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Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history

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Heliyon 2025年第1期11卷 e41350页

作者： Pham, Anh El-Kareh, Robert Myers, Frank Ohno-Machado, Lucila Kuo, Tsung-Ting Division of Biomedical Informatics Department of Medicine University of California San Diego La Jolla CA United States UCSD Health System San Diego CA United States Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Department of Surgery Yale School of Medicine New Haven CT United States

Background: Clostridioides difficile infection is a major health threat. Healthcare institutions have strong medical and financial incentives to keep infections under control. Blanket testing at admission is in general not recommended, and current predictive models either used moderate sample sizes, over-inflated the number of covariates, or chose non-interpretable algorithms. We aim to develop models using patient data to predict positive Clostridioides difficile test results with discrimination performance, interpretable results, and a reasonable number of covariates that reflect health over a long-time span. Materials and methods: We processed records from 157,493 University of California San Diego Health patients seen between January 01, 2016–July 03, 2019 with at least 6 months of medication history, excluding pregnant women, patients under 18, and prisoners. Three models (Logistic Regression, Random Forest, and Ensemble) were constructed using hyper-parameters selected through 10-fold cross-validation. Model performance was measured by the Area Under the Receiver Operating Characteristic Curve (AUROC). The model coefficients’ odds ratios and p-values were calculated for the Logistic Regression model, as were Gini indices for Random Forest. Decision boundary analysis was conducted using pair-wise false positive and false negative cases each model would predict at a specific threshold. Results: Logistic Regression, Random Forest, and Ensemble models yielded test AUROCs of 0.839, 0.851, and 0.866, respectively. Significant covariates that may affect risk include age, immuno-compromised treatments, past antibiotic uses, and some medications for the gastrointestinal tract. Conclusions: The models achieve high discrimination performance (AUROC >0.83). There is a general consensus among different analysis approaches regarding predictors that impact patients' chances of having a positive test, which may influence Clostridioides difficile risk, including features clini

关键词： Clostridioides difficile infection Decision support systems Electronic health record Machine learning

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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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BiomedRAG: A Retrieval augmented Large Language Model for Biomedicine

arXiv

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arXiv 2024年

作者： Li, Mingchen Kilicoglu, Halil Xu, Hua Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Information Sciences University of Illinois at Urbana-Champaign Champaign United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains;however, these models encounter issues such as generating inaccurate information or hallucinations. Retrieval-augmented generation provided a solution for these models to update knowledge and enhance their performance. In contrast to previous retrieval-augmented LMs, which utilize specialized cross-attention mechanisms to help LLM encode retrieved text, BiomedRAG adopts a simpler approach by directly inputting the retrieved chunk-based documents into the LLM. This straightforward design is easily applicable to existing retrieval and language models, effectively bypassing noise information in retrieved documents, particularly in noise-intensive tasks. Moreover, we demonstrate the potential for utilizing the LLM to supervise the retrieval model in the biomedical domain, enabling it to retrieve the document that assists the LM in improving its predictions. Specifically, BIOMEDRAG retrieves relevant documents from a specifically curated, diverse chunk database through a unique, purpose-built chunk scoring mechanism by a tunable scorer. This retrieval process then integrates the selected information directly into the LLM's input, facilitating the generation of expected output, such as structured knowledge. Our experiments reveal that with the tuned scorer, BIOMEDRAG attains superior performance across 4 biomedical NLP tasks, encompassing information extraction (triple extraction, relation extraction), text classification, and link prediction leveraging over 8 datasets. For instance, in the triple extraction task, BIOMEDRAG outperforms other triple extraction systems with micro-F1 scores of 81.42 and 88.83 on GIT and ChemProt corpora, respectively. The outstanding performance underscores the potential of BIOMEDRAG in constructing effective biomedical intervention systems for various tasks. © 2024, CC BY.

关键词： Classification (of information)

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SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search

arXiv

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arXiv 2024年

作者： Du, Hanwen Peng, Bo Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Division of Medicinal Chemistry and Pharmacognosy College of Pharmacy 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

Conversational Recommender Systems (CRS) proactively engage users in interactive dialogues to elicit user preferences and provide personalized recommendations. Existing methods train Reinforcement Learning (RL)-based agent with greedy action selection or sampling strategy, and may suffer from suboptimal conversational planning. To address this, we present a novel Monte Carlo Tree Search (MCTS)-based CRS framework SAPIENT. SAPIENT consists of a conversational agent (S-agent) and a conversational planner (S-planner). S-planner builds a conversational search tree with MCTS based on the initial actions proposed by S-agent to find conversation plans. The best conversation plans from S-planner are used to guide the training of S-agent, creating a self-training loop where S-agent can iteratively improve its capability for conversational planning. Furthermore, we propose an efficient variant SAPIENT-e for trade-off between training efficiency and performance. Extensive experiments on four benchmark datasets validate the effectiveness of our approach, showing that SAPIENT outperforms the state-of-the-art baselines. © 2024, CC BY.

