Explanipedia

Less is More: Explainable and Efficient ICD Code Prediction with Clinical Entities Open

James W. Douglas, Yidong Gan, Ben Hachey, Jonathan K. Kummerfeld · 2025

Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review Open

Yidong Gan, Maciej Rybiński, Ben Hachey, Jonathan K. Kummerfeld · 2025

Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review Open

Yidong Gan, Maciej Rybiński, Ben Hachey, Jonathan K. Kummerfeld · 2024

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 Englis…

Synthetic Data, Common Data Models and Federation: Holy Trinity or unholy mess? Open

James Boyd, Simon Thompson, Michael J. Schull, Alison L. Park, Ben Hachey , et al. · 2024

The healthcare sector's adoption of data and digital technologies is hindered by stringent data privacy regulations. Synthetic data, common data models (CDMs) and federated data ecosystems present promising solutions to these challenges. T…

SynD: Australian synthetic health data community of practice Open

Ben Hachey, Jodie A Austin, James Boyd, Teyl Engstrom, Amir Marashi , et al. · 2024

ObjectivesThe current workflow for health data research in Australia is inefficient. After funding is secured, researchers often face delays of months or years to access the necessary data. Synthetic data could significantly improve the pa…

Designing a utility evaluation framework for synthetic health data Open

Amir Marashi, Sadiq Dohadwalla, Patrick J. Browne, Ben Hachey · 2024

ObjectivesSynthetic data (SD) promises to unlock health data for training, research, and innovation. However, where utility evaluation is performed, it is applied ad-hoc for a single task of interest. We produce an initial design for a rob…

MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction Open

Xiang Dai, Sarvnaz Karimi, Abeed Sarker, Ben Hachey, Cécile Paris · 2024

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, …

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy Open

Quinlan D. Buchlak, Cyril Tang, Jarrel Seah, Andrew Johnson, Xavier Holt , et al. · 2023

Effects of a comprehensive brain computed tomography deep-learning model on radiologist detection accuracy: a multireader, multicase study Open

Quinlan D. Buchlak, Cyril Tang, Jarrel Seah, Andrew Johnson, Xavier Holt , et al. · 2022

Background: Non-contrast computed tomography of the brain (NCCTB) is commonly used in clinical practice to detect intracranial pathology but is subject to interpretation errors. Machine learning is capable of augmenting clinical decision m…

Charting the potential of brain computed tomography deep learning systems Open

Quinlan D. Buchlak, Michael Milne, Jarrel Seah, Andrew Johnson, Gihan Samarasinghe , et al. · 2022

Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and …

AI-CLRA 2021 Workshop Organizing Committee Open

Hongxu Chen, Gihan Samarasinghe, Harrison Ai, Damith Senanayake, Ali Behnaz , et al. · 2021

Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study Open

Jarrel Seah, Cyril Tang, Quinlan D. Buchlak, Xavier Holt, Jeffrey Wardman , et al. · 2021

Annalise.ai.

Chest radiographs and machine learning – Past, present and future Open

Catherine M Jones, Quinlan D. Buchlak, Luke Oakden‐Rayner, Michael Milne, Jarrel Seah , et al. · 2021

Summary Despite its simple acquisition technique, the chest X‐ray remains the most common first‐line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvemen…

Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media Open

Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cécile Paris · 2020

Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data. Often, the pretraining data used in these models are selected based on their subject ma…

An Effective Transition-based Model for Discontinuous NER Open

Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cécile Paris · 2020

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 …

An Effective Transition-based Model for Discontinuous NER Open

Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cécile Paris · 2020

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 …

Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media Open

Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cécile Paris · 2020

Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data. Often, the pretraining data used in these models are selected based on their subject ma…

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations Open

David Schlangen, Qun Liu, Huawei Noah's Ark Lab, Hong Kong, Adrián Pastor , et al. · 2020

Using Similarity Measures to Select Pretraining Data for NER Open

Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cécile Paris · 2019

Word vectors and Language Models (LMs) pretrained on a large amount of unlabelled data can dramatically improve various Natural Language Processing (NLP) tasks. However, the measure and impact of similarity between pretraining data and tar…

Using Similarity Measures to Select Pretraining Data for Open

Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cécile Paris · 2019

Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cecile Paris. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

NNE: A Dataset for Nested Named Entity Recognition in English Newswire Open

Nicky Ringland, Xiang Dai, Ben Hachey, Sarvnaz Karimi, Cécile Paris , et al. · 2019

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 en…

Face Value: business leaders nervous about consumers spending less and regulation Open

Ross Guest, Ben Hachey, Leonardo Pinheiro · 2018

Can adult mental health be predicted by childhood future-self narratives? Insights from the CLPsych 2018 Shared Task Open

Kylie Radford, Louise Lavrencic, Ruth Peters, Kim M. Kiely, Ben Hachey , et al. · 2018

Kylie Radford, Louise Lavrencic, Ruth Peters, Kim Kiely, Ben Hachey, Scott Nowson, Will Radford. Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. 2018.

Learning to generate one-sentence biographies from Wikidata Open

Andrew M. Chisholm, Will Radford, Ben Hachey · 2017

We investigate the generation of one-sentence 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 summa…

Post-edit Analysis of Collective Biography Generation Open

Bo Han, Will Radford, Anaïs Cadilhac, Art Harol, Andrew M. Chisholm , et al. · 2017

Text generation is increasingly common but often requires manual post-editing where high precision is critical to end users. However, manual editing is expensive so we want to ensure this effort is focused on high-value tasks. And we want …

English Event Detection With Translated Language Features Open

Sam Wei, Igor Korostil, Joel Nothman, Ben Hachey · 2017

We propose novel radical features from automatic translation for event extraction. Event detection is a complex language processing task for which it is expensive to collect training data, making generalisation challenging. We derive meani…

Learning to generate one-sentence biographies from Wikidata Open

Andrew M. Chisholm, Will Radford, Ben Hachey · 2017

We investigate the generation of one-sentence 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 summa…

Post-edit Analysis of Collective Biography Generation Open

Bo Han, Will Radford, Anaïs Cadilhac, Art Harol, Andrew M. Chisholm , et al. · 2017

Text generation is increasingly common but often requires manual post-editing where high precision is critical to end users. However, manual editing is expensive so we want to ensure this effort is focused on high-value tasks. And we want …

Presenting a New Dataset for the Timeline Generation Problem Open

Xavier Holt, Will Radford, Ben Hachey · 2016

The timeline generation task summarises an entity's biography by selecting stories representing key events from a large pool of relevant documents. This paper addresses the lack of a standard dataset and evaluative methodology for the prob…

:telephone::person::sailboat::whale::okhand:; or "Call me Ishmael" - How do you translate emoji? Open

Will Radford, Andrew M. Chisholm, Ben Hachey, Bo Han · 2016

We report on an exploratory analysis of Emoji Dick, a project that leverages crowdsourcing to translate Melville's Moby Dick into emoji. This distinctive use of emoji removes textual context, and leads to a varying translation quality. In …

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