Temporal Rule Mining for Enhanced Risk Pattern Extraction: A Case Study with Acute Kidney Injury

2025 Informatics Summit On Demand

Presentation Time: 12:45 PM - 01:00 PM

Abstract Keywords: Data-Driven Research and Discovery, Machine Learning, Generative AI, and Predictive Modeling, Secondary Use of EHR Data
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Novel Methods for Variant Detection and Interpretation from Omics Data

Association rule mining is a widely used data mining technique for extracting knowledge from large datasets. Its application in healthcare involves uncovering meaningful patterns within electronic health records (EHR) to inform clinical decision-making and treatment strategies. However, most association rule mining studies overlook temporal information, potentially missing valuable patterns associated with specific time periods or events. In recent years, several methods have been developed to mine temporal association rules, offering improved predictive and descriptive capabilities. We propose a multi-step rule mining framework that utilize temporal pattern mining algorithm to extract actionable and temporal risk patterns for acute kidney injury (AKI) using EHR data. Our algorithm discovered around 26K rules, with low support and high confidence, centered at 40 actionable. The derived rules have a median support of 0.057 and confidence of 0.49. We highlight selected rules, their potential etiology, and provide a network view of more specific actionable insights.
Speaker(s):
Ho Yin Chan, PhD
University of Florida

Author(s):
Alan Yu, M.B., B.Chir. - The University of Kansas Medical Center; Mei Liu, PhD - University of Florida;

Studying Veteran food insecurity longitudinally using electronic health record data and natural language processing

2025 Informatics Summit On Demand

Presentation Time: 01:00 PM - 01:15 PM

Abstract Keywords: Natural Language Processing, Social Determinants of Health, Measuring Outcomes, Real-World Evidence and Policy Making, Secondary Use of EHR Data, Data-Driven Research and Discovery
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice

Food insecurity is an important social risk factor that is directly linked to patient health and well-being. The Department of Veterans Affairs (VA) aims to identify and resolve food insecurity through social and clinical interventions. However, evaluating the impact of such interventions is made challenging by the lack of follow-up data on Veteran food insecurity status. One potential solution leverages documentation of food insecurity in electronic health records (EHRs). In this paper, we developed and validated a natural language processing system to identify food insecurity status from clinical notes and applied it to study longitudinal trajectories of food insecurity among a large cohort of food insecure Veterans. Our methods provide insight into the timing and persistence of Veteran food insecurity and have the potential to be used for designing policy and evaluating interventions that target food insecurity.
Speaker(s):
Alec Chapman, MS
University of Utah

Author(s):
Alec Chapman, MS - University of Utah; Talia Panadero, MPH - Greater Los Angeles Veterans Affairs Healthcare System, Los Angeles, CA; Rachel Dalrymple, MBA - University of Utah; Alicia Cohen, MD - Brown University; Nipa Kamdar, PhD - Baylor University; Farhana Pethani, BDent - University of Sydney; Andrea Kalvesmaki, PhD - U.S. Department of Veterans Affairs; Richard Nelson; Jorie Butler, PhD - University of Utah;

Large Language Models in Biomedical Named Entity Recognition

2025 Informatics Summit On Demand

Presentation Time: 01:15 PM - 01:30 PM

Abstract Keywords: Machine Learning, Generative AI, and Predictive Modeling, Natural Language Processing, Data Mining and Knowledge Discovery
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Harnessing the Power of Large Language Models in Health Data Science

Large language models (LLMs), like GPT-4, have revolutionized natural language processing (NLP), demonstrating exceptional performance across various tasks. However, their effectiveness in biomedical named entity recognition (BioNER) remains limited due to the need for domain-specific knowledge. This study focuses on fine-tuning general-domain LLMs, specifically Llama-2 models, for BioNER tasks. We convert five BioNER datasets from the BLURB benchmark into an instruction-following format to optimize fine-tuning. Our approach incorporates zero-shot prompting, Chain-of-Thought (CoT) reasoning, and a perplexity-based evaluation method. We evaluate the fine-tuned Llama-2 models on the AnatEM, BioNLP11EPI, and BioNLP13GE datasets, and our method consistently outperforms baseline models such as UniNER-7B, InstructUIE-11B, and BioLinkBERT. Furthermore, larger models like Llama2-13B demonstrate superior performance compared to smaller ones, highlighting the significance of model parameters. This study underscores the potential of instruction-tuned LLMs for BioNER tasks and opens avenues for their application in other biomedical NLP tasks.
Speaker(s):
Cong Sun, Ph.D.
Weill Cornell Medicine

