Author name cluster

Andrey Kormilitzin

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning

Torben Berndt
Benjamin Walker
Tiexin Qin
Jan Stühmer
Andrey Kormilitzin

Dynamic graphs exhibit complex temporal dynamics due to the interplay between evolving node features and changing network structures. Recently, Graph Neural Controlled Differential Equations (Graph Neural CDEs) successfully adapted Neural CDEs from paths on Euclidean domains to paths on graph domains. Building on this foundation, we introduce \textit{Permutation Equivariant Graph Neural CDEs}, which project Graph Neural CDEs onto permutation equivariant function spaces. This significantly reduces the model's parameter count without compromising representational power, resulting in more efficient training and improved generalisation. We empirically demonstrate the advantages of our approach through experiments on simulated dynamical systems and real-world tasks, showing improved performance in both interpolation and extrapolation scenarios.

PDF Details

AIIM Journal 2024 Journal Article

Developing healthcare language model embedding spaces

Niall Taylor
Dan Schofield
Andrey Kormilitzin
Dan W. Joyce
Alejo Nevado-Holgado

Pre-trained Large Language Models (LLMs) have revolutionised Natural Language Processing (NLP) tasks, but often struggle when applied to specialised domains such as healthcare. The traditional approach of pre-training on large datasets followed by task-specific fine-tuning is resource-intensive and poorly aligned with the constraints of many healthcare settings. This presents a significant challenge for deploying LLM-based NLP solutions in medical contexts, where data privacy, computational resources, and domain-specific language pose unique obstacles. This study aims to develop and evaluate efficient methods for adapting smaller LLMs to healthcare-specific datasets and tasks. We seek to identify pre-training approaches that can effectively instil healthcare competency in compact LLMs under tight computational budgets, a crucial capability for responsible and sustainable deployment in local healthcare settings. We explore three specialised pre-training methods to adapt smaller LLMs to different healthcare datasets: traditional Masked Language modelling (MLM), Deep Contrastive Learning for Unsupervised Textual Representations (DeCLUTR), and a novel approach utilising metadata categories from healthcare settings. These methods are assessed across multiple healthcare datasets, with a focus on downstream document classification tasks. We evaluate the performance of the resulting LLMs through classification accuracy and analysis of the derived embedding spaces. Contrastively trained models consistently outperform other approaches on classification tasks, delivering strong performance with limited labelled data and fewer model parameter updates. While our novel metadata-based pre-training does not further improve classifications across datasets, it yields interesting embedding cluster separability. Importantly, all domain-adapted LLMs outperform their publicly available, general-purpose base models, validating the importance of domain specialisation. This research demonstrates the efficacy of specialised pre-training methods in adapting compact LLMs to healthcare tasks, even under resource constraints. We provide guidelines for pre-training specialised healthcare LLMs and motivate continued inquiry into contrastive objectives. Our findings underscore the potential of these approaches for aligning small LLMs with privacy-sensitive medical tasks, offering a path toward more efficient and responsible NLP deployment in healthcare settings. This work contributes to the broader goal of making advanced NLP techniques accessible and effective in specialised domains, particularly where resource limitations and data sensitivity are significant concerns.

Details DOI

AIIM Journal 2024 Journal Article

Efficiency at scale: Investigating the performance of diminutive language models in clinical tasks

Niall Taylor
Upamanyu Ghose
Omid Rohanian
Mohammadmahdi Nouriborji
Andrey Kormilitzin
David A. Clifton
Alejo Nevado-Holgado

The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability. This was followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods. We present an investigation into the suitability of different PEFT methods to clinical decision-making tasks, across a range of model sizes, including extremely small models with as few as 25 million parameters. Our analysis shows that the performance of most PEFT approaches varies significantly from one task to another, with the exception of LoRA, which maintains relatively high performance across all model sizes and tasks, typically approaching or matching full fine-tuned performance. The effectiveness of PEFT methods in the clinical domain is evident, particularly for specialised models which can operate on low-cost, in-house computing infrastructure. The advantages of these models, in terms of speed and reduced training costs, dramatically outweighs any performance gain from large foundation LLMs. Furthermore, we highlight how domain-specific pre-training interacts with PEFT methods and model size, finding the domain pre-training to be particularly important in smaller models and discuss how these factors interplay to provide the best efficiency-performance trade-off. Full code available at: https: //github. com/nlpie-research/efficient-ml.

Details DOI

AIIM Journal 2024 Journal Article

Model development for bespoke large language models for digital triage assistance in mental health care

Niall Taylor
Andrey Kormilitzin
Isabelle Lorge
Alejo Nevado-Holgado
Andrea Cipriani
Dan W. Joyce

Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) — a particularly important use-case for mental health where a majority of routinely-collected patient data lacks structured, machine-readable content. A significant problem for the United Kingdom’s National Health Service (NHS) are the long waiting lists for specialist mental healthcare. According to NHS data (NHS Digital, 2024), in each month of 2023, there were between 370, 000 and 470, 000 individual new referrals into secondary mental healthcare services. Referrals must be triaged by clinicians, using clinical information contained in the patient’s EHR to arrive at a decision about the most appropriate mental healthcare team to assess and potentially treat these patients. The ability to efficiently recommend a relevant team by ingesting potentially voluminous clinical notes could help services both reduce referral waiting times and with the right technology, improve the evidence available to justify triage decisions. We present and evaluate three different approaches for LLM-based, end-to-end ingestion of variable-length clinical EHR data to assist clinicians when triaging referrals. Our model is able to deliver triage recommendations consistent with existing clinical practices and its architecture was implemented on a single GPU, making it practical for implementation in resource-limited NHS environments where private implementations of LLM technology will be necessary to ensure confidential clinical data are appropriately controlled and governed. Code available at: https: //github. com/NtaylorOX/BespokeLLM_Triage.

Details DOI

AIIM Journal 2021 Journal Article

Med7: A transferable clinical natural language processing model for electronic health records

Andrey Kormilitzin
Nemanja Vaci
Qiang Liu
Alejo Nevado-Holgado

Electronic health record systems are ubiquitous and the majority of patients’ data are now being collected electronically in the form of free text. Deep learning has significantly advanced the field of natural language processing and the self-supervised representation learning and the transfer learning have become the methods of choice in particular when the high quality annotated data are limited. Identification of medical concepts and information extraction is a challenging task, yet important ingredient for parsing unstructured data into structured and tabulated format for downstream analytical tasks. In this work we introduced a named-entity recognition (NER) model for clinical natural language processing. The model is trained to recognise seven categories: drug names, route of administration, frequency, dosage, strength, form, duration. The model was first pre-trained on the task of predicting the next word, using a collection of 2 million free-text patients’ records from MIMIC-III corpora followed by fine-tuning on the named-entity recognition task. The model achieved a micro-averaged F1 score of 0. 957 across all seven categories. Additionally, we evaluated the transferability of the developed model using the data from the Intensive Care Unit in the US to secondary care mental health records (CRIS) in the UK. A direct application of the trained NER model to CRIS data resulted in reduced performance of F1 = 0. 762, however after fine-tuning on a small sample from CRIS, the model achieved a reasonable performance of F1 = 0. 944. This demonstrated that despite a close similarity between the data sets and the NER tasks, it is essential to fine-tune the target domain data in order to achieve more accurate results. The resulting model and the pre-trained embeddings are available at https: //github. com/kormilitzin/med7.

Details DOI