AMIA's Annual Symposium is the premier learning and networking conference attended by more than 2,500 health informaticians from across the world. Now, you can access full presentations and slides from the live event at your convenience while earning CME/CNE online.
AMIA 2024 Annual Symposium On Demand is designed to provide you with the very latest health informatics content with maximum value and convenience. Revisit one or all top 20 sessions from the conference, featuring leading voices from across the informatics field. Choose the format that fits your preferred learning style. Take up to two years to claim your education credits. Recorded at AMIA’s Annual Symposium, held November 9-13, 2024, in San Francisco, CA.
Choose Your Format
Presenter, Slides, and Audio
Members: $190 $152*
Nonmembers: $270 $216*
*Includes 20% discount through March 31. Log in to see your discount.
Slides and Audio
Members: $150 $120*
Nonmembers: $210 $168*
*Includes 20% discount through March 31. Log in to see your discount.
Deep Learning-based Time-to-event Analysis of Depression and Asthma using the All of Us Research Program
While there is a growing recognition of the association between depression and asthma, few studies have leveraged deep learning-based (DL-based) models in analysis and prediction on a large sample size retrospective cohort study. We analyzed the association between depression and asthma among 239,161 participants of the All of Us Research Program through DL-based, logistic regression, and Cox Proportional Hazards (CoxPH) models. We used SHAP values to help interpret DL-based models and c-index to help model performance. Results suggest a significant odds ratio for depression in asthma. The c-indices for the CoxPH, DeepSurv, and DeepHit were 0.619, 0.625, and 0.596, respectively. SHAP indicated a different set of important variables when compared with CoxPH. In conclusion, we provide strong evidence of a positive relationship between depression and asthma. DL-based models did not outperform the CoxPH model on the c-index. Sex and income may play important roles in depression in asthma patients.
Learning Outcomes
Describe the Cox Proportional Hazard model, Deep-learning based time-to-event models. Discuss the utility of SHAP values to interpret deep learning models. Design a time-to-event analysis study in the All of Us Research Program. Understand the positive association between depression and asthma.
Speakers
- Xueting Wang, Master of Public Health, Yale University
Identification and Validation of Common Respiratory Infections in All of Us
This study integrates billing codes, multiple methods of microbiological testing, and medication usage from the All of Us Research Program to identify pathogen-specific seasonal respiratory infections. We validate this phenotyping approach against CDC epidemiological trends to demonstrate accurate seasonality of detection and capture the impact of reduced transmission during the COVID-19 pandemic.
Learning Outcomes
- Compose an EHR computable phenotype to identify seasonal respiratory infections and validate these cohorts using comparison with the CDC national surveillance.
Speakers
- Bennett Waxse, MD, PhD, NIAID/CNH
Pregnancy Outcomes in Hidradenitis Suppurativa Patients
Hidradenitis suppurativa is an autoinflammatory condition resulting in painful cysts, nodules, and sinus tracts in areas of high skin on skin contact. The microenvironment of affected tissues is high in pro-inflammatory cytokines and T-helper 17 cells. Other auto-inflammatory diseases, like psoriasis, have an enhanced risk of systemic inflammation and an elevated risk of spontaneous abortion. A cohort of pregnant patients from Cerner Health Facts® was identified using a Python adaptation of a validated pregnancy identification and classification algorithm. The HS population was identified among the pregnant population and was shown to be statistically significantly associated with outcome type by Chi square. A multinomial logistic regression also indicated a statistically significant increase in the odds of a pregnant patient having a spontaneous abortion over a live birth when controlling for thyroid disease, polycystic ovarian syndrome, antiphospholipid syndrome, other inflammatory diseases, and advanced maternal age.
Learning Outcomes
- Understand the interpretation of multinomial logistic regression.
Speakers
- David Walsh, BS, UMKC
EHR Phenotyping Methods for Measuring Treatment Adherence Among People Living With HIV in All of Us: Towards Disparities and Inequalities in HIV Care Continuum
HIV treatment adherence is among the most important determinants of HIV outcomes. However, only 50% of people living with HIV in the US were retained in care. Measuring HIV treatment adherence in the clinical settings is feasible but when it comes to the growing number of multi-site Electronic Health Records (EHR), there has been a dearth of research for adequate informatics methods to handle EHR. We sought to address this gap by developing a cluster of metrics for measuring HIV treatment adherence via EHR phenotyping. Our methods were developed and tested in the All of Us research program. We also performed preliminary analyses to explore disparities in HIV treatment adherence and demographic factors contributing to poor adherence. This study paves the way for systematic data mining and analyses for the HIV care continuum, disparities, and inequality on All of Us and other EHR normalized with the OMOP Common Data Model.
Learning Outcomes
- Understand EHR phenotyping in the All of Us research program. Develop HIV treatment adherence measurements using EHR data. Explore health disparities and inequalities in the HIV care continuum using EHR data.
Speakers
- Tianchu Lyu, PhD, University of South Carolina
Continuing Education Credit
Physicians
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this online enduring material for 1.25 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.
Nurses
The American Medical Informatics Association is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center’s Commission on Accreditation.
- Approved Contact Hours: 1.25 participant maximum
- Nurse planner for this activity: Jenna Thate, PhD, RN, CNE
- Jenna Thate discloses that she has no financial relationships with ACCME/ANCC-defined ineligible companies.
Upon completion of each video and corresponding evaluation portion of this activity, all learners will be able to download the appropriate credit certificate, or a certificate of participation.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.
ACHIPsTM
AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.
ACHIPsTM may use CME/CNE certificates or the ACHIPsTM Recertification Log to report 2024 Symposium sessions attended for ACHIPsTM Recertification.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.