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

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.

This narrative review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EHRs) and patient-generated health data (PGHD) in clinical decision support. We focused on integrated data that combined PGHD and EHR data, and we investigated the role of artificial intelligence (AI) in the application. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search articles in six databases: PubMed, Embase, Web of Science, Scopus, ACM Digital Library, and IEEE Computer Society Digital Library. In addition, we also synthesized seminal sources, including other systematic reviews, reports, and white papers, to inform the context, history, and development of this field. Twenty-six publications met the review criteria after screening. The EHR-integrated PGHD introduces benefits to health care, including empowering patients and families to engage via shared decision-making, improving the patient-provider relationship, and reducing the time and cost of clinical visits. AI's roles include cleaning and management of heterogeneous datasets, assisting in identifying dynamic patterns to improve clinical care processes, and providing more sophisticated algorithms to better predict outcomes and propose precise recommendations based on the integrated data. Challenges mainly stem from the large volume of integrated data, data standards, data exchange and interoperability, security and privacy, interpretation, and meaningful use. The use of PGHD in health care is at a promising stage but needs further work for widespread adoption and seamless integration into health care systems. AI-driven, EHR-integrated PGHD systems can greatly improve clinicians' abilities to diagnose patients' health issues, classify risks at the patient level by drawing on the power of integrated data, and provide much-needed support to clinics and hospitals. With EHR-integrated PGHD, AI can help transform health care by improving diagnosis, treatment, and the delivery of clinical care, thus improving clinical decision support.

The process of patients waiting for diagnostic examinations after an abnormal screening mammogram is inefficient and anxiety-inducing. Artificial intelligence (AI)-aided interpretation of screening mammography could reduce the number of recalls after screening. We proposed a same-day diagnostic workup to alleviate patient anxiety by employing an AI-aided interpretation to reduce unnecessary diagnostic testing after an abnormal screening mammogram. However, the potential unintended consequences of introducing this workflow in a high-volume breast imaging center are unknown. Using discrete event simulation, we observed that implementing the AI-aided screening mammogram interpretation and same-day diagnostic workflow would reduce daily patient volume by 4%, increase the time a patient would be at the clinic by 24%, and increase waiting times by 13-31%. We discuss how changing the hours of operation and introducing new imaging equipment and personnel may alleviate these negative impacts.

Using physiological data from wearable devices, the study aimed to predict exercise exertion levels by building deep learning classification and regression models. Physiological data were obtained using an unobtrusive chest-worn ECG sensor and portable pulse oximeter from healthy individuals who performed 16-minute cycling exercise sessions. During each session, real-time ECG, pulse rate, oxygen saturation, and revolutions per minute (RPM) data were collected at three intensity levels. Subjects' ratings of perceived exertion (RPE) were collected once per minute. Each 16-minute exercise session was divided into eight 2-minute windows. The self-reported RPEs, heart rate, RPMs, and oxygen saturation levels were averaged for each window to form the predictive features. In addition, heart rate variability (HRV) features were extracted from the ECG for each window. Different feature selection algorithms were used to choose top-ranked predictors. The best predictors were then used to train and test deep learning models for regression and classification analysis. Our results showed the highest accuracy and F1 score of 98.2% and 98%, respectively in training the models. For testing the models, the highest accuracy and F1 score were 80%.

Transgender and nonbinary (TGNB) individuals have an increased risk of certain mental health outcomes, such as depression and suicide attempts. This population skews younger in the United States and prior studies have not included TGNB patients for the entire pediatric age range in an emergency department (ED) setting. The present study aimed to examine gender identity documentation in the electronic health record and then use that information to identify and further characterize the pediatric TGNB population presenting to a psychiatric emergency service. Preliminary findings include a greater percentage of TGNB patients compared to non-TGNB individuals who had repeat visits to the ED for high acuity psychiatric concerns. A larger portion of TGNB patients also had at least one evaluation that included suicidal ideation. These results call for increased attention on the quality of mental healthcare for TGNB youth both inside and outside of the ED.

Mental health challenges are significant global public health concerns, affecting millions of people and impacting individuals, families, and communities alike. Therapists play a crucial role in supporting those with mental health issues by providing emotional, practical, and financial assistance, as well as facilitating access to treatment and services. Utilizing one-to-one interviews is an effective approach that yields valuable transcripts for further study. In this paper, we focus on interview transcripts between therapists and caregivers with family members suffering from dementia. We propose a method to efficiently handle long interview transcripts for classification. Then we employ the Shapley-value based interpretability technique to identify important contents that significantly contribute to classification results and build a corpus containing sentences potentially beneficial to the therapy. This approach offers valuable insights for enhancing the treatment of mental health issues.