HDTwin: Building a Human Digital Twin using Large Language Models for Cognitive Diagnosis (Preprint) DOI Creative Commons
Gina Sprint, Maureen Schmitter‐Edgecombe, Diane J. Cook

et al.

JMIR Formative Research, Journal Year: 2024, Volume and Issue: 8, P. e63866 - e63866

Published: Nov. 7, 2024

Background Human digital twins have the potential to change practice of personalizing cognitive health diagnosis because these systems can integrate multiple sources information and influence into a unified model. Cognitive is multifaceted, yet researchers clinical professionals struggle align diverse single Objective This study aims introduce method called HDTwin, for unifying heterogeneous data using large language models. HDTwin designed predict diagnoses offer explanations its inferences. Methods integrates from sources, including demographic, behavioral, ecological momentary assessment, n-back test, speech, baseline experimenter testing session markers. Data are converted text prompts The system then combines inputs with relevant external knowledge scientific literature construct predictive model’s performance validated 3 studies involving 124 participants, comparing diagnostic accuracy machine learning classifiers. Results achieves peak 0.81 based on automated selection markers, significantly outperforming On average, yielded accuracy=0.77, precision=0.88, recall=0.63, Matthews correlation coefficient=0.57. In comparison, classifiers average accuracy=0.65, precision=0.86, recall=0.35, coefficient=0.36. experiments also reveal that yields superior when fused compared sources. HDTwin’s chatbot interface provides interactive dialogues, aiding in interpretation allowing further exploration patient data. Conclusions data, enhancing explainability diagnoses. approach outperforms traditional models an navigating information. shows promise improving early detection intervention strategies health.

Language: Английский

Large Language Models in Healthcare and Medical Domain: A Review DOI Creative Commons
Zabir Al Nazi, Wei Peng

Informatics, Journal Year: 2024, Volume and Issue: 11(3), P. 57 - 57

Published: Aug. 7, 2024

The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These exhibit remarkable ability to provide proficient responses free-text queries, demonstrating a nuanced understanding professional medical knowledge. This comprehensive survey delves into functionalities existing LLMs designed for applications elucidates trajectory their development, starting with traditional Pretrained Language Models (PLMs) then moving present state in sector. First, we explore potential amplify efficiency effectiveness diverse applications, particularly focusing on clinical tasks. tasks encompass wide spectrum, ranging from named entity recognition relation extraction natural inference, multimodal document classification, question-answering. Additionally, conduct an extensive comparison most recent state-of-the-art domain, while also assessing utilization various open-source highlighting significance applications. Furthermore, essential performance metrics employed evaluate biomedical shedding light limitations. Finally, summarize prominent challenges constraints faced by offering holistic perspective benefits shortcomings. review provides exploration current landscape healthcare, addressing role transforming areas that warrant further research development.

Language: Английский

Citations

67
Progress and trends in neurological disorders research based on deep learning DOI
Muhammad Shahid Iqbal, Md Belal Bin Heyat, Saba Parveen

et al.

Computerized Medical Imaging and Graphics, Journal Year: 2024, Volume and Issue: 116, P. 102400 - 102400

Published: May 25, 2024

Language: Английский

Citations

9
Introduction to Large Language Models (LLMs) for dementia care and research DOI Creative Commons
Matthias S. Treder,

Sojin Lee,

Kamen A. Tsvetanov

et al.

Frontiers in Dementia, Journal Year: 2024, Volume and Issue: 3

Published: May 14, 2024

Dementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities social engagement. In light of the recent advent Large Language Models (LLMs) such as ChatGPT, this paper aims thoroughly analyse their potential applications usefulness dementia care research.

Language: Английский

Citations

8
Pre-trained language models in medicine: A survey DOI Creative Commons
Xudong Luo,

Zhiqi Deng,

Binxia Yang

et al.

Artificial Intelligence in Medicine, Journal Year: 2024, Volume and Issue: 154, P. 102904 - 102904

Published: June 5, 2024

With the rapid progress in Natural Language Processing (NLP), Pre-trained Models (PLM) such as BERT, BioBERT, and ChatGPT have shown great potential various medical NLP tasks. This paper surveys cutting-edge achievements applying PLMs to Specifically, we first brief PLMS outline research of medicine. Next, categorise discuss types tasks NLP, covering text summarisation, question-answering, machine translation, sentiment analysis, named entity recognition, information extraction, education, relation mining. For each type task, provide an overview basic concepts, main methodologies, advantages PLMs, steps application, datasets for training testing, metrics task evaluation. Subsequently, a summary recent important findings is presented, analysing their motivations, strengths vs weaknesses, similarities differences, discussing limitations. Also, assess quality influence reviewed this by comparing citation count papers reputation impact conferences journals where they are published. Through these indicators, further identify most concerned topics currently. Finally, look forward future directions, including enhancing models' reliability, explainability, fairness, promote application clinical practice. In addition, survey also collect some download links model codes relevant datasets, which valuable references researchers techniques medicine professionals seeking enhance expertise healthcare service through AI technology.

