An Automated Strategy to Calculate Medication Regimen Complexity
AMIA Annu Symp Proc. 2024 Jan 11;2023:1077-1086. eCollection 2023.ABSTRACTUnderstanding medication regimen complexity is important to understand what patients may benefit from pharmacist interventions. Medication Regimen Complexity Index (MRCI), a 65-item tool to quantify the complexity by incorporating the count, dosage form, frequency, and additional administration instructions of prescription medicines, provides a more nuanced way of assessing complexity. The goal of this study was to construct and validate a computational strategy to automate the calculation of MRCI. The performance of our strategy was evaluated by com...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yuzhi Lu Ariel R Green Rosalphie Quiles Casey Overby Taylor Source Type: research
A Computable Phenotype for the Identification of Sexual and Gender Minorities in Electronic Health Records
AMIA Annu Symp Proc. 2024 Jan 11;2023:1057-1066. eCollection 2023.ABSTRACTSexual gender minorities, including lesbian, gay, and bisexual (LGB) individuals face unique challenges due to discrimination, stigma, and marginalization, which negatively impact their well-being. Electronic health record (EHR) systems present an opportunity for LGB research, but accurately identifying LGB individuals in EHRs is challenging. Our study developed and validated a rule-based computable phenotype (CP) to identify LGB individuals and their subgroups using both structured data and unstructured clinical narratives from a large integrated he...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yongqiu Li Xing He Christopher Wheldon Yonghui Wu Mattia Prosperi Elizabeth A Shenkman Michael S Jaffee Jingchuan Guo Fei Wang Yi Guo Jiang Bian Source Type: research
Local Contrastive Learning for Medical Image Recognition
AMIA Annu Symp Proc. 2024 Jan 11;2023:1236-1245. eCollection 2023.ABSTRACTThe proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model pred...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Syed A Rizvi Ruixiang Tang Xiaoqian Jiang Xiaotian Ma Xia Hu Source Type: research
The SMART Text2FHIR Pipeline
Conclusion: Future work should include mapping more categories of NLP-extracted information into FHIR resources and mappings from additional open-source NLP tools.PMID:38222416 | PMC:PMC10785871 (Source: AMIA Annual Symposium Proceedings)
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Timothy A Miller Andrew J McMurry James Jones Daniel Gottlieb Kenneth D Mandl Source Type: research
Text Classification of Cancer Clinical Trial Eligibility Criteria
In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained clinical trial BERT model. Our results demonstrate the feasibility of automatically classifying common exclusion criteria. Additionally, we demonstrate the value of a pre-trained language model specifically for clinical trials, which ...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yumeng Yang Soumya Jayaraj Ethan Ludmir Kirk Roberts Source Type: research
MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment
AMIA Annu Symp Proc. 2024 Jan 11;2023:641-652. eCollection 2023.ABSTRACTMental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history. To address this issue, we propose a personalized mental health tracking and mood prediction system that utilizes patient physiological data collected through personal health devices. Our system leverages a decentralized learning mechanism that combines transfer and federated machine learning concepts using smart contracts, allowing data to remain on users' devices and enabli...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Manan Shukla Oshani Seneviratne Source Type: research
Fatigue, Pain, and Medication: Mining Online Posts Regarding Rheumatoid Arthritis From Reddit
This study demonstrates the potential of using text-mining techniques on social media data to learn the treatment experiments of RA patients.PMID:38222419 | PMC:PMC10785940 (Source: AMIA Annual Symposium Proceedings)
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yi Xin Congning Ni Qingyuan Song Zhijun Yin Source Type: research
Analyzing Patient-Provided Responses to Improve Collection of Health Equity Data Elements
AMIA Annu Symp Proc. 2024 Jan 11;2023:569-578. eCollection 2023.ABSTRACTSelf-report is purported to be the gold standard for collecting demographic information. Many entry forms include a free-text "write-in" option in addition to structured responses. Balancing the flexibility of free-text with the value of collecting data in a structured format is a challenge if the data are to be useful for measuring and mitigating health disparities. While much work has been done to improve collection of race and ethnicity information, how to best collect data related to sexual and gender minority status and military veteran status has...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Jennifer Prey Dawson Heather Finn Aliasgar Z Chittalia David K Vawdrey Source Type: research
Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load
