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AB1656 THE USE OF MACHINE LEARNING METHODS IN OPIOID-ASSOCIATED OUTCOMES RESEARCH: A SYSTEMATIC REVIEW

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Annals of the Rheumatic Diseases 2023年 82卷 2063-2064页

作者： C. Ramirez Medina J. Benitez-Aurioles D. Jenkins M. Jani The University of Manchester Musculoskeletal and Dermatological Science Centre for Epidemiology Versus Arthritis Manchester United Kingdom The University of Manchester Division of Informatics Imaging and Data Science Manchester United Kingdom NIHR Manchester Biomedical Research Unit Manchester University NHS Foundation Trust Manchester Academic Health Science Centre Manchester United Kingdom

Background Prescription opioids have been a considerable contributor towards the opioid epidemic, a major public health concern with profound economic repercussions and mortality. In order to address the opioid crisis, a need exists to improve understanding on how to personalise prescribing incorporating individual factors to estimate the risk of opioid-related harms. Machine learning (ML) techniques, both supervised and unsupervised, have potential identify patients at risk through the development of risk prediction models and a data-driven redefinition of patient classes. This might be particularly relevant when addressing the changing dynamics of the opioid epidemic, due to its ability to ascertain non-linear relationships between various risk factors and the risk of opioid-related harms. Objectives To perform a systematic review of the use of ML methods in prescription opioid-related harms research. Methods A systematic literature search was conducted using the databases Ovid MEDLINE, PubMed and SCOPUS databases to identify relevant articles that were published from inception to October 2022. Articles were eligible for review if they described studies that applied ML techniques with a primary outcome that was related to prescription opioid harms and using electronic health records as a main data source for the research. We excuded studies that focused patients with cancer or younger than 18 years old. Results A total of 760 abstracts were reviewed, yielding 75 articles for full text review. Four main categories were identified in this area: development of ML algorithms for prediction (n=81, 81%), identification of risk factors n=7, 9%), patient subtype clustering (n=5, 7%) and the application of natural language processing to understand user-generated text for pharmacovigilance (n=2, 3%). Within the studies that developed ML models for prediction, most of them (n=16, 21%) had prolonged postoperative opioid use as their main outcome (the majority looking at arthr

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Validation of a revised instrument to assess implementation readiness of skin-to-skin care in acute or intensive care for neonates born with complex congenital heart disease

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Journal of Neonatal Nursing 2025年第1期31卷 129-134页

作者： Lisanti, Amy Jo Baxelbaum, Keith Kneeland, Tondi Bettencourt, Amanda Vittner, Dorothy University of Pennsylvania School of Nursing Department of Family and Community Health 418 Curie Blvd Philadelphia 19104 PA United States Children's Hospital of Philadelphia Research Institute 734 Schuylkill Ave Philadelphia 19146 PA United States Department of Biomedical and Health Informatics Data Science and Biostatistics Unit The Children's Hospital of Philadelphia Philadelphia PA United States The Ohio State University College of Nursing United States Fairfield University Egan School of Nursing and Health Studies Fairfield CT United States Connecticut Children's Hartford CT United States

An instrument measuring implementation readiness of skin-to-skin care for critically ill premature infants has been previously validated. The aim of this study was to adapt the instrument to the neonatal complex congenital heart disease population and perform initial validation for the revised instrument. A group of experts reviewed the original instrument and adapted items based on existing research and face validity. The revised instrument was distributed to nurses and healthcare professionals caring for neonates with complex congenital heart disease at an international conference and word of mouth from August to December 2023. A total of 158 nurses and 65 healthcare professionals completed the survey. Cronbach's alpha demonstrated strong internal consistency (α = 0.96, 95%CI = 0.94–0.97). Exploratory factor analysis revealed a seven-factor solution provided the strongest fit. This instrument may serve as a useful tool for nurses aiming to enhance the uptake of skin-to-skin care for neonates with complex congenital heart disease. © 2024 Neonatal Nurses Association

关键词： Congenital heart disease Infants Instrument Skin-to-skin care

PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022

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Pacific Symposium on Biocomputing

