SlowGuess/ACL_2020_OCR huggingface dataset:
5.1 External domain knowledge is needed
So far, we have shown that domain knowledge may not be very useful for models answering questions in the emrQA dataset; however, we argue that systems in real-world CminiRC need to be able to encode and use clinical domain knowledge effectively.
Clinical text often contains high variability in many domain-specific words due to abbreviations and synonyms. The presence of different aliases in the question and context can make it difficult for a model to represent semantics accurately and choose the correct span. Besides, medical domain-specific relations (e.g., treats, caused by) and hierarchical relations (e.g., isa) between medical concepts would be likely to appear. The process followed to generate the current emrQA dataset leads to these problems being largely under-represented, even though they can be very common in real cases. We use the following 3 examples as representatives to illustrate the real cases we may encounter.
Synonym. For example, for the question in Figure 2, "Has this patient ever been on Flagyl?", it is easy for the model to answer since "Flagyl" appears in the context. However, if we change "Flagyl" to its synonyms "Metronidazole" (which may not appear in training) in the question, it is hard for the reader to extract the correct answer, as it is not possible for model to capture the semantic meaning of "Metronidazole" as "Flagyl".
Clinical Relations. Another example is the ques
tion shown in Figure 1, "Why has the patient been prescribed hctz?". Currently, machines can easily find the answer since keyword "hctz" is mentioned in the answer. However, given a situation where the drug "hctz" does not appear in the local context of "HTN", our model may have a better chance to extract the correct answers if it stores the relation "(hctz, treats, HTN)".
Hierarchical Relation. For the question "Is there a history of mental illness?", it is more likely that the medical report describes a specific type of psychological condition rather than mention the general phrase "mental illness" since clinical support require specifics. To obtain the correct answer in this case "Depression with previous suicidal ideation," encoding the relation "(depression, isa, mental illness)" would probably help the model make a correct prediction.
These three cases help illustrate how complex medical relations affect the real CliniRC task. Without leveraging external domain knowledge, it is difficult for models to capture the semantic relations necessary to resolve such cases.
In order to verify our claim quantitatively, we select synonym as a representative relation type and manipulate each question by replacing its entities with plausible synonyms or abbreviations. We then introduce external domain knowledge into current models and compare their performance against base models on these augmented questions.
More specifically, we first detect entities in the questions and link them to a medical knowledge base (KB): UMLS (Bodenreider, 2004) using a biomedical and clinical text NLP pipeline tool, ScispaCy (Neumann et al., 2019). Synonyms of detected entities are then retrieved from UMLS and used to replace the original mention. We filter the questions that do not contain entities or that contain entities with no synonyms. We focus on the Relation dataset and only modify the questions in the dev and test set; the questions in the training set are not modified. Finally, we get 69,912 and 125,338 questions in the dev and test set.
We then introduce a simple Knowledge Incorporation Module (KIM) to evaluate the usefulness of external domain knowledge. Formally, given a question  $q:\{w_1^q,w_2^q,\dots,w_l^q\}$  and its context  $c:\{w_1^c,w_2^c,\dots,w_m^c\}$ , where  $w_{i}^{q},w_{j}^{c}$  are words (tokens), all the words can be mapped to  $d_{1}$  dimensional vectors via a word embedding matrix  $E_w\in \mathbb{R}^{d_1\times |\mathcal{V}|}$ , where  $\mathcal{V}$  denotes the word vocab
Figure 4: Performances of DocReader and DocReader + Knowledge Incorporation Module (KIM) on our created questions modified from the Relation dataset.
ulary. So we have  $q: \mathbf{w}_{\mathbf{1}}^{\mathbf{q}}, \ldots, \mathbf{w}_{\mathbf{l}}^{\mathbf{q}} \in \mathbb{R}^{d_1}$  and  $c: \mathbf{w}_{\mathbf{1}}^{\mathbf{c}}, \ldots, \mathbf{w}_{\mathbf{m}}^{\mathbf{c}} \in \mathbb{R}^{d_1}$ .
We then detect entities  $\{e_1^q, e_2^q, \dots, e_n^q\}$  in the question and entities  $\{e_1^c, e_2^c, \dots, e_o^c\}$  in the context and map them to a medical knowledge base (KB), UMLS (Bodenreider, 2004) using scispacy (Neumann et al., 2019). Note that  $l$  is not equal to  $n$  and  $m$  is not equal to  $o$ , since not every token can be mapped to a entity in KB. For entities that contain multiple words, we align them to the first token, same as the alignment used in (Zhang et al., 2019). We then map detected entities to  $d_2$  dimensional vectors  $\{\mathbf{e}_1^{\mathbf{q}}, \mathbf{e}_2^{\mathbf{q}}, \dots, \mathbf{e}_n^{\mathbf{q}}\}$  and  $\{\mathbf{e}_1^{\mathbf{c}}, \mathbf{e}_2^{\mathbf{c}}, \dots, \mathbf{e}_o^{\mathbf{c}}\}$  via a entity embedding matrix  $E_e \in \mathbb{R}^{d_2 \times |\mathcal{U}|}$ , which is pretrained on the entire UMLS KB using the knowledge embedding method TransE (Bordes et al., 2013).  $\mathcal{U}$  denotes the entity vocabulary.
