Criteria2Query 3.0: Leveraging generative large language models for clinical trial eligibility query generation
- Jimyung Park,
- Yilu Fang,
- Casey Ta,
- Gongbo Zhang,
- Betina Idnay,
- Fangyi Chen,
- David Feng,
- Rebecca Shyu,
- Emily R. Gordon,
- Matthew Spotnitz,
- Chunhua Weng
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Abstract ObjectiveAutomated identification of eligible patients is a bottleneck of clinical research. We propose Criteria2Query (C2Q) 3.0, a system that leverages GPT-4 for the semi-automatic transformation of clinical trial eligibility criteria text ...
A roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness
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Abstract ObjectivesWe evaluated methods for preparing electronic health record data to reduce bias before applying artificial intelligence (AI).
MethodsWe created methods for transforming raw data into a data framework for applying machine learning and ...
Towards Machine-FAIR: Representing software and datasets to facilitate reuse and scientific discovery by machines
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Abstract ObjectiveTo use software, datasets, and data formats in the domain of Infectious Disease Epidemiology as a test collection to evaluate a novel M1 use case, which we introduce in this paper. M1 is a machine that upon receipt of a new digital ...
Opportunities for incorporating intersectionality into biomedical informatics
- Oliver J. Bear Don't Walk,
- Amandalynne Paullada,
- Avery Everhart,
- Reggie Casanova-Perez,
- Trevor Cohen,
- Tiffany Veinot
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AbstractMany approaches in biomedical informatics (BMI) rely on the ability to define, gather, and manipulate biomedical data to support health through a cyclical research-practice lifecycle. Researchers within this field are often fortunate to work ...
Clinical trial recommendations using Semantics-Based inductive inference and knowledge graph embeddings
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Abstract ObjectiveDesigning a new clinical trial entails many decisions, such as defining a cohort and setting the study objectives to name a few, and therefore can benefit from recommendations based on exhaustive mining of past clinical trial records. ...
A framework for longitudinal latent factor modelling of treatment response in clinical trials with applications to Psoriatic Arthritis and Rheumatoid Arthritis
- Fabian Falck,
- Xuan Zhu,
- Sahra Ghalebikesabi,
- Matthias Kormaksson,
- Marc Vandemeulebroecke,
- Cong Zhang,
- Ruvie Martin,
- Stephen Gardiner,
- Chun Hei Kwok,
- Dominique M. West,
- Luis Santos,
- Chengeng Tian,
- Yu Pang,
- Aimee Readie,
- Gregory Ligozio,
- Kunal K. Gandhi,
- Thomas E. Nichols,
- Ann-Marie Mallon,
- Luke Kelly,
- David Ohlssen,
- George Nicholson
Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, ...
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Identifying gene expression programs in single-cell RNA-seq data using linear correlation explanation
- Yulia I. Nussbaum,
- K.S.M. Tozammel Hossain,
- Jussuf Kaifi,
- Wesley C. Warren,
- Chi-Ren Shyu,
- Jonathan B. Mitchem
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Abstract ObjectiveGene expression analysis through single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of gene regulation in diverse cell types, tissues, and organisms. While existing methods primarily focus on identifying cell ...
Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models
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Abstract BackgroundAdvances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients ...
Automated annotation of disease subtypes
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Abstract BackgroundDistinguishing diseases into distinct subtypes is crucial for study and effective treatment strategies. The Open Targets Platform (OT) integrates biomedical, genetic, and biochemical datasets to empower disease ontologies, ...
Chat-ePRO: Development and pilot study of an electronic patient-reported outcomes system based on ChatGPT
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Abstract ObjectiveChatbots have the potential to improve user compliance in electronic Patient-Reported Outcome (ePRO) system. Compared to rule-based chatbots, Large Language Model (LLM) offers advantages such as simplifying the development process and ...
Cost prediction for ischemic heart disease hospitalization: Interpretable feature extraction using network analysis
: Ischemic heart disease (IHD) is a significant contributor to global mortality and disability, imposing a substantial social and economic burden on individuals and healthcare systems. To enhance the efficient allocation of medical ...
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Highlights
- We presented an interpretable network-based approach for medical code utilization.
- It balanced predictability and interpretability in IHD hospitalization cost prediction.
- We identified key factors linked to higher costs, aiding ...
How can we reward you? A compliance and reward ontology (CaRO) for eliciting quantitative reward rules for engagement in mHealth app and healthy behaviors
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Abstract ObjectiveWhen developing mHealth apps with point reward systems, knowledge engineers and domain experts should define app requirements capturing quantitative reward patterns that reflect patient compliance with health behaviors. This is a ...
A survey of recent methods for addressing AI fairness and bias in biomedicine
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Abstract ObjectivesArtificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to ...
