PROGRAM

Stay tuned for the Full Program (EXCEL)

For scientific paper sessions, regular presentations have 15 minutes (12 minutes presentation and 3 minutes QA).

For poster and demo session, please follow the Presenter Guidelines.

Monday, June 26

  BRC 280 BRC 282 BRC 284 BRC 286 Auditorium
7:30 AM
8:30 AM
Registration @Prefunction Space
Breakfast @Exhibition Space
 
 
10:00 AM
Workshop 1
Health Natural Language Processing (HealthNLP 2023)
Workshop 2
Health Informatics Education (HI-EDU 2023)
Workshop 3
Ethics and Bias of Artificial Intelligence in Clinical Applications (EBAIC 2023)
Workshop 4
Data Science and AI Applications in Cancer Research
10:20 AM
Coffee Break @Event/Exhibition Space
 
12:00 PM
Workshop 1
Health Natural Language Processing (HealthNLP 2023)
Workshop 2
Health Informatics Education (HI-EDU 2023)
Workshop 3
Ethics and Bias of Artificial Intelligence in Clinical Applications (EBAIC 2023)
Workshop 4
Data Science and AI Applications in Cancer Research
1:00 PM
Lunch @Event/Exhibition Space
 
2:30 PM
Workshop 1
Health Natural Language Processing (HealthNLP 2023)
Workshop 2
Health Informatics Education (HI-EDU 2023)
Workshop 3
Ethics and Bias of Artificial Intelligence in Clinical Applications (EBAIC 2023)
Workshop 5
Network and Pathway Analysis In Health Informatics (NPAHI 2023) & AI for Pharmaceutical Discovery and Development (AIPHA 2023)
Doctoral Consortium
2:50 PM
Coffee Break @Event/Exhibition Space
 
4:30 PM
Workshop 1
Health Natural Language Processing (HealthNLP 2023)
Workshop 2
Health Informatics Education (HI-EDU 2023)
Workshop 3
Ethics and Bias of Artificial Intelligence in Clinical Applications (EBAIC 2023)
Workshop 5
AI for Pharmaceutical Discovery and Development (AIPHA 2023)
Doctoral Consortium

Tuesday, June 27

  Auditorium BRC 282 BRC 284 BRC 280 BRC 286
7:30 AM
8:30 AM
Registration @Prefunction Space
Breakfast @Exhibition Space
 
 
10:00 AM
Doctoral Consortium
Tutorial 1
Modifying the Design Sprint Methodology: Leveraging Online Tools for Collaborating and Prototyping in Research Settings
Tutorial 2
Training Recurrent Neural Network-Based Model to Predict COVID-19 Patient Risk for PASC using Pytorch_EHR
Tutorial 3
Enabling AI-Augmented Clinical Workflows by Accessing Patient Data in Real-Time with FHIR
Tutorial 4
Trustworthy Computing in Biomedical Challenges
10:20 AM
Coffee Break @Event/Exhibition Space
 
12:00 PM
Doctoral Consortium
Tutorial 1
Modifying the Design Sprint Methodology: Leveraging Online Tools for Collaborating and Prototyping in Research Settings
Tutorial 2
Training Recurrent Neural Network-Based Model to Predict COVID-19 Patient Risk for PASC using Pytorch_EHR
Tutorial 3
Enabling AI-Augmented Clinical Workflows by Accessing Patient Data in Real-Time with FHIR
Tutorial 4
Trustworthy Computing in Biomedical Challenges
1:00 PM
Lunch @Event/Exhibition Space
 
2:15 PM
Welcome Session (1:00 - 1:15 PM)
Keynote 1: Challenges and Opportunities for Improving Patient Safety Through Data Science and Informatics
Patricia C. Dykes
@Auditorium
2:30 PM
Coffee Break @Event/Exhibition Space
 
4:00 PM
ChatGPT Panel
@2:30 PM - 4:30PM
Analytics 1
Analytics
Systems 1
Design and Development
Human Factors 1
Human-centered Design
 
5:30 PM
Poster Session & Reception (sponsored by SBMI)

@Event/Exhibition Space

Wednesday, June 28

       
7:30 AM
8:45 AM
Registration @Prefunction Space
Breakfast @Event/Exhibition Space
Women in Healthcare Informatics Meet & Greet
@Event/Exhibition Space
9:00 AM
 
 
10:00 AM
Keynote 2: Explainable Deep Tabular Learning Models for Biomedical and Healthcare Applications
Dr. Aidong Zhang
@Auditorium
10:30 AM
Coffee Break @Event/Exhibition Space
 
