AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献

This study describes the deployment process of an AI-driven clinical decision support (CDS) system to support postpartum depression (PPD) prevention, diagnosis and management. Central to this CDS is an L2-regularized logistic regression model trained on electronic health record (EHR) data at an academic medical center, and subsequently refined through a broader dataset from a consortium to ensure its generalizability and fairness. The deployment architecture leveraged Microsoft Azure to facilitate a scalable, secure, and efficient operational framework. We used Fast Healthcare Interoperability Resources (FHIR) for data extraction and ingestion between the two systems. Continuous Integration/Continuous Deployment pipelines automated the deployment and ongoing maintenance, ensuring the system's adaptability to evolving clinical data. Along the technical preparation, we focused on a seamless integration of the CDS within the clinical workflow, presenting risk assessment directly within the clinician schedule and providing options for subsequent actions. The developed CDS is expected to drive a PPD clinical pathway to enable efficient PPD risk management.

Extracting valuable insights from unstructured clinical narrative reports is a challenging yet crucial task in the healthcare domain as it allows healthcare workers to treat patients more efficiently and improves the overall standard of care. We employ ChatGPT, a Large language model (LLM), and compare its performance to manual reviewers. The review focuses on four key conditions: family history of heart disease, depression, heavy smoking, and cancer. The evaluation of a diverse sample of History and Physical (H&P) Notes, demonstrates ChatGPT's remarkable capabilities. Notably, it exhibits exemplary results in sensitivity for depression and heavy smokers and specificity for cancer. We identify areas for improvement as well, particularly in capturing nuanced semantic information related to family history of heart disease and cancer. With further investigation, ChatGPT holds substantial potential for advancements in medical information extraction.

The purpose of this study was to characterize opioid and antimicrobial prescribing among uninsured patients seeking emergency medical care and to build predictive machine learning models. Uninsured patients were less likely to receive an opioid medication, more likely to receive non-opioid alternatives, and less likely to receive an antimicrobial prescription. The most impactful contributing factors were housing status, comorbidities, and recidivism.

Clinical trials are critical to many medical advances; however, recruiting patients remains a persistent obstacle. Automated clinical trial matching could expedite recruitment across all trial phases. We detail our initial efforts towards automating the matching process by linking realistic synthetic electronic health records to clinical trial eligibility criteria using natural language processing methods. We also demonstrate how the Sørensen-Dice Index can be adapted to quantify match quality between a patient and a clinical trial.

Researchers estimate the number of dementia patients to triple by 2050¹. Dementia seldom occurs in isolation; it's frequently accompanied by other health conditions². The coexistence of conditions further complicates the management of dementia. In this study, we embarked on an innovative approach, applying association rule mining to analyze National Alzheimer's Coordinating Center (NACC) data. First, we completed a literature review on the utilization of association rules, heatmaps, and network analysis to detect and visualize comorbidities. Then, we conducted a secondary data analysis on the NACC data using association rule mining. This algorithm uncovers associations of comorbidities that are diagnosed together in patients who have Alzheimer's disease and related dementias (ADRD). Also, for these patients, the algorithm provides the probability of a patient developing another comorbidity given the diagnosis of an associated comorbidity. These findings can enhance treatment planning, advance research on high-association diseases, and ultimately enhance healthcare for dementia patients.

This study aims to propose a novel approach for enhancing clinical prediction models by combining structured and unstructured data with multimodal data fusion. We presented a comprehensive framework that integrated multimodal data sources, including textual clinical notes, structured electronic health records (EHRs), and relevant clinical data from National Electronic Injury Surveillance System (NEISS) datasets. We proposed a novel hybrid fusion method, which incorporated state-of-the-art pre-trained language model, to integrate unstructured clinical text with structured EHR data and other multimodal sources, thereby capturing a more comprehensive representation of patient information. The experimental results demonstrated that the hybrid fusion approach significantly improved the performance of clinical prediction models compared to traditional fusion frameworks and unimodal models that rely solely on structured data or text information alone. The proposed hybrid fusion system with RoBERTa language encoder achieved the best prediction of the Top 1 injury with an accuracy of 75.00% and Top 3 injuries with an accuracy of 93.54%. Our study highlights the potential of integrating natural language processing (NLP) techniques with multimodal data fusion for enhancing clinical prediction models' performances. By leveraging the rich information present in clinical text and combining it with structured EHR data, the proposed approach can improve the accuracy and robustness of predictive models. The approach has the potential to advance clinical decision support systems, enable personalized medicine, and facilitate evidence-based health care practices. Future research can further explore the application of this hybrid fusion approach in real-world clinical settings and investigate its impact on improving patient outcomes.

