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Comment on: “Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts”

Authors:
Correspondence
Comment on: “Deep learning for pharmacovigilance:
recurrent neural network architectures for labeling
Arjun Magge,
1
Abeed Sarker,
2
2
and Graciela Gonzalez-Hernandez
2
1
College of Health Solutions, Arizona State University, Scottsdale, Arizona, USA, and
2
Perelman School of Medicine, University of
Received 28 November 2018; Editorial Decision 20 December 2018; Accepted 21 January 2019
Dear Editor,
We read with great interest the article by Cocos et al.
1
In it, the
authors use one of the datasets made public by our lab in parallel
with a publication in Journal of the American Medical Informatics
Association,
2
(henceforth the ADRMine Dataset). Cocos et al use state-of-the-art
recurrent neural network (RNN) models for extracting adverse drug
for their clear description of the workings of neural models, and on
their experiments on the use of fixed versus trainable embeddings,
which can be very valuable to the natural language processing
(NLP) research community. We believe that using deep learning
models offer greater opportunities for mining ADR posts on social
media.
However, there are key choices made by the authors that require
clarification to avoid a misunderstanding on the impact of their find-
ings. In a nutshell, because the authors did not use the ADRMine
Dataset in its entirety, discarding upfront all tweets with no human
annotations (ie, those that do not contain any ADRs), the resulting
train and test sets are biased toward the positive class. Thus, the per-
formance measures reported for the task in Cocos et al are not com-
parable to those reported in Nikfarjam et al,
2
contrary to what the
manuscript reports.
After discarding tweets with no human annotation from the
Twitter, and added a small set (203 tweets) to form the dataset used
almost unavoidable reduction in the dataset size—as not all tweets
are available as time goes by—it would not generally affect the class
balance. The elimination of the tweets with no human annotations
from the ADRMine Dataset, however, is a choice that is not
discussed by Cocos et al, even though it severely impacts the
positive-to-negative class balance of the dataset, leaving it at the 95
to 5 that they report, and, as our experiments show, has a significant
impact on the reported performance. Our comparisons of ADRMine
with the system proposed by Cocos et al reveal that, actually, when
the two systems are employed on the dataset with the original bal-
2
performs significantly better than their proposed
approach (last two rows of Table 1). Thus, the claim in the Results
and Conclusion sections of Cocos et al that their model “represents
new state-of-the-art performance” and that “RNN models ... estab-
lish new state-of-the-art performance by achieving statistically sig-
nificant superior F-measure performance compared to the CRF-
based model” is premature. We expand on these points next.
To give some context to the ADRMine dataset, it contains a set
of tweets collected on medication name as a keyword. Retweets
were removed, and tweets with a URL were omitted, given that our
the data in a way that reflected what was automatically possible at
the time, a binary classifier with precision around 0.4-0.5 was as-
sumed. Thus, negative (non-ADR) instances were kept at around
50%, down from approximately 89% non-ADR tweets that come
naturally when collecting on medication name as a keyword,
2
a bal-
ance one would expect for this task utilizing state-of-the-art auto-
matic methods for classification before attempting extraction. It is
thus a realistic, justified, balance.
Regarding the Cocos et al approach, although controlled experi-
ments training with different ratios of class examples are not un-
usual in machine learning, results for different positive-to-negative
ratios are usually reported and are noted upfront. Cocos et al use a
95-to-5 positive-to-negative split, and only report on the perfor-
mance on this altered dataset, making no mention of the alteration
or class imbalance in the abstract. The statement in the abstract
summarizes their results as follows: “Our best-performing RNN
V
CThe Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association.
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
journals.permissions@oup.com 577
Journal of the American Medical Informatics Association, 26(6), 2019, 577–579
doi: 10.1093/jamia/ocz013
Advance Access Publication Date: 11 April 2019
Correspondence
model ... achieved an approximate match F-measure of 0.755 for
ADR identification on the dataset, compared to 0.631 for a baseline
lexicon system and 0.65 for the state-of-the-art conditional random
fields model.” Although further in the manuscript Cocos et al refer
to having implemented a CRF model “as described for previous
state-of-the-art results,” citing Nikfarjam et al,
2
the statement in the
abstract could be misconstrued as directly comparing it to Nikfar-
jam et al, which is the state-of-the-art conditional random fields
(CRF) model. In reality, the results are not comparable, given the
changes to the dataset. Their implementation of a CRF model must
have been significantly different to ADRMine as described in Nik-
farjam et al, given that the reported performance in Cocos et al for a
CRF model (0.65) is much lower than when both systems are used
on the unaltered ADRMine Dataset, as our experiments show (last
two rows of Table 1).
2
Please note that Cocos et al did not make
available their CRF model implementation, so any differences to the
ADRMine model could not be verified directly, only inferred from
the reported results. The binaries of ADRMine were available at the
time of publication, and we have since made available the full code
to facilitate reproducibility.
a
In machine learning research, authors decide how the model is
trained and how the data are algorithmically filtered before training,
apply accepted practices for balancing the data, or include addi-
tional weakly supervised examples.
3
However, such methods are ap-
plied to the training data only, leaving the evaluation data intact in
order to be able to compare approaches. By excluding tweets that
are negative for the presence of ADRs and other entities from their
training, the authors built a model that is biased to the positive class.
This might not be immediately obvious in Cocos et al, as the model
is evaluated against a similarly biased test set. However, when run
against the balanced test set, the problem becomes evident. The
authors do note this, stating that “including a significant number of
posts without ADRs in the training data produced relatively poor
results for both the RNN and baseline models,” but they did not in-
clude a report of these results or altered their experimental approach
to make this more evident.