We merge the word embeddings with entity embeddings to feed them into a Multi-layer Perceptron (MLP):
$$
\mathbf {h} _ {\mathbf {i}} ^ {\mathbf {q}} = \sigma \left(\mathbf {W} _ {\mathbf {c}} \mathbf {w} _ {\mathbf {i}} ^ {\mathbf {q}} + \mathbf {W} _ {\mathbf {e}} \mathbf {e} _ {\mathbf {i}} ^ {\mathbf {q}} + \mathbf {b}\right) \tag {1}
$$
$$
\mathbf {h} _ {\mathbf {j}} ^ {\mathbf {c}} = \sigma (\mathbf {W} _ {\mathbf {c}} \mathbf {w} _ {\mathbf {j}} ^ {\mathbf {c}} + \mathbf {W} _ {\mathbf {e}} \mathbf {e} _ {\mathbf {j}} ^ {\mathbf {c}} + \mathbf {b})
$$
where  $\sigma$  is activation function,  $W_{c}, W_{e}, b$  are trainable parameters and  $h_{i}^{q}, h_{j}^{c}$  denote the integrated embeddings that contain information from both the word  $c_{j}$  and the entity  $e_{j}$  in the question and context respectively. For the word that is not mapped to an entity,  $e_{j}$  will be set to 0. The merged embeddings are used as the input to the base reader.
As shown in Figure 4, by adding a basic Knowledge Incorporation Module to the base model, we obtain around  $5\%$  increase of F1 score on the manipulated questions in the test set. This suggests that for questions that involve relations between medical concepts, external domain knowledge may be quite important.
Model	Existing Questions	Paraphrased Questions	New Questions	Overall	emrQA Relation
EM	F1	EM	F1	EM	F1	EM	F1	EM	F1
DocReader	58.33	71.62	38.09	57.28	29.41	35.35	40.00	53.27	86.94	94.85
ClinicalBERT	58.33	73.12	38.09	62.04	23.53	48.79	38.00	60.19	85.33	93.06
Table 5: Results of models when tested on new questions and unseen clinical notes (not in emrQA, but from MIMIC-III dataset). Performance drops around  $40\%$  compared with previously reported on the Relation test set, highlighting generalizability as an essential future direction for CminiRC.
5.2 Generalizing to unseen questions and documents
The aim of CminiRC is to build robust QA systems for doctors to retrieve information buried in clinical texts. When deploying a CminiRC system to a new environment (e.g., a new set of clinical records, a new hospital, etc.), it is infeasible to create new QA pairs for training every time. Thus, an ideal CminiRC system is able to generalize to unseen documents and questions after being fully trained.
To test the generalizability of models trained on emrQA (we focus on the Relation dataset here), our medical experts created 50 new questions that were not present in the emrQA dataset and extracted answers from unseen patient notes in the MIMIC-III (Johnson et al., 2016) dataset. This dataset consists of three types of questions: 12 questions were made from emrQA question templates but contain entities which do not appear in the training set (e.g., "How was the diagnosis of acute cholecystitis made?" was created from the template "How was the diagnosis of |problem| made?"). The other 38 questions have different forms from existing question templates: 21 paraphrase existing questions from emrQA (e.g., "Was an edema found in the physical exam?") was paraphrased from "Does he have any evidence of |problem| in |test|?" and 17 are completely semantically different from the ones in the emrQA dataset (e.g., "What chemotherapy drugs are being administered to the patient?").
As could be expected, we see in Table 5 that the more the new questions deviate from the original emrQA, the more the models struggle to answer them. We observe a performance drop of roughly  $20\%$  compared to the Relation test set on questions made from emrQA templates using MIMIC III clinical notes which were not in the original dataset. For question that are more significantly different, we notice an approximate  $40\%$  and  $60\%$  loss in F1 score when predicting paraphrased questions and entirely new questions respectively. This steep drop in performance for these new settings, espe
cially for paraphrased and new questions, shows how much work there is to be done on this front and highlights generalizability as an important future direction in CiniRC. We also notice that ClinicalBERT works slightly better than the base model DocReader. The reason might be ClinicalBERT was pretrained on the MIMIC-III dataset, which might help the model have a better understanding of the context.
