Papers by Ben Hachey
Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review (2025.acl-long)
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| Challenge: | Clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. |
| Approach: | They propose to use AI to improve evaluation methods and propose new methods to assist clinical coders in their workflows. |
| Outcome: | The proposed methods can be improved and improved on existing methods and the existing ones to assist coders in their workflows. |
NNE: A Dataset for Nested Named Entity Recognition in English Newswire (P19-1)
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| Challenge: | Named entity recognition (NER) is widely used in downstream tasks but most tools focus on flat mention structure over coarse schemas. |
| Approach: | They describe a fine-grained, nested named entity dataset over the Wall Street Journal portion of the Penn Treebank. |
| Outcome: | The proposed dataset comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting. |
Using Similarity Measures to Select Pretraining Data for NER (N19-1)
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| Challenge: | Existing studies on how to select appropriate data to pretrain word vectors or LMs are lacking. |
| Approach: | They propose to quantify aspects of similarity between pretraining and target data. |
| Outcome: | The proposed measures are good predictors of the usefulness of pretrained models for Named Entity Recognition over 30 data pairs. |
An Effective Transition-based Model for Discontinuous NER (2020.acl-main)
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| Challenge: | Named Entity Recognition (NER) data sets often contain mentions consisting of discontinuous spans. |
| Approach: | They propose a transition-based model with generic neural encoding for discontinuous NER that can recognize discontinuous mentions without sacrificing the accuracy on continuous mentions. |
| Outcome: | The proposed model can recognize discontinuous mentions without sacrificing accuracy on continuous mentions. |
Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media (2020.findings-emnlp)
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| Challenge: | Recent studies show that domain-specific BERT models can be improved when in-domain data is used for pretraining. |
| Approach: | They propose to use Twitter and forum text as pretraining sources for two BERT models and use similarity measures to nominate in-domain data for pretraining. |
| Outcome: | The proposed method can be used to improve performance on downstream tasks by using in-domain data. |
Less is More: Explainable and Efficient ICD Code Prediction with Clinical Entities (2025.acl-long)
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| Challenge: | Clinical coding is labor-intensive and prone to delays, leading to global backlogs. |
| Approach: | They propose an approach that combines Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction. |
| Outcome: | The proposed approach reduces training time by over half on a standard evaluation dataset compared to current methods . it uses Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction. |