Author(s):

Identifying Necrotizing Enterocolitis Diagnosis from Progress Notes Using Natural Language Processing and Classification Models

2025 Informatics Summit On Demand

Presentation Time: 01:30 PM - 01:45 PM

Abstract Keywords: Cohort Discovery, Natural Language Processing, Clinical and Research Data Collection, Curation, Preservation, or Sharing, EHR-based Phenotyping
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Translational Bioinformatics Using Multi-Modal Patient Data and AI

Necrotizing Enterocolitis (NEC) is a serious neonatal condition with high mortality and morbidity. This study utilized NLP to analyze progress notes, enhancing NEC patient identification accuracy and specificity. The method surpasses manual chart review and traditional cohort discovery approaches. Improving precision in patient classification significantly reduces the reliance on labor-intensive reviews, offering a scalable solution for NEC identification.
Speaker(s):
Woo Yeon Park, MS
Johns Hopkins University

Author(s):
Khyzer Aziz, M.D. - Johns Hopkins University; Nicholas Dobbins, PhD, MLIS - Johns Hopkins University;

A Multipronged Approach: Harnessing LLMs and NLP on Structured and Unstructured Data to Enhance Traditional Chart Review

2025 Informatics Summit On Demand

Presentation Time: 01:45 PM - 02:00 PM

Abstract Keywords: Machine Learning, Generative AI, and Predictive Modeling, Knowledge Representation, Management, or Engineering, EHR-based Phenotyping
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Harnessing the Power of Large Language Models in Health Data Science

Accurate and efficient chart review is crucial for extracting clinically relevant information. It is performed for several purposes from validation studies to care assessments. The manual review process is time consuming, costly, and prone to human error. Using AI by leveraging LLMs combined with practical NLP, we can enhance the chart review process in a meaningful way. At MGB, we developed a flexible “reasoning chain pipeline” using LLMs and NLP to improve specificity and sensitivity.
Speaker(s):
Nich Wattanasin, MS
Mass General Brigham

Author(s):
Martin Rees, BS - Mass General Brigham; Victor Castro, MS - Mass General Brigham; Heekyong Park, PhD - Mass General Brigham; Kavishwar Wagholikar, MD, PhD - Harvard Medical School /MGH; Taowei Wang, PhD - Mass General Brigham; Bhaswati Ghosh, MS - Mass General Brigham; Allan Harris, BS - Mass General Brigham; Vivian Gainer, MS - Mass General Brigham; Shawn Murphy, MD, Ph.D. - Massachusetts General Hospital;

A Comparison of Rule-based, Machine Learning, and Large Language Model Methods for Extracting Adverse Events from Clinical Notes

2025 Informatics Summit On Demand

Presentation Time: 02:00 PM - 02:15 PM

Abstract Keywords: Natural Language Processing, Secondary Use of EHR Data, Clinical Trials Innovations, Machine Learning, Generative AI, and Predictive Modeling, Informatics Research/Biomedical Informatics Research Methods
Primary Track: Clinical Research Informatics
Programmatic Theme: Harnessing the Power of Large Language Models in Health Data Science

Adverse event detection is a necessary component of clinical trial data collection and currently requires massive expenditure of effort in the form of manual chart review. NLP techniques can automate this effort, but their performance is uncertain within the context of clinical trial replicability. We developed a rule-based AE detection approach and evaluated it alongside an LLM and a previously piloted best-of-breed technique in notes for patients with mantle cell lymphoma.
Speaker(s):
Aashri Aggarwal, BA
Weill Cornell Medicine

Author(s):
Jordan Goldstein, MD - Stanford University; Aashri Aggarwal, BA - Eastern Virginia Medical School; Evan Sholle, MS - Weill Cornell Medical College; Itzel Nino, Bachelor of Science; Thomas Campion, PhD - Weill Cornell Medicine;