and
Adverse Drug-drug
Interactions
10 percent of all U.S. deaths are now due to medical error
3rd highest cause of death in the U.S. is medical error
15 percent U.S.
population. Cost more than $177 billion per year in disease
Adverse drug-drug interactions affects
management
https://www.hopkinsmedicine.org/news/media/releases/study_suggests_medical_errors_now_third_leading_cause_of_death_in_the_us
4
GAMENet
Recommending Medication Combinations
Medication Recommendation takes patient history (represented
by medical codes of medication, diagnosis, and procedures) as
input, and outputs a set of medications for the current visit.
Patient history
predict a set of medications
Dx, Rx, and CPT codes
5
GAMENet
Challenges for medication recommendation
Medications
Diseases
Complex
Dependencies
Drug-drug
Interaction
Patient
History
predict
past history
Dx, Rx, and
CPT codes
future
Rx
6
GAMENet
Outline
 Recommending Drug Combinations
 Existing Works
 Graph Augmented MEmory Networks (GAMENet)
 Experiments
7
GAMENet
Existing Works
Complex
Dependencies
Avoid DDI
Leap (KDD’ 17)
✓
✓
DMNC (KDD’ 18)
✓
✓
high
L2P (ICLR’ 16)
✓
✓
high
RETAIN (NIPS’ 16)
✓
✓
high
✓
high
GAMENe
t
✓
✓
Longitudinal
Personalized
low
8
GAMENet
Outline
 Recommending Drug Combinations
 Existing Works
 Graph Augmented MEmory Networks (GAMENet)
 Experiments
9
GAMENet
Graph Augmented Memory
Networks (GAMENet)
Medical Embedding &
Patient Representation
Module
Graph Augmented
Memory Module
(I, G, O, R)
Training and
Inference
10
GAMENet
Medical Embedding & Patient
Representation Module
Input
𝒄𝑡∗
Embedding
𝒆𝑡∗ = 𝑾∗,𝑒 𝒄𝑡∗
Patient Representation
[𝒉𝑡𝑑 , 𝒉𝑡𝑝 ]
Output
𝑡
𝑡
𝒄𝑡∗ : multi-hot vector of medical codes 𝒉𝑡𝑑 = 𝑅𝑁𝑁𝑑 𝒆1𝑑 , ⋯ , 𝒆𝑡𝑑 (diagnosis) 𝒉𝑑 , 𝒉𝑝
𝑾∗,𝑒 : embedding matrix to learn
𝒉𝑡𝑝 = 𝑅𝑁𝑁𝑝 (𝒆1𝑝 , ⋯ , 𝒆𝑡𝑝 ) (procedure)
11
GAMENet
Graph Augmented Memory Module (I, G, O, R)
Inspired by Memory Networks, we propose graph augmented
memory network that comprises of memory components I, G, O, R.
12
GAMENet
Graph Augmented Memory Module (I, G, O, R)
I (input)
G
Generalization
O R
Output Response
𝒒𝑡 = 𝑓 𝒉𝑡𝑑 , 𝒉𝑡𝑝
Input medical embedding ℎ𝑑𝑡 , ℎ𝑝𝑡 , output patient query 𝑞 𝑡 .
13
GAMENet
Graph Augmented Memory Module (I, G, O, R)
G (generalization)
I
Input
Memory Bank
Dynamic Memory
Memory Bank (MB)
Augmented by EHR graph (combined
use of medications) and DDI graph.
O R
Dynamic
(DM)
Output Memory
Response
Key-value pairs, where keys are
patient query 𝑞𝑡 , value are
dynamically updated patient history.
14
GAMENet
Graph Augmented Memory Module (I, G, O, R)
Memory Bank (MB)
Input adjacency matrix for EHR
graph and DDI graph 𝑨∗
1
−2
𝑫
1
−2
𝑨∗ =
𝑨∗ + 𝑰 𝑫
𝒁1 = 𝑨𝑒 tanh 𝑨𝑒 𝑾𝑒1 𝑾1
𝒁2 = 𝑨𝑑 tanh 𝑨𝑑 𝑾𝑒2 𝑾2
𝑴𝑏 = 𝒁1 + 𝛽𝒁2
Output improved embedding 𝑴𝑏 .
15
GAMENet
Graph Augmented Memory Module (I, G, O, R)
Dynamic Memory (DM)
16
GAMENet
Graph Augmented Memory Module (I, G, O, R)
O (output) R (response)
I
G
Generalization
Input
Given patient query 𝒒𝑡 , the O performs attentional memory
retrieval output 𝒐𝑡𝑏 from MB and output 𝒐𝑡𝑑 from DM. Then R
predicts a set of medications 𝑦𝑡 = 𝜎([𝒒𝑡 , 𝒐𝑡𝑏 , 𝒐𝑡𝑑 ])
17
GAMENet
Outline
 Recommending Drug Combinations
 Existing Works
 Graph Augmented MEmory Networks (GAMENet)
 Experiments
19
GAMENet
Experiments
Patient Record
Gold-standard DDI Knowledge
TWOSIDES
Top-40
severe
DDIs
 Patient more than one visit.
 Medication during the first
24 hours.
MIMIC-III
20
GAMENet
Experiments
DDI rate
change
Jaccard
F1
Average
#Medications
Nearest
+1.80%
0.3911
0.5465
14.77
LR
+1.16%
0.4075
0.5658
11.42
Leap
-31.53%
0.3844
0.5410
14.42
RETAIN
+2.57%
0.4168
0.5781
16.68
DMNC
+22.14%
0.4343
0.5934
20.00
-3.60%
0.4509
0.6081
14.02
Method
GAMENet
21
GAMENet
Summary
 An end-to-end deep learning model (GAMENet)
that generates effective and safe recommendations
of medication combinations.
 Memory bank is augmented by integrated drug
usage (from real-world evidence) and DDI graphs
(from knowledge).
 Dynamic memory for dynamic and personalized
medication recommendation.
22
GAMENet: Graph Augmented MEmory Networks
for Recommending Medication Combination
Github: https://github.com/sjy1203/GAMENet
GAMENet
Variants of GAMENet
DDI rate
change
Jaccard
F1
PRAUC
EHR only
-1.12%
0.4304
0.5894
0.6736
DDI only
-4.44%
0.4257
0.5850
0.6665
DDI + EHR
-0.47%
0.4337
0.5931
0.6755
DM only
4.52%
0.4431
0.6047
0.6891
GAMENet
-3.60%
0.4509
0.6081
0.6904
Method
24
Q&A
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