103 research outputs found
Effective Natural Language Processing Algorithms for Gout Flare Early Alert from Chief Complaints
In this study, we extend the exploration of gout flare detection initiated by Osborne, J. D. 16et al, through the utilization of their dataset of Emergency Department (ED) triage nurse chief com- 17plaint notes. Addressing the challenge of identifying gout flares prospectively during an ED visit, 18where documentation is typically minimal, our research focuses on employing alternative Natural 19Language Processing (NLP) techniques to enhance the detection accuracy. This study investigates 20the application of medical domain-specific Large Language Models (LLMs), distinguishing between 21generative and discriminative models. Models such as BioGPT, RoBERTa-large-PubMed-M3, and 22BioElectra were implemented to compare their efficacy with the original implementation by Os- 23borne, J. D. et al. The best model was Roberta-large-PM-M3 with a 0.8 F1 Score on the Gout-CC-2019 24dataset followed by BioElectra with 0.76 F1 Score. We concluded that discriminative LLMs per- 25formed better for this classification task compared to generative LLMs. However, a combination of 26using generative models as feature extractors and employing SVM for the classification of embed- 27dings yielded promising results comparable to those obtained with discriminative models. Never- 28theless, all our implementations surpassed the results obtained in the original publication
Assessing mortality prediction through different representation models based on concepts extracted from clinical notes
Recent years have seen particular interest in using electronic medical
records (EMRs) for secondary purposes to enhance the quality and safety of
healthcare delivery. EMRs tend to contain large amounts of valuable clinical
notes. Learning of embedding is a method for converting notes into a format
that makes them comparable. Transformer-based representation models have
recently made a great leap forward. These models are pre-trained on large
online datasets to understand natural language texts effectively. The quality
of a learning embedding is influenced by how clinical notes are used as input
to representation models. A clinical note has several sections with different
levels of information value. It is also common for healthcare providers to use
different expressions for the same concept. Existing methods use clinical notes
directly or with an initial preprocessing as input to representation models.
However, to learn a good embedding, we identified the most essential clinical
notes section. We then mapped the extracted concepts from selected sections to
the standard names in the Unified Medical Language System (UMLS). We used the
standard phrases corresponding to the unique concepts as input for clinical
models. We performed experiments to measure the usefulness of the learned
embedding vectors in the task of hospital mortality prediction on a subset of
the publicly available Medical Information Mart for Intensive Care (MIMIC-III)
dataset. According to the experiments, clinical transformer-based
representation models produced better results with getting input generated by
standard names of extracted unique concepts compared to other input formats.
The best-performing models were BioBERT, PubMedBERT, and UmlsBERT,
respectively
Exploration and adaptation of large language models for specialized domains
Large language models have transformed the field of natural language processing (NLP). Their improved performance on various NLP benchmarks makes them a promising tool—also for the application in specialized domains. Such domains are characterized by highly trained professionals with particular domain expertise. Since these experts are rare, improving the efficiency of their work with automated systems is especially desirable. However, domain-specific text resources hold various challenges for NLP systems. These challenges include distinct language, noisy and scarce data, and a high level of variation. Further, specialized domains present an increased need for transparent systems since they are often applied in high stakes settings. In this dissertation, we examine whether large language models (LLMs) can overcome some of these challenges and propose methods to effectively adapt them to domain-specific requirements.
We first investigate the inner workings and abilities of LLMs and show how they can fill the gaps that are present in previous NLP algorithms for specialized domains. To this end, we explore the sources of errors produced by earlier systems to identify which of them can be addressed by using LLMs. Following this, we take a closer look at how information is processed within Transformer-based LLMs to better understand their capabilities. We find that their layers encode different dimensions of the input text. Here, the contextual vector representation, and the general language knowledge learned during pre-training are especially beneficial for solving complex and multi-step tasks common in specialized domains.
Following this exploration, we propose solutions for further adapting LLMs to the requirements of domain-specific tasks. We focus on the clinical domain, which incorporates many typical challenges found in specialized domains. We show how to improve generalization by integrating different domain-specific resources into our models. We further analyze the behavior of the produced models and propose a behavioral testing framework that can serve as a tool for communication with domain experts. Finally, we present an approach for incorporating the benefits of LLMs while fulfilling requirements such as interpretability and modularity. The presented solutions show improvements in performance on benchmark datasets and in manually conducted analyses with medical professionals.
Our work provides both new insights into the inner workings of pre-trained language models as well as multiple adaptation methods showing that LLMs can be an effective tool for NLP in specialized domains
KG-MTT-BERT: Knowledge Graph Enhanced BERT for Multi-Type Medical Text Classification
Medical text learning has recently emerged as a promising area to improve
healthcare due to the wide adoption of electronic health record (EHR) systems.
