Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes
AMIA Annu Symp Proc. 2024 Jan 11;2023:923-932. eCollection 2023.ABSTRACTNatural Language Processing (NLP) methods have been broadly applied to clinical tasks. Machine learning and deep learning approaches have been used to improve the performance of clinical NLP. However, these approaches require sufficiently large datasets for training, and trained models have been shown to transfer poorly across sites. These issues have led to the promotion of data collection and integration across different institutions for accurate and portable models. However, this can introduce a form of bias called confounding by provenance. When so...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Xiruo Ding Zhecheng Sheng Meliha Yeti şgen Serguei Pakhomov Trevor Cohen Source Type: research
Semantically oriented EHR navigation with a patient specific knowledge base and a clinical context ontology
We report the process to create a CCO, which guides annotation of structured and narrative patient data to produce a PSKB. We also present an application of our PSKB using real patient data displayed on a semantically oriented patient summary to improve EHR navigation. Our approach can potentially save precious time spent by clinicians using today's EHRs, by showing a chronological view of the patient's record along with contextual statements needed for care decisions with minimum effort. We propose several other applications of a PSKB to improve multiple EHR functions to guide future research.PMID:38222434 | PMC:PMC107859...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Tiago K Colicchio John D Osborne Clementino V Do Rosario Ankit Anand Nicholas A Timkovich Matthew C Wyatt James J Cimino Source Type: research
Evaluation of Discrepancies Among National Library of Medicine (NLM) Value Set Authority Center (VSAC) ICD-10-CM Value Sets: Case Study for Diagnoses of Common Chronic Conditions, Implications, and Potential Solutions
AMIA Annu Symp Proc. 2024 Jan 11;2023:1087-1095. eCollection 2023.ABSTRACTThe National Library of Medicine (NLM)'s Value Set Authority Center (VSAC) is a crowd-sourced repository with a potential for substantial discrepancy among value sets for the same clinical concepts. To characterize this potential problem, we identified the most common chronic conditions affecting US adults and assessed for discrepancy among VSAC ICD-10-CM value sets for these conditions. An analysis of 32 value sets for 12 conditions identified that a median of 45% of codes for a given condition were potentially problematic (included in at least one,...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Nikolay Lukyanchikov Kensaku Kawamoto Source Type: research
Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasets
AMIA Annu Symp Proc. 2024 Jan 11;2023:669-678. eCollection 2023.ABSTRACTElectronic medical records (EMRs) are stored in relational databases. It can be challenging to access the required information if the user is unfamiliar with the database schema or general database fundamentals. Hence, researchers have explored text-to-SQL generation methods that provide healthcare professionals direct access to EMR data without needing a database expert. However, currently available datasets have been essentially "solved" with state-of-the-art models achieving accuracy greater than or near 90%. In this paper, we show that there is sti...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Richard Tarbell Kim-Kwang Raymond Choo Glenn Dietrich Anthony Rios Source Type: research
Unsupervised SoftOtsuNet Augmentation for Clinical Dermatology Image Classifiers
AMIA Annu Symp Proc. 2024 Jan 11;2023:329-338. eCollection 2023.ABSTRACTData Augmentation is a crucial tool in the Machine Learning (ML) toolbox because it can extract novel, useful training images from an existing dataset, thereby improving accuracy and reducing overfitting in a Deep Neural Network (DNNs). However, clinical dermatology images often contain irrelevant background information,such as furniture and objects in the frame. DNNs make use of that information when optimizing the loss function. Data augmentation methods that preserve this information risk creating biases in the DNN's understanding (for example, that...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Miguel Dominguez John T Finnell Source Type: research
Probabilistic Prediction of Laboratory Test Information Yield
In this study, we assess whether stability in repeated laboratory diagnostic measurements is predictable with uncertainty estimates using electronic health record data available before the diagnostic is ordered. We use probabilistic regression to predict a distribution of plausible values, allowing use-time customization for various definitions of "stability" given dynamic ranges and clinical scenarios. After converting distributions into "stability" scores, the models achieve a sensitivity of 29% for white blood cells, 60% for hemoglobin, 100% for platelets, 54% for potassium, 99% for albumin and 35% for creatinine for pr...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yixing Jiang Andrew H Lee Xiaoyuan Ni Conor K Corbin Jeremy A Irvin Andrew Y Ng Jonathan H Chen Source Type: research
Leveraging Unlabeled Clinical Data to Boost Performance of Risk Stratification Models for Suspected Acute Coronary Syndrome
AMIA Annu Symp Proc. 2024 Jan 11;2023:744-753. eCollection 2023.ABSTRACTThe performance of deep learning models in the health domain is desperately limited by the scarcity of labeled data, especially for specific clinical-domain tasks. Conversely, there are vastly available clinical unlabeled data waiting to be exploited to improve deep learning models where their training labeled data are limited. This paper investigates the use of task-specific unlabeled data to boost the performance of classification models for the risk stratification of suspected acute coronary syndrome. By leveraging large numbers of unlabeled clinica...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yutong Wu David Conlan Siegfried Perez Anthony Nguyen Source Type: research
Digital Solutions Observed in Clinical Trials: A Formative Feasibility Scoping Review