AMIA Annu Symp Proc. 2024 Jan 11;2023:389-396. eCollection 2023.ABSTRACTThe effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement. We conduct a study to explore the impact of gamification on self-reporting. Our approach involves the creation of a system to assess cognitive load (CL) through the analysis of photoplethysmography (PPG) signals. The data from 11 participants is utilized to train a machine learning model to detect CL. Subsequently, we create two versions of surveys: a gamified and a trad...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Michal K Grzeszczyk Paulina Adamczyk Sylwia Marek Ryszard Pr ęcikowski Maciej Ku ś M Patrycja Lelujko Rosmary Blanco Tomasz Trzci ński Arkadiusz Sitek Maciej Malawski Aneta Lisowska Source Type: research
Auditing Learned Associations in Deep Learning Approaches to Extract Race and Ethnicity from Clinical Text
We present an approach to audit the learned associations of models trained to identify RE information in clinical text by measuring the concordance between model-derived salient features and manually identified RE-related spans of text. We show that while models perform well on the surface, there exist concerning learned associations and potential for future harms from RE-identification models if left unaddressed.PMID:38222422 | PMC:PMC10785932 (Source: AMIA Annual Symposium Proceedings)
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Oliver J Bear Don't Walk Iv Adrienne Pichon Harry Reyes Nieva Tony Sun Jaan Altosaar Karthik Natarajan Adler Perotte Peter Tarczy-Hornoch Dina Demner-Fushman No émie Elhadad Source Type: research
Experiences and Perceptions of Distinct Telehealth Delivery Models for Remote Patient Monitoring among Older Adults in the Community
AMIA Annu Symp Proc. 2024 Jan 11;2023:794-803. eCollection 2023.ABSTRACTThree major telehealth delivery models-home-based, community-based, and telephone-based-have been adopted to enable remote patient monitoring of older adults to improve patient experience and reduce healthcare costs. Even though prior work has evaluated each of these delivery models, we know less about the perceptions and user experiences across these telehealth delivery models for older adults. In the present work, we addressed this research gap by interviewing 16 older adults who had experience using all these telehealth delivery models. We found tha...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Zhan Zhang Jina Huh-Yoo Karen Joy Monica Angeles David Sachs John Migliaccio Melody K Schiaffino Source Type: research
Optimizing the Synergistic Potential of Pseudo-Labels from Radiology Notes and Annotated Ground Truth in Identifying Pulmonary Opacities on Chest Radiographs for Early Detection of Acute Respiratory Distress Syndrome
AMIA Annu Symp Proc. 2024 Jan 11;2023:270-279. eCollection 2023.ABSTRACTAcute Respiratory Distress Syndrome (ARDS) is a life-threatening lung injury, hallmarks of which are bilateral radiographic opacities. Studies have shown that early recognition of ARDS could reduce severity and lethal clinical sequela. A Convolutional Neural Network (CNN) model that can identify bilateral pulmonary opacities on chest x-ray (CXR) images can aid early ARDS recognition. Obtaining large datasets with ground truth labels to train CNNs is challenging, as medical image annotation requires clinical expertise and meticulous consideration. In th...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Mehak Arora Carolyn M Davis Angana Mondal Niraj R Gowda Dennis Gene Foster Rishikesan Kamaleswaran Source Type: research
Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic
AMIA Annu Symp Proc. 2024 Jan 11;2023:942-950. eCollection 2023.ABSTRACTElectronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system. We characterize patterns of missing data in laboratory measurements collected at the University of Pennsylvania Hospital System from long-term COV...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Emily Getzen Amelia Lm Tan Gabriel Brat Gilbert S Omenn Zachary Strasser Consortium for Clinical Characterization of COVID-19 by EHR (4CE) (Collaborative Group/Consortium) Qi Long John H Holmes Danielle Mowery Source Type: research
Contextual Variation of Clinical Notes induced by EHR Migration
In this study, we evaluate the contextual variation of clinical notes by measuring the semantic and syntactic similarity of the notes of two sets of physicians comprising four medical specialties across EHR migrations at two Mayo Clinic sites. We find significant semantic and syntactic variation imposed by the context of the EHR system and between medical specialties whereas only minor variation is caused by variation of spatial context across sites. Our findings suggest that clinical language models need to account for process differences at the specialty sublanguage level to be generalizable.PMID:38222426 | PMC:PMC107858...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Kurt Miller Sungrim Moon Sunyang Fu Hongfang Liu Source Type: research
Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
AMIA Annu Symp Proc. 2024 Jan 11;2023:884-893. eCollection 2023.ABSTRACTClinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models m...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Chia-Yuan Chang Jiayi Yuan Sirui Ding Qiaoyu Tan Kai Zhang Xiaoqian Jiang Xia Hu Na Zou Source Type: research