To illustrate the impact of the dataset modifications on the over-
all results, we ran the training and evaluation experiments on the
ADRMine Dataset for tweets available as of October 2018 using the
authors’ publicly available implementation
b
and summarize the
results in Table 1. Under the same settings as Cocos et al (eliminat-
ing virtually all tweets in the negative class), the performance
reported (row 1) and our replication (row 2) can be considered a
match with a slight drop that could be attributed to fewer tweets
available as of October 2018 compared with when they ran it. How-
ever, evaluating the Cocos et al model on the balanced test set (row
3) shows a drop of 10 percentage points compared with evaluating
against the mostly positive set (row 2). Training on all available pos-
itive and negative tweets from the October 2018 set (row 4) leads to
an improved model but continues to show significantly lower per-
formance (0.64) with respect to when the same model is trained and
tested on the biased set (0.73 in row 2). Additionally, and to be able
to do a direct comparison, we trained and tested the Cocos et al sys-
tem as provided by them (except for the download script) on the
original, balanced, ADRMine Dataset containing 1784 tweets. We
found a mean performance of 0.67 over 10 runs (row 5), 5 points
lower than the 0.72 F
1
-score reported in Nikfarjam et al on the same
dataset (row 6).
2
Furthermore, referring to the ADRMine Dataset,
2
Cocos et al re-
port, “Of the 957 identifiers in the original dataset...,” which is in-
correct. The original dataset, publicly available and unchanged since
its first publication in 2015, contains a total of 1784 tweets (1340 in
the training set and 444 in the evaluation or test set). As of October
2018, 1012 of the 1784 original training set tweets were still avail-
able in Twitter (including 267 of the 444 original evaluation tweets).
Cocos et al do not mention the additional 827 tweets that were in
the ADRMine Dataset, even though many of them were still avail-
able at the time of their publication. They used only 149 tweets
from the 444 in the evaluation set. From our analysis, the 957 men-
tioned in Cocos et al correspond to the number of tweets in the
ADRMine Dataset that are manually annotated for the presence of
ADRs and other entities, such as indications, drug, and other (mis-
cellaneous) entities. The rest (827 tweets) mentioning medications
but with no other entities present, are discarded upfront, as can be
script. Although the Cocos et al code points researchers to the origi-
the said script with that data, they lose all the unannotated negative
tweets. The authors do not discuss the rationale as to why the data-
set was modified in such a manner. From the time that Cocos et al
was published, subsequent papers have also used the 95-to-5 posi-
tive-to-negative split, presumably because they reuse the python
script.
47
We have made available with this letter, a modification to
tweets.
c
In conclusion, the performance reported for the RNN model in
Cocos et al is not comparable to any prior published approach, and
Table 1. Performance comparison of NERs under different training and testing modes
Mode Dataset Size Precision Recall F
1
-score
Cocos et al on MostlyPos dataset as published 844 tweets 0.70 (0.66-0.74) 0.82 (0.76-0.89) 0.75 (0.74-0.76)
October 2018: train
MostlyPos
and test
MostlyPos
526 tweets 0.76 (0.70-0.82) 0.72 (0.63-0.81) 0.73 (0.70-0.76)
October 2018: train
MostlyPos
and test
Standard
644 tweets 0.60 (0.54-0.65) 0.70 (0.62-0.77) 0.63 (0.60-0.66)
October 2018: train
Standard
and test
Standard
1012 tweets 0.73 (0.66-0.79) 0.60 (0.52-0.68) 0.64 (0.62-0.66)
Cocos et al on ADRMine Dataset 1784 tweets 0.68 (0.62-0.73) 0.69 (0.62-0.75) 0.67 (0.66-0.69)
1
1784 tweets 0.76 0.68 0.72
Values are mean (95% conﬁdence interval). Scores were achieved by each model over 10 training and evaluation rounds. MostlyPos refers to how the dataset is
used by Cocos et al (ie, removing tweets without span annotations), hence leaving mostly positive tweets. Standard refers to the dataset including a roughly 50-50
balance of positive to negative tweets as in Nikfarjam et al,
2
and the balance of the ADRMine Dataset.
a https://github.com/azinik/ADRMine Accessed November 21, 2018.
21, 2018.
Accessed November 21, 2018.
578 Journal of the American Medical Informatics Association, 2019, Vol. 26, No. 6
in effect, when trained and tested with the full dataset, its perfor-
mance (0.64) is significantly lower than the state of the art for the
2
ADR mentions are very rare events on social media, as
media. Even after three years, the best classifier reaches only a preci-
sion of 0.44, recall of 0.63, for an F-measure of 0.52.
8
The upfront
stripping of negative examples, whereby 95% of the dataset con-
tains at least 1 ADR or indication mention, as done in Cocos et al,
results in an extremely biased dataset, which in turn results in a
model biased to the positive class that does not reflect any realistic
deployment of a solution to the original problem.
FUNDING
This work was supported by National Institutes of Health National Library
of Medicine grant number 5R01LM011176. The content is solely the respon-
sibility of the authors and does not necessarily represent the official views of
the National Library of Medicine or National Institutes of Health.
AUTHOR CONTRIBUTORS
AM first noted the data use problem, ran the experiments and wrote
the initial draft of the manuscript. AS and AN contributed to some
sections and made edits to the manuscript. GG designed the experi-
ments and wrote the final version of the manuscript.
Conflict of interest statement: None declared.
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Journal of the American Medical Informatics Association, 2019, Vol. 26, No. 6 579
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