AMIA Annu Symp Proc. 2024 Jan 11;2023:987-996. eCollection 2023.ABSTRACTGrowing digital access accelerates digital transformation of clinical trials where digital solutions (DSs) are increasingly and widely leveraged for improving trial efficiency, effectiveness, and accessibility. Many factors impact DS success including technology barriers, privacy concerns, or user engagement activities. It is unclear how those factors are considered or reported in the literature. Here, we perform a formative feasibility scoping review to identify gaps impacting DS quality and reproducibility in trials. Articles containing digital terms...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Taylor M Harrison Sungrim Moon Liwei Wang Sunyang Fu Hongfang Liu Source Type: research
Bridging the Skills Gap: Evaluating an AI-Assisted Provider Platform to Support Care Providers with Empathetic Delivery of Protocolized Therapy
AMIA Annu Symp Proc. 2024 Jan 11;2023:436-445. eCollection 2023.ABSTRACTDespite the high prevalence and burden of mental health conditions, there is a global shortage of mental health providers. Artificial Intelligence (AI) methods have been proposed as a way to address this shortage, by supporting providers with less extensive training as they deliver care. To this end, we developed the AI-Assisted Provider Platform (A2P2), a text-based virtual therapy interface that includes a response suggestion feature, which supports providers in delivering protocolized therapies empathetically. We studied providers with and without e...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: William R Kearns Jessica Bertram Myra Divina Lauren Kemp Yinzhou Wang Alex Marin Trevor Cohen Weichao Yuwen Source Type: research
Assessing Telemedicine Competencies: Developing and Validating Learner Measures for Simulation-Based Telemedicine Training
AMIA Annu Symp Proc. 2024 Jan 11;2023:474-483. eCollection 2023.ABSTRACTIn 2021, the Association of American Medical Colleges published Telehealth Competencies Across the Learning Continuum, a roadmap for designing telemedicine curricula and evaluating learners. While this document advances educators' shared understanding of telemedicine's core content and performance expectations, it does not include turn-key-ready evaluation instruments. At the University of Oklahoma School of Community Medicine, we developed a year-long telemedicine curriculum for third-year medical and second-year physician assistant students. We used ...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Blake Lesselroth Helen Monkman Ryan Palmer Craig Kuziemsky Andrew Liew Kristin Foulks Deirdra Kelly Ainsly Wolfinbarger Frances Wen Liz Kollaja Shannon Ijams Juell Homco Source Type: research
Development and Usability Testing of an Exercise-Based Primary Care Fall Prevention Clinical Decision Support Tool
This study highlights the features and usability offall prevention CDS for helping primary care providers deliver patient-centeredfall prevention care.PMID:38222393 | PMC:PMC10785844 (Source: AMIA Annual Symposium Proceedings)
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Christian J Tejeda Pamela M Garabedian Hannah Rice Lipika Samal Nancy K Latham Patricia C Dykes Source Type: research
Extracting Thyroid Nodules Characteristics from Ultrasound Reports Using Transformer-based Natural Language Processing Methods
This study lays ground for assessing the documentation quality of thyroid ultrasound reports and examining outcomes of patients with thyroid nodules using electronic health records.PMID:38222394 | PMC:PMC10785862 (Source: AMIA Annual Symposium Proceedings)
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Aman Pathak Zehao Yu Daniel Paredes Elio Paul Monsour Andrea Ortiz Rocha Juan P Brito Naykky Singh Ospina Yonghui Wu Source Type: research
Use of Health Belief Model-based Deep Learning to Understand Public Health Beliefs in Breast Cancer Screening from Social Media before and during the COVID-19 Pandemic
AMIA Annu Symp Proc. 2024 Jan 11;2023:280-288. eCollection 2023.ABSTRACTBreast cancer is the second leading cause of cancer death for women in the United States. While breast cancer screening participation is the most effective method for early detection, screening rate has remained low. Given that understanding health perception is critical to understand health decisions, our study utilized the Health Belief Model-based deep learning method to predict and examine public health beliefs in breast cancer and its screening behavior. The results showed that the trends in public health perception are sensitive to political (i.e...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Michelle Bak Chieh-Li Chin Jessie Chin Source Type: research
Identification of Outcome-Oriented Progression Subtypes from Mild Cognitive Impairment to Alzheimer's Disease Using Electronic Health Records
In this study, we proposed an outcome-oriented model to identify progression pathways from mild cognitive impairment (MCI) to AD using electronic health records (EHRs) from the OneFlorida+ Clinical Research Consortium. To achieve this, we employed the long short-term memory (LSTM) network to extract relevant information from the sequential records of each patient. The hierarchical agglomerative clustering was then applied to the learned representation to group patients based on their progression subtypes. Our approach identified multiple progression pathways, each of which represented distinct patterns of disease progressi...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Jie Xu Rui Yin Yu Huang Hannah Gao Yonghui Wu Jingchuan Guo Glenn E Smith Steven T DeKosky Fei Wang Yi Guo Jiang Bian Source Type: research
Characterizing Autism Spectrum Disorder and Predicting Suicide Risk for Pediatric Psychiatric Emergency Services Encounters
This study sought to characterize and classify youth presenting to the psychiatric emergency department (ED) for a chief complaint of STB. The results of this study validated that a high number of patients with ASD present to the ED with STB. There were important differences in clinical characteristics to those with ASD versus those without. Clinical features that showed important impact in predicting high suicide risk in the ASD cases include elements of the mental status exam such as affect, trauma symptoms, abuse history, and auditory hallucinations. Focused attention is needed on these unique differences in ASD cases s...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Katherine A Brown Kathleen R Donise Mary Kathryn Cancilliere Dilum P Aluthge Elizabeth S Chen Source Type: research