关键词： Chatbots

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Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach

Adverse Drug Reaction Posts Detection with a Bi-LSTM based a...

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International Conference on Big data and Smart Computing (BIGCOMP)

作者： Chung-Chun Lee Seunghee Lee Mi Hwa Song Suehyun Lee Jong-Yeop Kim Department of Biomedical Informatics College of Medicine Konyang University Daejeon Republic of Korea Healthcare Data Science Center Konyang Univ. Medical Center Daejeon Republic of Korea Division of Information and Communication Sciences Semyung University Jecheon Republic of Korea

Social Network Services (SNS) is currently the most active communication platform, generating big data every day. In the field of pharmacovigilance, it is also an interesting research topic to extract meaningful information from accumulated data from SNS data. In this work, we propose a Recurrent Neural Network (RNN) based classification model to extract Adver Drug Reactions (ADR) posts from social network service (SNS) data. For Ketoprofen drugs with high prescription frequency and high number of posts, posts from Naver Blog and Cafe (2005-2020) were secured, and the final 3,828 cases were analyzed. As a result, three types of lexicons (drugs name, ADR, and stop words) were defined for Ketoprofen, and based on this, 87% accuracy was obtained based on the Bi-LSTM classification model. Extra drug was also verified through the entire process above, with an accuracy of 80%. It is expected to provide convenience in extracting ADR posts through the developed Bi-LSTM classification model.

关键词： Drugs Recurrent neural networks Social networking (online) Filtering Computational modeling Blogs Big data

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Biomedrag: A Retrieval Augmented Large Language Model for Biomedicine

SSRN

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SSRN 2024年

作者： Li, Mingchen Kilicoglu, Halil Xu, Hua Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

Retrieval-augmented generation (RAG) offers a solution by retrieving knowledge from an established database to avoid hallucination of large language models (LLM). However, these models utilize specialized cross-attention mechanisms to help LLMs encode the retrieved text, hindering the application of RAG in closed LLMs (such as GPT-4). Additionally, these models retrieve information at the sentence or paragraph level, potentially introducing noise and affecting the generation quality. To address these issues, we propose a novel BiomedRAG framework that directly feeds automatically retrieved chunk-based documents into the LLM. Our evaluation of BiomedRAG across four biomedical natural language processing tasks using eight datasets demonstrates that our proposed framework not only improves the performance of LLMs 12.12% averagely but also achieves state of-the-art results compared to various baseline models by 5.33%. The BiomedRAG paves the way for more accurate and adaptable LLM applications in the biomedical domain. © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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Pupillometry examinations of the human eye with the eye diagnostic device PEP-2000-First results

Pupillometry examinations of the human eye with the eye diag...

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作者： Schieding, Nikola Reuter, Thomas Grundmann, Andreas Walther, Sebastian Klee, Sascha ICM-Institut Chemnitzer Maschinen-und Anlagenbau E.V. Otto-SchmerbachStr. 19 Chemnitz09117 Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Tu Ilmenau Ilmenau Germany

Pupillometry forms the diagnostic basis for numerous pathologies of the eye. For this reason, fast and accurate diagnostics in the field of ophthalmology are essential. Two examination techniques, full-field ERG and pupillometry were combined in a diagnostic device developed by ICM e.V. to reduce the examination process for both examiners and patients. In this paper, the device is examined for the quality of the pupillometry measurements. A study with 12 healthy subjects (3 f, 9 m, 36.33 ± 11.94 years) was conducted to evaluate the device. The results showed that the minimal pupil diameter is 40 % higher than the literature values. The main reason for the differences is the low light intensity of 15 cd/m2. However, the maximum pupil diameter is within the range of the researched values. The results of the pupillary reaction measurements show that the values obtained (amplitude, contraction time and peak time) are within the range of literature values. The latency time of 690 ms is 40 % too high. The reason for this could be the moderate pupil detection rate of 50-70 %. Nevertheless, plausible and comparable analysis values could be obtained with the eye diagnostic device PEP-2000. Further work will look at improving pupil detection rates. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Ophthalmology

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

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

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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

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Heightened COVID-19 Mortality in People With Severe Mental Illness Persists After Vaccination: A Cohort Study of Greater Manchester Residents (vol 49, pg 275, 2023)