The complexity of the medical text such as diverse length, mixed text types,
and full of medical jargon, poses a great challenge for developing effective
deep learning models. BERT has presented state-of-the-art results in many NLP
tasks, such as text classification and question answering. However, the
standalone BERT model cannot deal with the complexity of the medical text,
especially the lengthy clinical notes. Herein, we develop a new model called
KG-MTT-BERT (Knowledge Graph Enhanced Multi-Type Text BERT) by extending the
BERT model for long and multi-type text with the integration of the medical
knowledge graph. Our model can outperform all baselines and other
state-of-the-art models in diagnosis-related group (DRG) classification, which
requires comprehensive medical text for accurate classification. We also
demonstrated that our model can effectively handle multi-type text and the
integration of medical knowledge graph can significantly improve the
performance
Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning
Bipolar disorder (BD) is closely associated with an increased risk of
suicide. However, while the prior work has revealed valuable insight into
understanding the behavior of BD patients on social media, little attention has
been paid to developing a model that can predict the future suicidality of a BD
patient. Therefore, this study proposes a multi-task learning model for
predicting the future suicidality of BD patients by jointly learning current
symptoms. We build a novel BD dataset clinically validated by psychiatrists,
including 14 years of posts on bipolar-related subreddits written by 818 BD
patients, along with the annotations of future suicidality and BD symptoms. We
also suggest a temporal symptom-aware attention mechanism to determine which
symptoms are the most influential for predicting future suicidality over time
through a sequence of BD posts. Our experiments demonstrate that the proposed
model outperforms the state-of-the-art models in both BD symptom identification
and future suicidality prediction tasks. In addition, the proposed temporal
symptom-aware attention provides interpretable attention weights, helping
clinicians to apprehend BD patients more comprehensively and to provide timely
intervention by tracking mental state progression.Comment: KDD 2023 accepte
Extreme multi-label deep neural classification of Spanish health records according to the International Classification of Diseases
111 p.Este trabajo trata sobre la minerÃa de textos clÃnicos, un campo del Procesamiento del Lenguaje Natural aplicado al dominio biomédico. El objetivo es automatizar la tarea de codificación médica. Los registros electrónicos de salud (EHR) son documentos que contienen información clÃnica sobre la salud de unpaciente. Los diagnósticos y procedimientos médicos plasmados en la Historia ClÃnica Electrónica están codificados con respecto a la Clasificación Internacional de Enfermedades (CIE). De hecho, la CIE es la base para identificar estadÃsticas de salud internacionales y el estándar para informar enfermedades y condiciones de salud. Desde la perspectiva del aprendizaje automático, el objetivo es resolver un problema extremo de clasificación de texto de múltiples etiquetas, ya que a cada registro de salud se le asignan múltiples códigos ICD de un conjunto de más de 70 000 términos de diagnóstico. Una cantidad importante de recursos se dedican a la codificación médica, una laboriosa tarea que actualmente se realiza de forma manual. Los EHR son narraciones extensas, y los codificadores médicos revisan los registros escritos por los médicos y asignan los códigos ICD correspondientes. Los textos son técnicos ya que los médicos emplean una jerga médica especializada, aunque rica en abreviaturas, acrónimos y errores ortográficos, ya que los médicos documentan los registros mientras realizan la práctica clÃnica real. Paraabordar la clasificación automática de registros de salud, investigamos y desarrollamos un conjunto de técnicas de clasificación de texto de aprendizaje profundo
ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model
The ChatGPT, a lite and conversational variant of Generative Pretrained
Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large
Language Models (LLMs) with billions of parameters. LLMs have stirred up much
interest among researchers and practitioners in their impressive skills in
natural language processing tasks, which profoundly impact various fields. This
paper mainly discusses the future applications of LLMs in dentistry. We
introduce two primary LLM deployment methods in dentistry, including automated
dental diagnosis and cross-modal dental diagnosis, and examine their potential
applications. Especially, equipped with a cross-modal encoder, a single LLM can
manage multi-source data and conduct advanced natural language reasoning to
perform complex clinical operations. We also present cases to demonstrate the
potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical
application. While LLMs offer significant potential benefits, the challenges,
such as data privacy, data quality, and model bias, need further study.
Overall, LLMs have the potential to revolutionize dental diagnosis and
treatment, which indicates a promising avenue for clinical application and
research in dentistry
Natural Language Processing: Emerging Neural Approaches and Applications
This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains
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