12:00 PM
Analytics 2
Deep Learning
@BRC 282
Analytics 3
Representations
@BRC 284
Human Factors 2

@BRC 286
1:00 PM
Lunch @Event/Exhibition Space
 
2:30 PM
Analytics 4
COVID Related
@BRC 282
Analytics 5
NLP
@BRC 284
Analytics 6
Spatio-Temporal
@BRC 286
3:00 PM
Coffee Break @Event/Exhibition Space
 
4:30 PM
Analytics 7
Phenotyping, Fairness and Explainability
@BRC 282
Analytics 8
Deep Learning (2)
@BRC 284
Panel: Resuming Healthcare Informatics Research after CoViD-19: The Healthcare System Perspective
@Auditorium
Remote Sessions
@zoom
@BRC 286
 
5:30 PM
 
6:00 PM
Award Ceremony and Banquet Dinner (6:00 - 8:00 PM)
@Event/Exhibition Space

Thursday, June 29

       
7:30 AM
8:45 AM
Registration @Prefunction Space
Breakfast @Prefunction Space
 
9:45 AM
Keynote 3: Data and System Harmonization: Reflections and observations from 25 years of research in disparate fields
Dr. Ed Nykaza
@Auditorium
10:00 AM
Coffee Break @Prefunction Space
 
12:00 PM
Industry Session
@Auditorium
 
12:30 PM
Close Session
@Auditorium
 
Grab & Go Lunch @Prefunction Space (12:30 - 1:00 PM)

Program Details

Monday, June 26

Monday, June 26, 8:30AM - 16:30 PM @BRC 280

The 6th International Workshop on Health Natural Language Processing (HealthNLP 2023)
More detailed information is listed here: https://www.healthnlp.info/

Workshop Chairs:
Yifan Peng, Weill Cornell Medicine, US
Halil Kilicoglu, University of Illinois at Urbana-Champaign, US

  • Paper presentations (8:30 AM - 4:30 PM)
    Accepted Papers (20 minutes each, 15 minutes for presentation, 5 minutes for discussion)
    • Hetong Ma, Xiaowei Xu, Xuwen Wang, Zhen Guo and Jiao Li, (2023), "Comma: A collaborative medical text annotation platform"
    • Zeljko Kraljevic, James Teo, Richard Dobson, Anthony Shek, Joshua Au-Yeung, Ewart Sheldon, Mohammad Ali-Agil, Haris Shuaib, Bai Xi, Kawsar Noor and Anoop Shah, (2023), "Deploying transformers for redaction of text from electronic health records in real world healthcare"
    • Simon Meoni, (2023), "Annotate Clinical Data Using Large Language Model Predictions
    • Zenan Sun and Cui Tao, (2023), "Named Entity Recognition and Normalization for Alzheimer's Disease Eligibility Criteria"
    • Miho Shimizu, Mike Wong and Anagha Kulkarni, (2023), "Quantitative Measures of Online Health Information (QMOHI): Broadening the impact through improved usability, applicability, and effectiveness"
    • Xiaoyu Wang, Dipankar Gupta, Michael Killian and Zhe He, (2023), "Benchmarking Transformer-Based Models for Identifying Social Determinants of Health in Clinical Notes"
    • Shaina Raza and Syed Raza Bashir, (2023), "Leveraging Foundation Models for Clinical Text Analysis"
    • Muskan Garg and Sohn Sunghwan, (2023), "CARED: Caregiver’s Experience with Cognitive Decline in Reddit Posts"
    • Mengtian Guo, David Gotz and Yue Wang, (2023), "How Does Imperfect Automatic Indexing Affect Semantic Search Performance?"
    • Sayantani Basu, Roy Campbell and Karrie Karahalios, (2023), "Detection of Novel COVID-19 Variants with Zero-Shot Learning"
    • Parker Seegmiller, Joseph Gatto, Madhusudan Basak, Diane Cook, Hassan Ghasemzadeh, John Stankovic and Sarah Masud Preum, (2023), "The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints"
Monday, June 26, 8:30AM - 16:30 PM @BRC 282

The 2nd International Workshop on Health Informatics Education (HI-EDU 2023)
More detailed information is listed here: https://sites.pitt.edu/~lzhou1/HIEDU2023.html

