AMIA Summits on Translational Science Proceedings

Genetic testing is a valuable tool to guide care of pancreatic cancer patients, yet personal and family uncertainty about the benefits of genetic testing (i.e., decisional conflict) may lead to low adoption. Enabling patients to learn more about genetic testing before their scheduled appointments may help to address this decisional conflict problem. We completed a feasibility assessment of a chatbot to provide genetic education (GEd) with 60 pancreatic cancer patients and using the chatbot to deliver surveys to assess: (a) opinions about the GEd, and (b) decisional conflict about genetic testing...

Transitions of care (TOC) is essential for patients with complex medical needs to maintain the continuity of care. The COVID-19 pandemic may result in unexpected pressure on healthcare organizations' routine work and may burden the TOC system. The objective of this study is to assess TOC structures in pre- and intra-COVID-19 and quantify changes in the structures through the lens of network analysis. We investigated a trauma registry repository consisting of care transitions of 5,674 (2,699 and 2,975 in pre- and intra-COVID-19) inpatients admitted to Vanderbilt University Medical Center (VUMC) between January 2019 and May 2021...

Over 78 million people will suffer from dementia by 2030, emphasizing the need for early identification of patients with mild cognitive impairment (MCI) at risk, and personalized clinical evaluation steps to diagnose potentially reversible causes. Here, we leverage real-world electronic health records in the observational medical outcomes partnership (OMOP) data model to develop machine learning models to predict MCI up to a year in advance of recorded diagnosis. Our experimental results with logistic regression, random forest, and xgboost models trained and evaluated on more than 531K patient visits show random forest model can predict MCI onset with ROC-AUC of 68...

In the United States, primary open-angle glaucoma (POAG) is the leading cause of blindness, especially among African American and Hispanic individuals. Deep learning has been widely used to detect POAG using fundus images as its performance is comparable to or even surpasses diagnosis by clinicians. However, human bias in clinical diagnosis may be reflected and amplified in the widely-used deep learning models, thus impacting their performance. Biases may cause (1) underdiagnosis, increasing the risks of delayed or inadequate treatment, and (2) overdiagnosis, which may increase individuals' stress, fear, well-being, and unnecessary/costly treatment...

Per-/poly-fluoroalkyl substances (PFAS) are a group of manmade compounds with known human toxicity and evidence of contamination in drinking water throughout the US. We augmented our electronic health record data with geospatial information to classify PFAS exposure for our patients living in New Jersey. We explored the utility of three different methods for classifying PFAS exposure that are popularly used in the literature, resulting in different boundary types: public water supplier service area boundary, municipality, and ZIP code...

Cancer-related physical impairments and functional decline affect most patients receiving chemotherapy. Despite evidence that exercise can improve these symptoms, access to exercise-based rehabilitation for cancer patients is limited. Providing telerehabilitation services has shown promising results in alleviating these barriers to access. An in-depth understanding of patient perspectives on cancer telerehabilitation is imperative for the successful development of patient-centered interfaces and functionality...

More than half a million people were experiencing homelessness in America on any given night in 2021, yet only around 50% of them used shelters. To address unmet needs in homelessness, we report the creation of housing for homeless (H4H), the largest comprehensive repository of emergency shelters and other housing resources, from which we deployed state-of-the-art natural language processing approaches to extract information vital to individuals experiencing homelessness, including admission process, service provided, duration of stay, and eligibility...

The increasing death rate over the past eight years due to stroke has prompted clinicians to look for data-driven decision aids. Recently, deep-learning-based prediction models trained with fine-grained electronic health record (EHR) data have shown superior promise for health outcome prediction. However, the use of EHR-based deep learning models for hemorrhagic stroke outcome prediction has not been extensively explored. This paper proposes an ensemble deep learning framework to predict early mortality among ICU patients with hemorrhagic stroke...

Health literacy is the central focus of Healthy People 2030, the fifth iteration of the U.S. national goals and objectives. People with low health literacy usually have trouble understanding health information, following post-visit instructions, and using prescriptions, which results in worse health outcomes and serious health disparities. In this study, we propose to leverage natural language processing techniques to improve health literacy in patient education materials by automatically translating illiterate languages in a given sentence...

Diabetes is associated with heterogeneous behaviors affecting patients' clinical characteristics and trajectories. This study includes 21,288 patients with type 2 diabetes (women, ages 30 to 65). The cohort was filtered through a set of preprocessing heuristics in order to assure the cohort exhibited a similar clinical trajectory. Anomalous characteristics were then identified using dimensionality reduction and anomaly detection methods. Compared to the majority of the cohort, patients classified as anomalous were twice as likely to be admitted into the hospital (7...