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SCHIZOPHRENIA BULLETIN 2023年第5期49卷 1399-1403页

作者： Hassan, Lamiece Sawyer, Chelsea Peek, Niels Lovell, Karina Carvalho, Andre F. Solmi, Marco Tilston, George Sperrin, Matthew Firth, Joseph Division of Psychology and Mental Health The University of Manchester Manchester Academic Health Science Centre Manchester M13 9PL UK Centre for Health Informatics Division of Informatics Imaging and Data Sciences The University of Manchester M13 9PL UK NIHR Greater Manchester Patient Safety Translational Research Centre The University of Manchester Manchester UK Manchester Academic Health Science Centre National Institute for Health Research Manchester Biomedical Research Centre The University of Manchester Manchester M13 9PL UK Division of Nursing Midwifery and Social Work University of Manchester Manchester Academic Health Science Centre Manchester M13 9PL UK IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre School of Medicine Barwon Health Deakin University Geelong Victoria Australia Psychiatry Department University of Ottawa Ottawa ON Canada The Ottawa Hospital University of Ottawa Ottawa ON Canada Clinical Epidemiology Program Ottawa Hospital Research Institute (OHRI) University of Ottawa Ottawa ON Canada Greater Manchester Mental Health NHS Foundation Trust Manchester Academic Health Science Centre Manchester M13 9PL UK

Background and hypothesis Previous studies show that people with severe mental illness (SMI) are at higher risk of COVID-19 mortality, however limited evidence exists regarding risk post-vaccination. We investigated COVID-19 mortality among people with schizophrenia and other SMIs before, during and after the UK vaccine roll-out. Study Design Using the Greater Manchester (GM) Care Record to access routinely collected health data linked with death records, we plotted COVID-19 mortality rates over time in GM residents with schizophrenia/psychosis, bipolar disorder (BD) and/or recurrent major depressive disorder (MDD) from February 2020 to September 2021. Multivariable logistic regression was used to compare mortality risk (risk ratios;RRs) between people with SMI (N=190,188) and age-sex matched controls (N=760,752), adjusted for sociodemographic factors, pre-existing comorbidities and vaccination *** Results Mortality risks were significantly higher among people with SMI compared with matched controls, particularly among people with schizophrenia/psychosis (RR 3.14, CI 2.66-3.71) and/or BD (RR 3.17, CI 2.15-4.67). In adjusted models, the relative risk of COVID-19 mortality decreased, though remained significantly higher than matched controls for people with schizophrenia (RR 1.53, CI 1.24-1.88) and BD (RR 2.28, CI 1.49-3.49), but not recurrent MDD (RR 0.92, CI 0.78-1.09). People with SMI continued to show higher mortality rate ratios relative to controls throughout 2021, during vaccination roll-out. Conclusions People with SMI, notably schizophrenia and bipolar disorder, were at greater risk of COVID-19 mortality compared to matched controls. Despite population vaccination efforts that have prioritised people with SMI, disparities still remain in COVID-19 mortality for people with SMI.

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Few-Shot Learning in Diffusion Models for Generating Cerebral Aneurysm Geometries

Few-Shot Learning in Diffusion Models for Generating Cerebra...

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IEEE International Symposium on biomedical Imaging

作者： Yash Deo Fengming Lin Haoran Dou Nina Cheng Nishant Ravikumar Alejandro F Frangi Toni Lassila CISTIB University of Leeds Leeds UK NIHR Leeds Biomedical Research Centre Leeds UK Alan Turing Institute London UK Electrical Engineering and Cardiovascular Sciences Departments Medical Imaging Research Center (MIRC) KU Leuven Leuven Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester UK

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The study of brain vessel pathologies is critical for the advancement o f neurovascular medicine, yet researchers often face significant hurdles due to the scarcity of imaging data for certain uncommon types of aneurysms. Generative deep learning models have been proposed to address the lack of high-quality labeled medical images - however, the shortage of data also presents a unique challenge in training generative models. To address this issue, our work explores the efficacy of training latent diffusion models (LDMs) with few-shot learning, enabling the generation of detailed vessel segmentations from as few as five images per class. By incorporating set-based vision transformers for class embeddings and leveraging signed distance functions (SDFs) as a novel form of conditioning, our method reduces the need for extensive datasets for training. Comparative studies with established generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), highlight the robustness of our approach. Our model not only successfully generates high-quality segmentations of brain vessels with aneurysms but also significantly outperforms the standard generative models.

关键词： Training Image segmentation Biological system modeling Aneurysm Brain modeling Diffusion models Transformers

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