Search Results for author: Andrey Kormilitzin

Found 15 papers, 5 papers with code

Bespoke Large Language Models for Digital Triage Assistance in Mental Health Care

no code implementations28 Mar 2024 Niall Taylor, Andrey Kormilitzin, Isabelle Lorge, Alejo Nevado-Holgado, Dan W Joyce

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.

Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks

no code implementations16 Feb 2024 Niall Taylor, Upamanyu Ghose, Omid Rohanian, Mohammadmahdi Nouriborji, Andrey Kormilitzin, David 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, 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.

Decision Making

Detecting the Clinical Features of Difficult-to-Treat Depression using Synthetic Data from Large Language Models

1 code implementation12 Feb 2024 Isabelle Lorge, Dan W. Joyce, Niall Taylor, Alejo Nevado-Holgado, Andrea Cipriani, Andrey Kormilitzin

Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where despite treatment, they continue to experience significant burden.

Language Modelling Large Language Model

Clinical Prompt Learning with Frozen Language Models

1 code implementation11 May 2022 Niall Taylor, Yi Zhang, Dan Joyce, Alejo Nevado-Holgado, Andrey Kormilitzin

Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown impressive performance on a number of natural language tasks with common benchmarking text datasets in full, few-shot, and zero-shot train-evaluation setups.


Rationale production to support clinical decision-making

no code implementations15 Nov 2021 Niall Taylor, Lei Sha, Dan W Joyce, Thomas Lukasiewicz, Alejo Nevado-Holgado, Andrey Kormilitzin

In this work, we apply InfoCal, the current state-of-the-art model that produces extractive rationales for its predictions, to the task of predicting hospital readmission using hospital discharge notes.

Decision Making Feature Importance

Population Gradients improve performance across data-sets and architectures in object classification

no code implementations23 Oct 2020 Yurika Sakai, Andrey Kormilitzin, Qiang Liu, Alejo Nevado-Holgado

The most successful methods such as ReLU transfer functions, batch normalization, Xavier initialization, dropout, learning rate decay, or dynamic optimizers, have become standards in the field due, particularly, to their ability to increase the performance of Neural Networks (NNs) significantly and in almost all situations.

General Classification

An efficient representation of chronological events in medical texts

no code implementations EMNLP (Louhi) 2020 Andrey Kormilitzin, Nemanja Vaci, Qiang Liu, Hao Ni, Goran Nenadic, Alejo Nevado-Holgado

In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs).

Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks

no code implementations6 Jan 2019 Luka Gligic, Andrey Kormilitzin, Paul Goldberg, Alejo Nevado-Holgado

In our study, we develop an approach that solves these problems for named entity recognition, obtaining 94. 6 F1 score in I2B2 2009 Medical Extraction Challenge [6], 4. 3 above the architecture that won the competition.

named-entity-recognition Named Entity Recognition +2

Few-shot Learning for Named Entity Recognition in Medical Text

4 code implementations13 Nov 2018 Maximilian Hofer, Andrey Kormilitzin, Paul Goldberg, Alejo Nevado-Holgado

Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017).

Few-Shot Learning Medical Named Entity Recognition +2

A Primer on the Signature Method in Machine Learning

4 code implementations11 Mar 2016 Ilya Chevyrev, Andrey Kormilitzin

We have chosen to focus in detail on the principle properties of the signature which we believe are fundamental to understanding its role in applications.

BIG-bench Machine Learning

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