Language: Английский

Citations

7
Evaluating the validity of the nursing statements algorithmically generated based on the International Classifications of Nursing Practice for respiratory nursing care using large language models DOI
Hyeoneui Kim, Hyewon Park,

Sunghoon Kang

et al.

Journal of the American Medical Informatics Association, Journal Year: 2024, Volume and Issue: 31(6), P. 1397 - 1403

Published: April 17, 2024

Abstract Objective This study aims to facilitate the creation of quality standardized nursing statements in South Korea’s hospitals using algorithmic generation based on International Classifications Nursing Practice (ICNP) and evaluation through Large Language Models. Materials Methods We algorithmically generated 15 972 related acute respiratory care 117 concepts concept composition models ICNP. Human reviewers, Generative Pre-trained Transformers 4.0 (GPT-4.0), Bio_Clinical Bidirectional Encoder Representations from (BERT) evaluated for validity. The by GPT-4.0 Bio_ClinicalBERT was conducted with without contextual information training. Results Of statements, 2207 were deemed valid expert reviewers. showed a zero-shot AUC 0.857, which aggravated information. Bio_ClinicalBERT, after training, significantly improved, reaching an 0.998. Conclusion effectively validates auto-generated offering promising solution enhance streamline healthcare documentation processes.

Language: Английский

Citations

4
Resistance training protects the hippocampus and precuneus against atrophy and benefits white matter integrity in older adults with mild cognitive impairment DOI
Isadora Cristina Ribeiro, Camila Vieira Ligo Teixeira, Thiago Junqueira Ribeiro de Rezende

et al.

GeroScience, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Language: Английский

Citations

0
AI-powered model for accurate prediction of MCI-to-AD progression DOI Creative Commons
Ahmed Abdelhameed, Jingna Feng, Xinyue Hu

et al.

Acta Pharmaceutica Sinica B, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
The foundational capabilities of large language models in predicting postoperative risks using clinical notes DOI Creative Commons

Charles Alba,

Bing Xue, Joanna Abraham

et al.

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 11, 2025

Clinical notes recorded during a patient's perioperative journey holds immense informational value. Advances in large language models (LLMs) offer opportunities for bridging this gap. Using 84,875 preoperative and its associated surgical cases from 2018 to 2021, we examine the performance of LLMs predicting six postoperative risks using various fine-tuning strategies. Pretrained outperformed traditional word embeddings by an absolute AUROC 38.3% AUPRC 33.2%. Self-supervised further improved 3.2% 1.5%. Incorporating labels into training increased 1.8% 2%. The highest was achieved with unified foundation model, improvements 3.6% 2.6% compared self-supervision, highlighting foundational capabilities risks, which could be potentially beneficial when deployed care.

Language: Английский

Citations

0
The potential of Large Language Models for social robots in special education DOI Creative Commons
Evdokia Voultsiou, Εleni Vrochidou, Lefteris Moussiades

et al.

Progress in Artificial Intelligence, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

Language: Английский

Citations

0
Federated learning with multi‐cohort real‐world data for predicting the progression from mild cognitive impairment to Alzheimer's disease DOI Creative Commons

Jinqian Pan,

Zhengkang Fan,

Glenn E. Smith

et al.

Alzheimer s & Dementia, Journal Year: 2025, Volume and Issue: 21(4)

Published: April 1, 2025

Leveraging routinely collected electronic health records (EHRs) from multiple health-care institutions, this approach aims to assess the feasibility of using federated learning (FL) predict progression mild cognitive impairment (MCI) Alzheimer's disease (AD). We analyzed EHR data OneFlorida+ consortium, simulating six sites, and used a long short-term memory (LSTM) model with averaging (FedAvg) algorithm. A personalized FL was address between-site heterogeneity. Model performance assessed area under receiver operating characteristic curve (AUC) feature importance techniques. Of 44,899 MCI patients, 6391 progressed AD. models achieved 6% improvement in AUC compared local models. Key predictive features included body mass index, vitamin B12, blood pressure, others. showed promise predicting AD by integrating heterogeneous across institutions while preserving privacy. Despite limitations, it offers potential for future clinical applications. applied record institutions. improved prediction performance, increase identified key features, such as pressure. shows effectiveness handling heterogeneity sites ensuring Personalized pooled generally performed better than global

Language: Английский

Citations

0