Nut Limsopatham

A task-specific query and document representation for medical records search

One of the challenges of searching in the medical domain is to deal with the complexity and ambiguity of medical terminology. Concept-based representation approaches using terminology from domain-specific resources have been developed to handle such a challenge. However, it has been shown that these techniques are effective only when combined with a traditional term-based representation approach. In this paper, we propose a novel technique to represent medical records and queries by focusing only on medical concepts essential for the information need of a medical search task. Such a representation could enhance retrieval effectiveness since only the medical concepts crucial to the information need are taken into account. We evaluate the retrieval effectiveness of our proposed approach in the context of the TREC 2011 Medical Records track. The results demonstrate the effectiveness of our approach, as it significantly outperforms a baseline where all concepts are represented, and markedly outperforms a traditional term-based representation baseline. Moreover, when combining the relevance scores obtained from our technique and a term-based representation approach, the achieved performance is comparable to the best TREC 2011 systems.

Exploiting term dependence while handling negation in medical search

In medical records, negative qualifiers, e.g. no or without, are commonly used by health practitioners to identify the absence of a medical condition. Without considering whether the term occurs in a negative or positive context, the sole presence of a query term in a medical record is insufficient to imply that the record is relevant to the query. In this paper, we show how to effectively handle such negation within a medical records information retrieval system. In particular, we propose a term representation that tackles negated language in medical records, which is further extended by considering the dependence of negated query terms. We evaluate our negation handling technique within the search task provided by the TREC Medical Records 2011 track. Our results, which show a significant improvement upon a system that does not consider negated context within records, attest the importance of handling negation.

Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation

Automatically recognising medical concepts mentioned in social media messages (e.g. tweets) enables several applications for enhancing health quality of people in a community, e.g. real-time monitoring of infectious diseases in population. However, the discrepancy between the type of language used in social media and medical ontologies poses a major challenge. Existing studies deal with this challenge by employing techniques, such as lexical term matching and statistical machine translation. In this work, we handle the medical concept normalisation at the semantic level. We investigate the use of neural networks to learn the transition between layman’s language used in social media messages and formal medical language used in the descriptions of medical concepts in a standard ontology. We evaluate our approaches using three different datasets, where social media texts are extracted from Twitter messages and blog posts. Our experimental results show that our proposed approaches significantly and consistently outperform existing effective baselines, which achieved state-of-the-art performance on several medical concept normalisation tasks, by up to 44%.

Aggregating evidence from hospital departments to improve medical records search

Searching medical records is challenging due to their inherent implicit knowledge --- such knowledge may be known by medical practitioners, but it is hidden from an information retrieval (IR) system. For example, it is intuitive for a medical practitioner to assert that patients with heart disease are likely to have records from the hospitals cardiology department. Hence, we hypothesise that this implicit knowledge can be used to enhance a medical records search system that ranks patients based on the relevance of their medical records to a query. In this paper, we propose to group aggregates of medical records from individual hospital departments, which we refer to as department-level evidence, to capture some of the implicit knowledge. In particular, each department-level aggregate consists of all of the medical records created by a particular hospital department, which is then exploited to enhance retrieval effectiveness. Specifically, we propose two approaches to build the department-level evidence based on a federated search and a voting paradigm, respectively. In addition, we introduce an extended voting technique that could leverage this department-level evidence while ranking. We evaluate the retrieval effectiveness of our approaches in the context of the TREC 2011 Medical Records track. Our results show that modelling department-level evidence of records in medical records search improves retrieval effectiveness. In particular, our proposed approach to leverage department-level evidence built using a voting technique obtains results comparable to the best submitted TREC 2011 Medical Records track systems without requiring any external resources that are exploited in those systems.

Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages

Previous studies have shown that health reports in social media, such as DailyStrength and Twitter, have potential for monitoring health conditions (e.g. adverse drug reactions, infectious diseases) in particular communities. However, in order for a machine to understand and make inferences on these health conditions, the ability to recognise when laymen’s terms refer to a particular medical concept (i.e. text normalisation) is required. To achieve this, we propose to adapt an existing phrase-based machine translation (MT) technique and a vector representation of words to map between a social media phrase and a medical concept. We evaluate our proposed approach using a collection of phrases from tweets related to adverse drug reactions. Our experimental results show that the combination of a phrase-based MT technique and the similarity between word vector representations outperforms the baselines that apply only either of them by up to 55%.