Pretrained language models (PLMs) have motivated research on what kinds of knowledge these models learn. Fill-in-the-blanks problem (e.g., cloze tests) is a natural approach for gauging such knowledge. BioLAMA generates prompts for biomedical factual knowledge triples and uses the Top-k accuracy metric to evaluate different PLMs' knowledge. However, existing research has shown that such prompt-based knowledge probing methods can only probe a lower bound of knowledge. Many factors like prompt-based probing biases make the LAMA benchmark unreliable and unstable...

Strategy training is a multidisciplinary rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Strategy training has been shown in randomized, controlled clinical trials to be a more feasible and efficacious intervention for promoting independence than traditional rehabilitation approaches. A standardized fidelity assessment is used to measure adherence to treatment principles by examining guided and directed verbal cues in video recordings of rehabilitation sessions...

As the SARS-CoV-2 virus continues to remain a universal threat on a global scale, a large number of COVID-19 clinical trials and observational studies are being conducted and published. Currently, 9,202 COVID-19 clinical trials have been registered on ClinicalTrials.gov and 293,187 COVID-19 articles were indexed in PubMed. To fully capitalize on the voluminous number of publications reporting COVID-19 interventional and observational studies, their results should be freely accessible via an open-source harmonized shared resource...

We developed a novel data mining pipeline that automatically extracts potential COVID-19 vaccine-related adverse events from a large Electronic Health Record (EHR) dataset. We applied this pipeline to Optum® de-identified COVID-19 EHR dataset containing COVID-19 vaccine records between December 11, 2020 and January 20, 2022. We compared post-vaccination diagnoses between the COVID-19 vaccine group and the influenza vaccine group among 553,682 individuals without COVID-19 infection. We extracted 1,414 ICD-10 diagnosis categories (first three ICD10 digits) within 180 days after the first dose of the COVID-19 vaccine...

Participant recruitment continues to be a challenge to the success of randomized controlled trials, resulting in increased costs, extended trial timelines and delayed treatment availability. Literature provides evidence that study design features (e.g., trial phase, study site involvement) and trial sponsor are significantly associated with recruitment success. Principal investigators oversee the conduct of clinical trials, including recruitment. Through a cross-sectional survey and a thematic analysis of free-text responses, we assessed the perceptions of sixteen principal investigators regarding success factors for participant recruitment...

Imaging examination selection and protocoling are vital parts of the radiology workflow, ensuring that the most suitable exam is done for the clinical question while minimizing the patient's radiation exposure. In this study, we aimed to develop an automated model for the revision of radiology examination requests using natural language processing techniques to improve the efficiency of pre-imaging radiology workflow. We extracted Musculoskeletal (MSK) magnetic resonance imaging (MRI) exam order from the radiology information system at Henry Ford Hospital in Detroit, Michigan...

In Chronic Kidney Disease (CKD), kidneys are damaged and lose their ability to filter blood, leading to a plethora of health consequences that end up in dialysis. Despite its prevalence, CKD goes often undetected at early stages. In order to better understand disease progression, we stratified patients with CKD by considering the time to dialysis from diagnosis of early CKD (stages 1 or 2). To achieve this, we first reduced the number of clinical features in a predictive time-to-dialysis model and identified the top important features on a cohort of ∼ 40, 000 CKD patients...

STREAMLINE is a simple, transparent, end-to-end automated machine learning (AutoML) pipeline for easily conducting rigorous machine learning (ML) modeling and analysis. The initial version is limited to binary classification. In this work, we extend STREAMLINE through implementing multiple regression-based ML models, including linear regression, elastic net, group lasso, and L21 norm. We demonstrate the effectiveness of the regression version of STREAMLINE by applying it to the prediction of Alzheimer's disease (AD) cognitive outcomes using multimodal brain imaging data...

Clinical notes are an essential component of a health record. This paper evaluates how natural language processing (NLP) can be used to identify the risk of acute care use (ACU) in oncology patients, once chemotherapy starts. Risk prediction using structured health data (SHD) is now standard, but predictions using free-text formats are complex. This paper explores the use of free-text notes for the prediction of ACU in leu of SHD. Deep Learning models were compared to manually engineered language features. Results show that SHD models minimally outperform NLP models; an ℓ1 -penalised logistic regression with SHD achieved a C-statistic of 0...

Generating categories and classifications is a common function in life science research; however, categorizing the human population based on "race" remains controversial. There is an awareness and recognition of social-economic disparities with respect to health which are sometimes impacted by someone's ethnicity or race. This work describes an endeavor to develop a computable ontology model to represent a standardization of the concepts surrounding culture, race, ethnicity, and nationality - concepts misrepresented widely...