Summary. Based on these two aspects and our previous thorough analysis of the emrQA dataset, it is clear that better datasets are needed to advance CliniRC. Such datasets should be not only large-scale, but also less noisy, more diverse, and moreover allow researchers to systematically evaluate a model's ability to encode domain knowledge and to generalize to new questions and contexts.
We present a brief overview of open-domain, biomedical and clinical question answering tasks, which are most related to our work:
Question Answering (QA) aims to automatically answer questions asked by humans based on external sources, such as Web (Sun et al., 2016), knowledge base (Yih et al., 2015; Sun et al., 2015) and free text (Chen et al., 2016). As an important type of QA, reading comprehension intends to answer a question after reading the passage (Hirschman et al., 1999). Recently, the release of large-scale RC datasets, such as CNN & Daily Mail (Hermann et al., 2015), Stanford Question-Answering Dataset (SQuAD) (Rajpurkar et al., 2016, 2018) makes it possible to solve RC tasks by building deep neural models (Hermann et al., 2015; Wang and Jiang, 2017; Seo et al., 2017; Chen et al., 2017).
More recently, contextualized word representations and pretrained language models, such as ELMo (Peters et al., 2018), GPT (Radford et al., 2018), BERT (Devlin et al., 2019), have been
demonstrated to be very useful in various NLP tasks including RC. By seeing diverse contexts in large corpora, these pretrained language models can capture the rich semantic meaning and produce more accurate and precise representations for words given different contexts. Even a simple classifier or score function built upon these pretrained contextualized word representations perform well in extracting answer spans (Devlin et al., 2019).
Biomedical and Clinical QA. Due to the lack of large-scale annotated biomedical or clinical data, QA and RC systems in these domains are often rule-based and heuristic feature-based (Lee et al., 2006; Niu et al., 2006; Athenikos and Han, 2010).
In recent years, BioASQ challenges (Tsatsaronis et al., 2012) proposed the Biomedical Semantic QA task, where the participants need to respond to each test question with relevant articles, snippets and exact answers. Suster and Daelemans (2018) use summary points of clinical case reports to build a large-scale cloze-style dataset (CliCR), which is similar to the style of CNN & Daily Mail dataset. Jin et al. (2019b) presents PubMedQA, which extracts question-style titles and their corresponding abstracts as the questions and contexts respectively. A few QA pairs are annotated by human experts and most of them are annotated based a simple heuristic rule with "yes/no/maybe".
Due to the great power of contextualized word representations, pretrained language models also have been introduced to biomedical and clinical domain, e.g., BioELMo (Jin et al., 2019a), BioBERT (Lee et al., 2019), and ClinicalBERT (Alsentzer et al., 2019). They adopt similar architectures of the original models but pretrained on the medical and clinical corpus, such as PubMed articles and MIMIC-III (Johnson et al., 2016) clinical notes.
We study the Clinical Reading Comprehension (CliniRC) task with the recently created emrQA dataset. Our qualitative and quantitative analysis as well as exploration of the two desired aspects of CliniRC systems show that future clinical QA datasets should not only be large-scale but also less noisy and more diverse. Moreover, questions that involve complex relations and are across different domains should be included, and then more advanced external knowledge incorporation methods as well as domain adaptation methods can be carefully designed and systematically evaluated.
We thank our medical experts for their annotations. We thank Ping Zhang, Changchang Yin and anonymous reviewers for their helpful comments. This research was sponsored in part by the Patient-Centered Outcomes Research Institute Funding ME-2017C1-6413, the Army Research Office under cooperative agreements W911NF-17-1-0412, NSF Grant IIS1815674, and Ohio Supercomputer Center (Center, 1987). The views and conclusions contained herein are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S.Government. The U.S.Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notice herein.
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Medication	Relation
# Train (Q / C)	154,684 /182	621,428 / 296
# Dev (Q / C)	23,081 / 26	101,700 / 42
# Test (Q / C)	45,192 / 53	181,464 / 85
Total	222,957 / 261	904,592 / 423
Table A1: Statistics of train, dev, test set of the Medication and Relation datasets.
A Experimental Set-up
We split the two datasets Medication and Relation based on the documents (clinical texts) into train, dev, test with the ratio 7:1:2. The statistics are shown in Table A1.
We adopt Exact Match (EM) and F1 score (F1) as our evaluation metrics, same as the open-domain RC (Rajpurkar et al., 2016). We use SQuAD v1.1 official evaluation script to evaluate all the models. All the models used in the paper, BiDAF  $^{2}$ , DocReader  $^{3}$ , QANet  $^{4}$ , BERT  $^{5}$ , BioBERT  $^{6}$ , ClinicalBERT  $^{7}$  are run based on the implementations listed here and strictly followed the instructions.