Learning to selectively rank patients' medical history

Two main approaches have emerged in the literature for the effective deployment of a search system to rank patients having a medical history relevant to a query. The first approach is to directly rank patients based on the relevance of their medical history, represented as a concatenation of their associated medical records. Instead, the second approach initially retrieves the relevant medical records of patients, and then ranks the patients based on the relevance of their retrieved medical records. However, these two approaches may be useful for different queries. In this work, we propose a novel supervised approach that can effectively identify when to use either of the two aforementioned patient ranking approaches to attain effective retrieval performance. In particular, our approach deploys a classifier to learn to select a ranking approach when ranking patients, by using query difficulty measures, such as query performance predictors and the number of medical concepts detected in a query, as learning features. We thoroughly evaluate our approach using the standard test collections provided by the TREC Medical Records track. Our results show significant improvements over existing strong baselines.

Bidirectional LSTM for Named Entity Recognition in Twitter Messages

In this paper, we present our approach for named entity recognition in Twitter messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT). The main challenge that we aim to tackle in our participation is the short, noisy and colloquial nature of tweets, which makes named entity recognition in Twitter message a challenging task. In particular, we investigate an approach for dealing with this problem by enabling bidirectional long short-term memory (LSTM) to automatically learn orthographic features without requiring feature engineering. In comparison with other systems participating in the shared task, our system achieved the most effective performance on both the ‘segmentation and categorisation’ and the ‘segmentation only’ sub-tasks.

What’s missing in geographical parsing?

AbstractGeographical data can be obtained by converting place names from free-format text into geographical coordinates. The ability to geo-locate events in textual reports represents a valuable source of information in many real-world applications such as emergency responses, real-time social media geographical event analysis, understanding location instructions in auto-response systems and more. However, geoparsing is still widely regarded as a challenge because of domain language diversity, place name ambiguity, metonymic language and limited leveraging of context as we show in our analysis. Results to date, whilst promising, are on laboratory data and unlike in wider NLP are often not cross-compared. In this study, we evaluate and analyse the performance of a number of leading geoparsers on a number of corpora and highlight the challenges in detail. We also publish an automatically geotagged Wikipedia corpus to alleviate the dearth of (open source) corpora in this domain.

Towards the Semantic Interpretation of Personal Health Messages from Social Media

Recent attempts have been made to utilise social media platforms, such as Twitter, to provide early warning and monitoring of health threats in populations (i.e. Internet bio-surveillance). It has been shown in the literature that a system based on keyword matching that exploits social media messages could report flu surveillance well ahead of the Centers of Disease Control and Prevention (CDC). However, we argue that a simple keyword matching may not capture semantic interpretation of social media messages that would enable healthcare experts or machines to extract and leverage medical knowledge from social media messages. In this paper, we motivate and describe a new task that aims to tackle this technology gap by extracting semantic interpretation of medical terms mentioned in social media messages, which are typically written in laymans language. Achieving such a task would enable an automatic integration between the data about direct patient experiences extracted from social media and existing knowledge from clinical databases, which leads to advances in the use of community health experiences in healthcare services.

Modelling the Usefulness of Document Collections for Query Expansion in Patient Search

Dealing with the medical terminology is a challenge when searching for patients based on the relevance of their medical records towards a given query. Existing work used query expansion (QE) to extract expansion terms from different document collections to improve query representation. However, the usefulness of particular document collections for QE was not measured and taken into account during retrieval. In this work, we investigate two automatic approaches that measure and leverage the usefulness of document collections when exploiting multiple document collections to improve query representation. These two approaches are based on resource selection and learning to rank techniques, respectively. We evaluate our approaches using the TREC Medical Records tracks test collection. Our results show the potential of the proposed approaches, since they can effectively exploit 14 different document collections, including both domain-specific (e.g. MEDLINE abstracts) and generic (e.g. blogs and webpages) collections, and significantly outperform existing effective baselines, including the best systems participating at the TREC Medical Records track. Our analysis shows that the different collections are not equally useful for QE, while our two approaches can automatically weight the usefulness of expansion terms extracted from different document collections effectively.