Pacific Symposium on Biocomputing 2022年 IX-X页

作者： Altman, Russ B. Dunker, A. Keith Hunter, Lawrence Ritchie, Marylyn D. Klein, Teri E. Department of Bioengineering Genetics Medicine & Biomedical Data Science Stanford University United States Department of Biochemistry and Molecular Biology Indiana University School of Medicine United States Department of Pharmacology University of Colorado Health Sciences Center United States Department of Genetics Institute for Biomedical Informatics University of Pennsylvania United States Departments of Biomedical Data Science & Medicine Stanford University United States

Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework

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arXiv 2024年

作者： Averly, Reza Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States College of Pharmacy The Ohio State University United States

Clinical named entity recognition (NER) aims to retrieve important entities within clinical narratives. Recent works have demonstrated that large language models (LLMs) can achieve strong performance in this task. While previous works focus on proprietary LLMs, we investigate how open NER LLMs, trained specifically for entity recognition, perform in clinical NER. Our initial experiment reveals significant contrast in performance for some clinical entities and how a simple exploitment on entity types can alleviate this issue. In this paper, we introduce a novel framework, entity decomposition with filtering, or EDF. Our key idea is to decompose the entity recognition task into several retrievals of entity sub-types and then filter them. Our experimental results demonstrate the efficacies of our framework and the improvements across all metrics, models, datasets, and entity types. Our analysis also reveals substantial improvement in recognizing previously missed entities using entity decomposition. We further provide a comprehensive evaluation of our framework and an in-depth error analysis to pave future works. © 2024, CC BY.

关键词： Wiener filtering

Leveraging Large Language Models for Knowledge-free Weak Supervision in Clinical Natural Language Processing

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arXiv 2024年

作者： Hsu, Enshuo Roberts, Kirk McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston HoustonTX United States Center for Health Data Science & Analytics Houston Methodist HoustonTX United States Enterprise Dev & Integration University of Texas MD Anderson Cancer Center HoustonTX United States

The performance of deep learning-based natural language processing systems is based on large amounts of labeled training data which, in the clinical domain, are not easily available or affordable. Weak supervision and in-context learning offer partial solutions to this issue, particularly using large language models (LLMs), but their performance still trails traditional supervised methods with moderate amounts of gold-standard data. In particular, inferencing with LLMs is computationally heavy. We propose an approach leveraging fine-tuning LLMs and weak supervision with virtually no domain knowledge that still achieves consistently dominant performance. Using a prompt-based approach, the LLM is used to generate weakly-labeled data for training a downstream BERT model. The weakly supervised model is then further fine-tuned on small amounts of gold standard data. We evaluate this approach using Llama2 on three different n2c2 datasets. With no more than 10 gold standard notes, our final BERT models weakly supervised by fine-tuned Llama2-13B consistently outperformed out-of-the-box PubMedBERT by 4.7% to 47.9% in F1 scores. With only 50 gold standard notes, our models achieved close performance to fully fine-tuned systems. © 2024, CC BY-NC-ND.

关键词： Natural language processing systems

Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays

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arXiv 2023年

作者： van der Sluijs, Rogier Bhaskhar, Nandita Rubin, Daniel L. Langlotz, Curtis P. Chaudhari, Akshay S. Department of Radiology Stanford University United States Department of Electrical Engineering Stanford University United States Department of Biomedical Data Science Stanford University United States School of Medicine Stanford University United States Department of Biomedical Informatics Research Stanford University United States

Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques. While augmentation strategies for natural imaging have been studied extensively, medical images are vastly different from their natural counterparts. Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent. To address this challenge, in this study, we systematically assess the effect of various augmentations on the quality and robustness of the learned representations. We train and evaluate Siamese Networks for abnormality detection on chest X-Rays across three large datasets (MIMIC-CXR, CheXpert and VinDr-CXR). We investigate the efficacy of the learned representations through experiments involving linear probing, fine-tuning, zero-shot transfer, and data efficiency. Finally, we identify a set of augmentations that yield robust representations that generalize well to both out-of-distribution data and diseases, while outperforming supervised baselines using just zero-shot transfer and linear probes by up to 20%. Our code is available at https://***/StanfordMIMI/siaug. © 2023, CC BY.