In the realm of lung cancer treatment, where genetic heterogeneity presents formidable challenges, precision oncology demands an exacting approach to identify and hierarchically sort clinically significant somatic mutations. Current Next-Generation Sequencing (NGS) data filtering pipelines, while utilizing various external databases for mutation screening, often fall short in comprehensive integration and flexibility needed to keep pace with the evolving landscape of clinical data. Our study introduces a sophisticated NGS data filtering system, which not only aggregates but effectively synergizes diverse data sources, encompassing genetic variants, gene functions, clinical evidence, and an extensive body of literature. This system is distinguished by a unique algorithm that facilitates a rigorous, multi-tiered filtration process. This allows for the efficient prioritization of 420 genes and 1,193 variants from large datasets, with a particular focus on 80 variants demonstrating high clinical actionability. These variants have been aligned with FDA approvals, NCCN guidelines, and thoroughly reviewed literature, thereby equipping oncologists with a refined arsenal for targeted therapy decisions. The innovation of our system lies in its dynamic integration framework and its algorithm, tailored to emphasize clinical utility and actionability-a nuanced approach often lacking in existing methodologies. Our validation on real-world lung adenocarcinoma NGS datasets has shown not only an enhanced efficiency in identifying genetic targets but also the potential to streamline clinical workflows, thus propelling the advancement of precision oncology. Planned future enhancements include expanding the range of integrated data types and developing a user-friendly interface, aiming to facilitate easier access to data and promote collaborative efforts in tailoring cancer treatments.

The goal of this study was to analyze diagnostic discrepancies between emergency department (ED) and hospital discharge diagnoses in patients with congestive heart failure admitted to the ED. Using a synthetic dataset from the Department of Veterans Affairs, the patients' primary diagnoses were compared at two levels: diagnostic category and body system. With 12,621 patients and 24,235 admission cases, the study found a 58% mismatch rate at the category level, which was reduced to 30% at the body system level. Diagnostic categories associated with higher levels of mismatch included aplastic anemia, pneumonia, and bacterial infections. In contrast, diagnostic categories associated with lower levels of mismatch included alcohol-related disorders, COVID-19, cardiac dysrhythmias, and gastrointestinal hemorrhage. Further investigation revealed that diagnostic mismatches are associated with longer hospital stays and higher mortality rates. These findings highlight the importance of reducing diagnostic uncertainty, particularly in specific diagnostic categories and body systems, to improve patient care following ED admission.

Motivation: The proliferation of genetic testing and consumer genomics represents a logistic challenge to the personalized use of GWAS data in VCF format. Specifically, the challenge of retrieving target genetic variation from large compressed files filled with unrelated variation information. Compounding the data traversal challenge, privacy-sensitive VCF files are typically managed as large stand-alone single files (no companion index file) composed of variable-sized compressed chunks, hosted in consumer-facing environments with no native support for hosted execution. Results: A portable JavaScript module was developed to support in-browser fetching of partial content using byte-range requests. This includes on-the-fly decompressing irregularly positioned compressed chunks, coupled with a binary search algorithm iteratively identifying chromosome-position ranges. The in-browser zero-footprint solution (no downloads, no installations) enables the interoperability, reusability, and user-facing governance advanced by the FAIR principles for stewardship of scientific data. Availability - https://episphere.github.io/vcf, including supplementary material.

Radiology Imaging plays a pivotal role in medical diagnostics, providing clinicians with insights into patient health and guiding the next steps in treatment. The true value of a radiological image lies in the accuracy of its accompanying report. To ensure the reliability of these reports, they are often cross-referenced with operative findings. The conventional method of manually comparing radiology and operative reports is labor-intensive and demands specialized knowledge. This study explores the potential of a Large Language Model (LLM) to simplify the radiology evaluation process by automatically extracting pertinent details from these reports, focusing especially on the shoulder's primary anatomical structures. A fine-tuned LLM identifies mentions of the supraspinatus tendon, infraspinatus tendon, subscapularis tendon, biceps tendon, and glenoid labrum in lengthy radiology and operative documents. Initial findings emphasize the model's capability to pinpoint relevant data, suggesting a transformative approach to the typical evaluation methods in radiology.