Clinical coding, assigning standardized codes to medical notes, is critical for epidemiological research, hospital planning, and reimbursement.Neural coding models generally process entire discharge summaries, which are often lengthy and contain information that is not relevant to coding.We propose an approach that combines Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction.On MIMIC-IV, a standard evaluation dataset, our approach achieves near-equivalent performance to a state-of-the-art full-text baseline while using only 22% of the content and reducing training time by over half.Additionally, mapping model attention to complete entity spans yields coherent, clinically meaningful explanations, capturing coding-relevant modifiers such as acuity and laterality.We release a newly annotated NER+AC dataset for MIMIC-IV, designed specifically for ICD coding.Our entitycentric approach lays the foundation for more transparent and cost-effective assisted coding.

Clinical coding is crucial for healthcare billing and data analysis.Manual clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process.However, our analysis, based on US English electronic health records and automated coding research using these records, shows that widely used evaluation methods are not aligned with real clinical contexts.For example, evaluations that focus on the top 50 most common codes are an oversimplification, as there are thousands of codes used in practice.This position paper aims to align AI coding research more closely with practical challenges of clinical coding.Based on our analysis, we offer eight specific recommendations, suggesting ways to improve current evaluation methods.Additionally, we propose new AI-based methods beyond automated coding, suggesting alternative approaches to assist clinical coders in their workflows.

OBJECTIVE: Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been created, and shared tasks have been organised to facilitate active adverse event surveillance. However, most - if not all - datasets or shared tasks focus on extracting ADEs from a particular type of text. Domain generalisation - the ability of a machine learning model to perform well on new, unseen domains (text types) - is under-explored. Given the rapid advancements in natural language processing, one unanswered question is how far we are from having a single ADE extraction model that is effective on various types of text, such as scientific literature and social media posts. METHODS: We contribute to answering this question by building a multi-domain benchmark for adverse drug event extraction, which we named MultiADE. The new benchmark comprises several existing datasets sampled from different text types and our newly created dataset-CADECv2, which is an extension of CADEC (Karimi et al., 2015), covering online posts regarding more diverse drugs than CADEC. Our new dataset is carefully annotated by human annotators following detailed annotation guidelines. CONCLUSION: Our benchmark results show that the generalisation of the trained models is far from perfect, making it infeasible to be deployed to process different types of text. In addition, although intermediate transfer learning is a promising approach to utilising existing resources, further investigation is needed on methods of domain adaptation, particularly cost-effective methods to select useful training instances. The newly created CADECv2 and the scripts for building the benchmark are publicly available at CSIRO's Data Portal (https://data.csiro.au/collection/csiro:62387). These resources enable the research community to further information extraction, leading to more effective active adverse drug event surveillance.

Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans.Conventional sequence tagging techniques encode Markov assumptions that are efficient but preclude recovery of these mentions.We propose a simple, effective transition-based model with generic neural encoding for discontinuous NER.Through extensive experiments on three biomedical data sets, we show that our model can effectively recognize discontinuous mentions without sacrificing the accuracy on continuous mentions.

Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks.However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available in nested entity mentions.We describe NNE-a fine-grained, nested named entity dataset over the full Wall Street Journal portion of the Penn Treebank (PTB).Our annotation comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting.We hope the public release of this large dataset for English newswire will encourage development of new techniques for nested NER.

We investigate the generation of onesentence Wikipedia biographies from facts derived from Wikidata slot-value pairs.We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries.Our model incorporates a novel secondary objective that helps ensure it generates sentences that contain the input facts.The model achieves a BLEU score of 41, improving significantly upon the vanilla sequence-to-sequence model and scoring roughly twice that of a simple template baseline.Human preference evaluation suggests the model is nearly as good as the Wikipedia reference.Manual analysis explores content selection, suggesting the model can trade the ability to infer knowledge against the risk of hallucinating incorrect information.

Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories. We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, name, context, and coherence models from a corpus of web pages with links to Wikipedia. Experiments compare web link models to Wikipedia models on well-known conll and tac data sets. Results show that using 34 million web links approaches Wikipedia performance. Combining web link and Wikipedia models produces the best-known disambiguation accuracy of 88.7 on standard newswire test data.

Abstract A vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g. PersonW works for OrganisationX, GeneY encodes ProteinZ) and recode it in a format such as a relational database or RDF triplestore that can be more effectively used for querying and automated reasoning. A number of resources have been developed for training and evaluating automatic systems for relation extraction in different domains. However, comparative evaluation is impeded by the fact that these corpora use different markup formats and notions of what constitutes a relation. We describe the preparation of corpora for comparative evaluation of relation extraction across domains based on the publicly available ACE 2004, ACE 2005 and BioInfer data sets. We present a common document type using token standoff and including detailed linguistic markup, while maintaining all information in the original annotation. The subsequent reannotation process normalises the two data sets so that they comply with a notion of relation that is intuitive, simple and informed by the semantic web. For the ACE data, we describe an automatic process that automatically converts many relations involving nested, nominal entity mentions to relations involving non-nested, named or pronominal entity mentions. For example, the first entity is mapped from ‘one’ to ‘Amidu Berry’ in the membership relation described in ‘Amidu Berry, one half of PBS’. Moreover, we describe a comparably reannotated version of the BioInfer corpus that flattens nested relations, maps part-whole to part-part relations and maps n-ary to binary relations. Finally, we summarise experiments that compare approaches to generic relation extraction, a knowledge discovery task that uses minimally supervised techniques to achieve maximally portable extractors. These experiments illustrate the utility of the corpora.1

We describe a set of experiments using machine learning techniques for the task of extractive summarisation. The research is part of a summarisation project for which we use a corpus of judgments of the UK House of Lords. We present classification results for naïve Bayes and maximum entropy and we explore methods for scoring the summary-worthiness of a sentence. We present sample output from the system, illustrating the utility of rhetorical status information, which provides a means for structuring summaries and tailoring them to different types of users.

We report on the SUM project which applies automatic summarisation techniques to the legal domain. We describe our methodology whereby sentences from the text are classified according to their rhetorical role in order that particular types of sentence can be extracted to form a summary. We describe some experiments with judgments of the House of Lords: we have performed automatic linguistic annotation of a small sample set and then hand-annotated the sentences in the set in order to explore the relationship between linguistic features and argumentative roles. We use state-of-the-art NLP techniques to perform the linguistic annotation using XML-based tools and a combination of rule-based and statistical methods. We focus here on the predictive capacity of tense and aspect features for a classifier.