关键词： Large dataset

Genome assembly in the telomere-to-telomere era

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arXiv 2023年

作者： Li, Heng Durbin, Richard Department of Data Science Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Genetics Cambridge University Cambridge United Kingdom

De novo assembly is the process of reconstructing the genome sequence of an organism from sequencing reads. Genome sequences are essential to biology, and assembly has been a central problem in bioinformatics for four decades. Until recently, genomes were typically assembled into fragments of a few megabases at best but technological advances in long-read sequencing now enable near complete chromosome-level assembly, also known as telomere-to-telomere assembly, for many organisms. Here we review recent progress on assembly algorithms and protocols. We focus on how to derive near telomere-to-telomere assemblies and discuss potential future developments. © 2023, CC BY.

关键词： Genes

MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay 39

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MultiSFL: Towards Accurate Split Federated Learning via Mult...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Zeke Hu, Ming Yan, Dengke Liu, Ruixuan Li, Anran Xie, Xiaofei Chen, Mingsong MoE Engineering Research Center of SW/HW Co-Design Tech. and App. East China Normal University China School of Computing and Information Systems Singapore Management University Singapore Department of Biomedical Informatics & Data Science School of Medicine Yale University United States

ISBN: (纸本)157735897X

Although Split Federated Learning (SFL) effectively enables knowledge sharing among resource-constrained clients, it suffers from low training performance due to the neglect of data heterogeneity and catastrophic forgetting problems. To address these issues, we propose a novel SFL approach named MultiSFL, which adopts i) an effective multi-model aggregation mechanism to alleviate gradient divergence caused by heterogeneous data and ii) a novel knowledge replay strategy to deal with the catastrophic forgetting problem. MultiSFL adopts two servers (i.e., the fed server and main server) to maintain multiple branch models for local training and an aggregated master model for knowledge sharing among branch models. To mitigate catastrophic forgetting, the main server of MultiSFL selects multiple assistant devices for knowledge replay according to the training data distribution of each full branch model. Experimental results obtained from various non-IID and IID scenarios demonstrate that MultiSFL significantly outperforms conventional SFL methods by up to a 23.25% test accuracy improvement. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

Repurposing Drugs for Alzheimer's Diseases through Link Prediction on biomedical Literature 11

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Repurposing Drugs for Alzheimer's Diseases through Link Pred...

11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Xiao, Yongkang Hou, Yu Zhou, Huixue Diallo, Gayo Fiszman, Marcelo Wolfson, Julian Kilicoglu, Halil Chen, You Xu, Hua Mantyh, William G. Zhang, Rui University of Minnesota Institute for Health Informatics MinneapolisMN United States University of Minnesota Department of Surgery MinneapolisMN United States University of Bordeaux Inria Sistm Team AHeaD Inserm 1219 Bordeaux Population Health F-33000 France Pontifical Catholic University of Rio de Janeiro Nites - Núcleo de Inovação e Tecnologia em Saúde Brazil University of Minnesota Division of Biostatistics School of Public Health MinneapolisMN United States University of Illinois Urbana-Champaign School of Information Sciences ChampaignIL United States Vanderbilt University Medical Center Department of Biomedical Informatics NashvilleTN United States Yale University Section of Biomedical Informatics and Data Science New HavenCT United States University of Minnesota Department of Neurology MinneapolisMN United States

ISBN: (纸本)9798350302639

Recently, computational drug repurposing has emerged as a promising method for identifying new interventions for diseases. This study predicts novel drugs for Alzheimer's disease (AD) through link prediction on our developed biomedical knowledge graph. We constructed a comprehensive knowledge graph containing AD concepts and various potential interventions, called ADInt, by integrating a dietary supplement (DS) domain knowledge graph, SuppKG, with semantic triples from SemMedDB database. Four knowledge graph embedding models (TransE, RotatE, DistMult and ComplEX) and two graph convolutional network models (R-GCN and CompGCN) were compared to learn the representation of ADInt. R-GCN outperformed other models by evaluating on the time slice test set and the clinical trial test set, and was used to generate the score tables for the link prediction task. According to the results of link prediction, we proposed candidate drugs for AD. In conclusion, we presented a novel methodology to extend an existing knowledge graph and discover novel drugs for AD. Our method can potentially be applied to other clinical problems, such as discovering drug adverse reactions and drug-drug interactions. © 2023 IEEE.

关键词： Knowledge graph