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In intensive care units (ICUs), patient health is monitored through (1) continuous vital signals from various medical devices, and (2) clinical notes consisting of opinions and summaries from doctors which are recorded in electronic health records (EHR). It is difficult to jointly model these two sources of information because clinical notes, unlike vital signals, are collected at irregular intervals and their contents are relatively unstructured. In this paper, we present a model that combines both sources of information about ICU patients to make accurate in-hospital mortality predictions. 
We apply a fine-tuned BERT model to each of the patient&#39;s clinical notes. The resulting embeddings are then combined to obtain the overall embedding for the entire text part of the data. This is then combined with the output of an LSTM model that encodes patients&#39; vital signals.
Our model improves upon the state of the art for mortality prediction, attaining an AUC score of 0.9, compared to the previous 0.87, setting a new standard for mortality prediction on the MIMIC III benchmark.

IJCNLP-AACL 2021

Predicting in-hospital mortality by combining clinical notes with time-series data

time-series data

clinical data

mortality prediction

clinical text

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

In intensive care units (ICUs), patient health is monitored through (1) continuous vital signals from various medical devices, and (2) clinical notes consisting of opinions and summaries from doctors which are recorded in electronic health records (EHR). It is difficult to jointly model these two sources of information because clinical notes, unlike vital signals, are collected at irregular intervals and their contents are relatively unstructured. In this paper, we present a model that combines both sources of information about ICU patients to make accurate in-hospital mortality predictions. 
We apply a fine-tuned BERT model to each of the patient's clinical notes. The resulting embeddings are then combined to obtain the overall embedding for the entire text part of the data. This is then combined with the output of an LSTM model that encodes patients' vital signals.
Our model improves upon the state of the art for mortality prediction, attaining an AUC score of 0.9, compared to the previous 0.87, setting a new standard for mortality prediction on the MIMIC III benchmark.

technical paper

We consider the problem of collectively detecting multiple events, particularly in cross-sentence settings. The key to dealing with the problem is to encode semantic information and model event inter-dependency at a document-level. In this paper, we reformulate it as a Seq2Seq task and propose a \textbf{M}ulti-\textbf{L}ayer \textbf{Bi}directional \textbf{Net}work (MLBiNet) to capture the document-level association of events and semantic information simultaneously. Specifically, a bidirectional decoder is firstly devised to model event inter-dependency within a sentence when decoding the event tag vector sequence. Secondly, an information aggregation module is employed to aggregate sentence-level semantic and event tag information. Finally, we stack multiple bidirectional decoders and feed cross-sentence information, forming a multi-layer bidirectional tagging architecture to iteratively propagate information across sentences. We show that our approach provides significant improvement in performance compared to the current state-of-the-art results.

MLBiNet: A Cross-Sentence Collective Event Detection Network

We study the problem of event coreference resolution (ECR) that seeks to group coreferent event mentions into the same clusters. Deep learning methods have recently been applied for this task to deliver state-of-the-art performance. However, existing deep learning models for ECR are limited in that they cannot exploit important interactions between relevant objects for ECR, e.g., context words and entity mentions, to support the encoding of document-level context. In addition, consistency constraints between golden and predicted clusters of event mentions have not been considered to improve representation learning in prior deep learning models for ECR. This work addresses such limitations by introducing a novel deep learning model for ECR. At the core of our model are document structures to explicitly capture relevant objects for ECR. Our document structures introduce diverse knowledge sources (discourse, syntax, semantics) to compute edges/interactions between structure nodes for document-level representation learning. We also present novel regularization techniques based on consistencies of golden and predicted clusters for event mentions in documents. Extensive experiments show that our model achieve state-of-the-art performance on two benchmark datasets.

Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution

Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest. However, existing studies still face some challenging issues, including information loss, error propagation and ignoring the interaction between entity and relation. To intuitively explore the above issues and address them, in this paper, we provide a revealing insight into relational triple extraction from a stereoscopic perspective, which rationalizes the occurrence of these issues and exposes the shortcomings of existing methods. Further, a novel model is proposed for relational triple extraction, which maps relational triples to a three-dimension (3-D) space and leverages three decoders to extract them, aimed at simultaneously handling the above issues. A series of experiments are conducted on five public datasets, demonstrating that the proposed model outperforms the recent advanced baselines.

StereoRel: Relational Triple Extraction from a Stereoscopic Perspective

Identifying causal relations of events is an important task in natural language processing area. However, the task is very challenging, because event causality is usually expressed in diverse forms that often lack explicit causal clues. Existing methods cannot handle well the problem, especially in the condition of lacking training data. Nonetheless, humans can make a correct judgement based on their background knowledge, including descriptive knowledge and relational knowledge. Inspired by it, we propose a novel Latent Structure Induction Network (LSIN) to incorporate the external structural knowledge into this task. Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge. To leverage the relational knowledge, we propose a Relational Graph Induction module which is able to automatically learn a reasoning structure for event causality reasoning. Experimental results on two widely used datasets indicate that our approach significantly outperforms previous state-of-the-art methods.

Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks

Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer-grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Considering that common-sense knowledge plays an important role in understanding implicitly expressed emotions and the reasons for those emotions, we propose novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging. We show performance improvement on both tasks when including common-sense reasoning and a multi-task framework. We provide a thorough analysis to gain insights into model performance.

Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction

Recent papers have introduced methods to incorporate gazetteer features and entity segmentation techniques in neural named entity recognition models. These papers rely on different resources and 
include features not related to the use of gazetteers, rendering impossible the comparison of the relative effectiveness of the approaches. Here, we provide a comprehensive overview of methods for incorporating gazetteers and for entity segmentation. We evaluate representative methods from each in similar settings for a fair comparison and identify the ones that are consistently better across datasets and input representations.
We further show that gazetteers improve entity segmentation and not just entity typing. Hence, we explore their utility in recognizing long entities, a problem for which entity segmentation techniques were developed.
Our work explains the mechanisms via which gazetteers improve the performance of neural NER models.

The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English

Neural network algorithms such as those based on transformers and attention models have excelled on Automatic Text Classification (ATC) tasks. However, such enhanced performance comes at high computational costs. Ensembles of simpler classifiers (i.e., stacking) that exploit algorithmic and representational complementarities have also been shown to produce top-notch performance in ATC, enjoying high effectiveness and potentially lower computational costs. 

In this context, we present the first and largest comparative study to exploit the cost-effectiveness of stacking of ATC classifiers, consisting of transformers and non-neural algorithms. In particular, we are interested in answering research questions such as: Is it possible to obtain an effective ensemble with significantly less computational cost than the best learning model for a given dataset? Disregarding the computational cost, can an ensemble improve the effectiveness of the best learning model? Besides answering such questions, another main contribution of this paper is the proposal of a low-cost oracle-based method that can predict the best ensemble in each scenario (with and without computational cost limitations) using only a fraction of the available training data.

On the Cost-Effectiveness of Stacking of Neural and Non-Neural Methods for Text Classification: Scenarios and Performance Prediction

Transformer-based language models (LMs) continue to advance state-of-the-art performance on NLP benchmark tasks, including tasks designed to mimic human-inspired "commonsense" competencies. To better understand the degree to which LMs can be said to have certain linguistic reasoning skills, researchers are beginning to adapt the tools and concepts of the field of psychometrics. But to what extent can the benefits flow in the other direction? I.e., can LMs be of use in predicting what the psychometric properties of test items will be when those items are given to human participants? We gather responses from numerous human participants and LMs (transformer- and non-transformer-based) on a broad diagnostic test of linguistic competencies. We then use the responses to calculate standard psychometric properties of the items in the diagnostic test, using the human responses and the LM responses separately. We then determine how well these two sets of predictions match.
We find cases in which transformer-based LMs predict psychometric properties consistently well in certain categories but consistently poorly in others, thus providing new insights into fundamental similarities and differences between human and LM reasoning.

Can Transformer Langauge Models Predict Psychometric Properties?

Disentangling conversation threads is tantamount to the success of many downstream tasks. Structured information accompanying the text, such as user turn, user mentions, and timestamps, has been shown to be important for disentanglement. DAG-LSTMs, a generalization of TreeLSTMs, are a way to incorporate the non- sequential nature of this information. Working with the Ubuntu IRC dataset (Kummerfeld et al, 2019), we propose a model that achieves state-of-the-art results on the task of recovering reply-to relations. It achieves competitive results on other disentanglement metrics.

Disentangling Online Chats with DAG-Structured LSTMs

Cross-lingual word embeddings provide a way
for information to be transferred between languages. In this paper we evaluate an extension of a joint training approach to learning cross-lingual embeddings that incorporates
sub-word information during training. This
method could be particularly well-suited to
lower-resource and morphologically-rich languages because it can be trained on modest
size monolingual corpora, and is able to represent out-of-vocabulary words (OOVs). We
consider bilingual lexicon induction, including
an evaluation focused on OOVs. We find that
this method achieves improvements over previous approaches, particularly for OOVs.

Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages

Predicting in-hospital mortality by combining clinical notes with time-series data

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Predicting in-hospital mortality by combining clinical notes with time-series data

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Next from IJCNLP-AACL 2021

MLBiNet: A Cross-Sentence Collective Event Detection Network

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MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain

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