Gastroenterology (GI) cancer surveillance scheduling relies on extracting structured data from unstructured clinical texts, such as endoscopy and pathology reports. Traditional Natural Language Processing (NLP) models have been employed for this task, but recent advancements in Large Language Models (LLMs) present a new opportunity for automation without requiring extensive labeled datasets. In this study, we propose an LLM-based entity extraction and rule-based decision support framework for Barrett’s Oesophagus (BO) surveillance timing prediction. Our approach processes endoscopy and pathology reports to extract clinically relevant information and structures it into a standardised format, which is then used to determine appropriate surveillance intervals. We evaluate multiple state-of-the-art LLMs on real-world clinical datasets from two hospitals, assessing their performance in accuracy and running time cost. The results demonstrate that LLMs, particularly Phi-4 and (DeepSeek distilled) Qwen-2.5, can effectively automate the extraction of BO surveillance-related information with high accuracy, while Phi-4 is also efficient during inference. We also compared the trade-offs between LLMs and fine-tuned non-LLMs. Our findings indicate that LLM extraction based methods can support clinical decision-making by providing justifications from report extractions, reducing manual workload, and improving guideline adherence in BO surveillance scheduling.

With increased accessibility of machine-generated texts, the need for their evaluation has also grown. There are broadly two types of text generation tasks. In open-ended generation tasks (OGTs), the model generates de novo text without any input on which to base it, such as story generation. In reflective generation tasks (RGTs), the model output is generated to reflect an input sequence, such as in machine translation. There are many studies on RGT evaluation, where the metrics typically compare one or more gold-standard references to the model output. Evaluation of OGTs has received less attention and is more challenging: since the task does not aim to reflect an input, there are usually no reference texts. In this paper, we propose a new perspective that unifies OGT evaluation with RGT evaluation, based on which we develop an automatic, reference-free generative text evaluation model (ARGENT), and review previous literature from this perspective. Our experiments demonstrate the effectiveness of these methods across informal, formal, and domain-specific texts. We conduct a meta-evaluation to compare existing and proposed metrics, finding that our approach aligns more closely with human judgement.

The development of NLP models in the healthcare sector faces important challenges due to the limited availability of patient data, mainly driven by privacy concerns. This study proposes the generation of synthetic free-text medical reports, specifically focusing on the gastroenterology domain, to address the scarcity of specialised datasets, while preserving patient privacy. We fine-tune BioGPT on over 90 000 endoscopy reports and integrate Differential Privacy (DP) into the training process. 10 000 DP-private synthetic reports are generated by this model. The generated synthetic data is evaluated through multiple dimensions: similarity to real datasets, language quality, and utility in both supervised and semi-supervised NLP tasks. Results suggest that while DP integration impacts text quality, it offers a promising balance between data utility and privacy, improving the performance of a real-world downstream task. Our study underscores the potential of synthetic data to facilitate model development in the healthcare domain without compromising patient privacy.

Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research. However these present specific challenges compared to other classification tasks, notably due to the particular nature of the medical lexicon and language used in clinical records. Recent advances in embedding methods have shown promising results for several clinical tasks, yet there is no exhaustive comparison of such approaches with other commonly used word representations and classification models. In this work, we analyse the impact of various word representations, text pre-processing and classification algorithms on the performance of four different text classification tasks. The results show that traditional approaches, when tailored to the specific language and structure of the text inherent to the classification task, can achieve or exceed the performance of more recent ones based on contextual embeddings such as BERT.

Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk. We apply a deep neural network based classification model with a lightweight context encoder, to classify sentence level suicidal behaviour in EHRs. We show that incorporating information from sentences to left and right of the target sentence significantly improves classification accuracy. Our approach achieved the best performance when classifying suicidal behaviour in Autism Spectrum Disorder patient records. The results could have implications for suicidality research and clinical surveillance.

Free text fields within electronic health records (EHRs) contain valuable clinical information which is often missed when conducting research using EHR databases. One such type of information is medications which are not always available in structured fields, especially in mental health records. Most use cases that require medication information also generally require the associated temporal information (e.g. current or past) and attributes (e.g. dose, route, frequency). The purpose of this study is to develop a corpus of medication annotations in mental health records. The aim is to provide a more complete picture behind the mention of medications in the health records, by including additional contextual information around them, and to create a resource for use when developing and evaluating applications for the extraction of medications from EHR text. Thus far, an analysis of temporal information related to medications mentioned in a sample of mental health records has been conducted. The purpose of this analysis was to understand the complexity of medication mentions and their associated temporal information in the free text of EHRs, with a specific focus on the mental health domain.

In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a new method for sentence classification, Context-LSTM-CNN, that makes use of potentially large contexts. The method also utilizes long-range dependencies within the sentence being classified, using an LSTM, and short-span features, using a stacked CNN. Our experiments demonstrate that this approach consistently improves over previous methods on two different datasets.

Terminologies and other knowledge resources are widely used to aid entity recognition in specialist domain texts. As well as providing lexicons of specialist terms, linkage from the text back to a resource can make additional knowledge available to applications. Use of such resources is especially pertinent in the biomedical domain, where large numbers of these resources are available, and where they are widely used in informatics applications. Terminology resources can be most readily used by simple lexical lookup of terms in the text. A major drawback with such lexical lookup, however, is poor precision caused by ambiguity between domain terms and general language words. We combine lexical lookup with simple filtering of ambiguous terms, to improve precision. We compare this lexical lookup with a statistical method of entity recognition, and to a method which combines the two approaches. We show that the combined method boosts precision with little loss of recall, and that linkage from recognised entities back to the domain knowledge resources can be maintained.

In this paper we describe ANNALIST (Annotation, Alignment and Scoring Tool), a scoring system for the evaluation of the output of semantic annotation systems. ANNALIST has been designed as a system that is easily extensible and configurable for different domains, data formats, and evaluation tasks. The system architecture enables data input via the use of plugins and the users can access the system’s internal alignment and scoring mechanisms without the need to convert their data to a specified format. Although developed for evaluation tasks that involve the scoring of entity mentions and relations primarily, ANNALIST’s generic object representation and the availability of a range of criteria for the comparison of annotations enable the system to be tailored to a variety of scoring jobs. The paper reports on results from using ANNALIST in real-world situations in comparison to other scorers which are more established in the literature. ANNALIST has been used extensively for evaluation tasks within the VIKEF (EU FP6) and CLEF (UK MRC) projects.