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: $150 $120*
Nonmembers: $210 $168*
*Includes 20% discount through March 31. Log in to see your discount.
Slides and Audio
Members: $120 $96*
Nonmembers: $170 $136*
*Includes 20% discount through March 31. Log in to see your discount.
Large-scale Text Mining of Suicide Attempt improves Identification of Distinct Suicidal Events in Electronic Health Records
In this study, we explore a natural language processing (NLP) algorithm’s capacity to identify proximal but distinct suicide attempt (SA) events compared to diagnostic code-based approaches. This study used an NLP algorithm with high precision in identifying SA events, which processes clinical notes for suicide-related text expressions and generates SA outcome relevance scores on mentioned dates. We chart reviewed all SA visit pairs less than 15 days apart. Despite sample size limitations, our NLP method surpassed the code-based model's performance (0.85 [95% CI: 0.74 - 0.92] vs. 0.78 [95% CI: 0.56 - 0.92], p = 0.71). More importantly, NLP detected three times more SA visit pairs <15 days compared to the code-based approach (71 vs. 23), with only 3 overlaps. This study demonstrates NLP's efficacy in identifying distinct SA visit pairs. Given minimal overlap, we suggest leveraging both clinical notes and diagnostic codes for a comprehensive SA event detection.
Learning Outcomes
- Understand the challenges in identifying real suicide attempt events in EHR data and how clinical notes and natural language processing can address these challenges.
Speaker
- Hyunjoon Lee, MS, Vanderbilt University Department of Biomedical Informatics
Using Large Language Models for sentiment analysis of health-related social media data: empirical evaluation and practical tips
Health-related social media data generated by patients and the public provide valuable insights into patient experiences and opinions toward health issues such as vaccination and medical treatments. Using Natural Language Processing (NLP) methods to analyze such data, however, often requires high-quality annotations that are difficult to obtain. The recent emergence of Large Language Models (LLMs) such as the Generative Pre-trained Transformers (GPTs) has shown promising performance on a variety of NLP tasks in the health domain with little to no annotated data. However, their potential in analyzing health-related social media data remains underexplored. In this paper, we report empirical evaluations of LLMs (GPT-3.5-Turbo, FLAN-T5, and BERT-based models) on a common NLP task of health-related social media data: sentiment analysis for identifying opinions toward health issues. We explored how different prompting and fine-tuning strategies affect the performance of LLMs on social media datasets across diverse health topics, including Healthcare Reform, vaccination, mask wearing, and healthcare service quality. We found that LLMs outperformed VADER, a widely used off-the-shelf sentiment analysis tool, but are far from being able to produce accurate sentiment labels. However, their performance can be improved by data-specific prompts with information about the context, task, and targets. The highest performing LLMs are BERT-based models that were fine-tuned on aggregated data. We provided practical tips for researchers to use LLMs on health-related social media for optimal outcomes. We also discuss future work needed to continue to improve the performance of LLMs for analyzing health-related social media data with minimal annotations.
Learning Outcome
- Use emerging LLM to analyze the sentiments of health social media data.
- Improve the performance of LLM on health social media data.
Speaker
- Lu He, PhD, University of Wisconsin-Milwaukee
The 20% among Suicide Deaths died Without Warning Signs: Trend Analysis based on Suicide Decedents in the US from 2003-2020
Understanding who is less likely to reveal suicidal intentions is crucial for developing effective prevention strategies, as suicide rates increase in the US, notably among marginalized groups. Existing studies, limited by small, homogeneous samples, fail to thoroughly analyze various demographics and contexts. This study investigates trends in disclosed and non-disclosed suicide deaths in the US from 2003 to 2020, considering age, gender, race, ethnicity, methods of suicide, intended recipients of disclosures, and drug/substance categories. It utilizes cross-sectional data from 500,072 suicide decedents across 49 states, Puerto Rico, and the District of Columbia, sourced from the National Violent Death Reporting System's Restricted Access Database, with statistical analyses conducted between October 2023 and January 2024. The main outcomes measured were disclosures of suicidal intent within one month prior to death and the presence of suicide notes. Results show a consistent 80:20 ratio of non-disclosed to disclosed suicides. Specific groups, including older adults, both genders, certain racial groups, and those who died by specific methods or substances, displayed significantly lower odds of disclosing suicidal intentions. Notably, Black decedents disclosed at markedly lower rates than White decedents, with disparities more pronounced among females of these racial groups. The study emphasizes the need for targeted suicide prevention strategies, especially for racial minorities, older adults, males, and individuals utilizing certain suicide methods or substances. It highlights the necessity for increased public health efforts to normalize mental distress and enhance access to mental health services. 2
Speaker
- Yunyu Xiao, PhD, Weill Cornell Medicine, Population Health Sciences
Assessing demographic differences in psychological pain, hopelessness, connectedness, and capacity for suicide based on terminology-driven natural language processing of VHA clinical progress notes
Psychological pain, hopelessness, connectedness, and capacity for suicide are among the most important drivers of suicidal behavior. Scores based on terminology-driven natural language processing (NLP) of Veterans Health Administration (VHA) clinical progress notes showed meaningful change in these four factors before patients attempted or died by suicide. It is as yet unknown if these changes depend on sex, age group, race/ethnicity, and being involved in the criminal legal system (e.g., in court or incarcerated). We will present results of a repeated measures analysis of psychological pain, hopelessness, connectedness, and capacity for suicide with a between-subjects component for the listed demographic subgroups. Clinical progress notes entered between 2014 and September 2022 during eight weeks before patients attempted or died by suicide (n=43,581, female 7089, male 36,492) were pulled from the VHA corporate data warehouse. These notes were tagged and labelled for the four factors using a vocabulary of terms available from BioPortal. Using these labels, patient mean scores for the four factors were computed across the last four weeks (1-4) and across the four weeks (5-8) before that. Repeated measures analysis of variance was used to test the effect of Time, patient subgroup, and the Time by subgroup interaction. Results support the hypothesis that our terminology-driven NLP pipeline to determine psychological pain, hopelessness, connectedness, and capacity for suicide captures meaningful change in demographic subgroups prior to a suicidal event. Scores for these four factors may support clinical decision-making regarding suicide prevention.
Learning Outcome
- Recognize alarming patterns in psychological pain, hopelessness, connectedness, and capacity for suicide that may signal an imminent suicide attempt.
- Understand that the level of psychological pain, hopelessness, connectedness, and capacity for suicide before a suicide attempt differs by demographic subgroup.
Speaker
- Esther Meerwijk, Phd, MSN, Weill Cornell Medicine, Population Health Sciences
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 1.0 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: 1.0 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.