Workshop Chairs:
Leming Zhou, PhD, Leming.Zhou@pitt.edu, University of Pittsburgh, US
Huanmei Wu, PhD, huanmei.wu@temple.edu, Temple University, US
Yanshan Wang, PhD, yanshan.wang@pitt.edu, University of Pittsburgh, US
Stephen Abah, FWACP, abah.steve@fuhso.edu.ng, Federal University of Health Sciences Otukpo, Nigeria

  • Keynote Speech (8:30 - 9:30 AM, 1 hour), Susan H. Fenton, PhD, RHIA, ACHIP, FAMIA, UTHealth Houston
  • Invited Talk (9:30 - 10 AM, 30 minutes): Amanda Stefan, CAHIIM, Health Informatics Program Accreditation
  • Coffee Break (10:00 - 10:30 AM)
  • Paper presentations (10:30 AM - 4:30 PM)
    Accepted Papers (15 minutes each, 12 minutes for presentation, 3 minutes for discussion)
    • Vincent Major, Claudia Plottel and Yindalon Aphinyanaphongs, (2023), "Ten Years of Health Informatics Education for Physicians"
    • Sahaja Ratna, Saptarshi Purkayastha and Cathy Fulton, (2023), "Improving Health Informatics Competencies Using an Open-source Educational EHR"
    • Rob Quick, Marcela Alfaro Cordoba, Stephan Diggs, Raphael Cobe, Louise Bezuidenhout, Hugh Shannahan and Bianca Peterson, (2023), "Foundational Data Science Training for Health Equity Researchers at Minority Serving Institutions: A SoRDS Event"
    • Duo Helen Wei, Keith Kiminsky, Patrick Hamill and Quynh Nguyen, (2023), "Involving Undergraduates into Health Informatics Research via Project-Based Learning Classes – A Case Study"
    • Toufeeq Ahmed, Aidan Hoyal, Katie Stinson, Jay Johnson, Zainab Latif, Legand Burge, Alexander Libib, Guodong Gao, Nawar Shara and Jamboor Vishwanatha, (2023), "AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data Science Training Platform to Increase Researcher Diversity and Advance Health Equity"
    • Hua Min, Hedyeh Mobahi and Janusz Wojtusiak, (2023), "Application of Synthetic Datasets across Courses in Health Informatics Education"
  • Lunch break (12:00 PM - 1:00 PM)
    • Sripriya Rajamani, Sarah Solarz, Amber Koskey, Taylor Dawson, Ann Kayser, Hannah Woods, Chris Brueske and Aasa Schmit, (2023), "Informatics Workforce to Support the Data Modernization Initiative: Assessment and Capacity Building at a State Public Health Agency"
    • Bari Dzomba, Kesa Bond and Cathy Flite, (2023), "Blowing Chunks with ChatGPT"
    • Jay Patel, Omotese Oaikhena, Than Phan, Hoa Vo, Javad Alizadeh and Huanmei Wu, (2023), "The Landscape of Health Informatics Education in Low- or Middle-Income Countries
  • Digital Poster presentation, 10 minutes each
    • Sripriya Rajamani, Yasmin Odowa and Rebecca Wurtz, (2023), "Foundational Course in Public Health Informatics to meet Training Needs and Build Workforce Capacity"
    • Caroline Spice, Anjulie Ganti and Houda Benlhabib, (2023), "So you want to talk about race AND genetics?"
Monday, June 26, 8:30AM - 16:30 PM @BRC 284

The 1st International Workshop on Ethics and Bias of Artificial Intelligence in Clinical Applications (EBAIC 2023)
More detailed information is listed here: https://pittnail.github.io/EBAIC/

Workshop Chairs:
Yanshan Wang, PhD, University of Pittsburgh, US
Hongfang Liu, PhD, Mayo Clinic, Rochester, MN, USA
Ahmad P. Tafti, PhD, University of Pittsburgh, Pittsburgh, PA, USA
Kirk Roberts, PhD, The University of Texas Health Science Center at Houston, USA

  • Paper presentations (8:30 AM - 4:30 PM)
    Accepted Papers (20 minutes each, 15 minutes for presentation, 5 minutes for discussion)
    • Leilei Su, Zezheng Wang, Yifan Peng and Cong Sun, (2023), "Identiffcation of offensive language in social media using prompt learning"
    • Seha Ay, Michael Cardei, Anne-Marie Meyer, Wei Zhang and Umit Topaloglu, (2023), "Improving Equity in Deep Learning Medical Applications with the Gerchberg-Saxton Algorithm"
    • Shanshan Song and Casey Overby Taylor, (2023), "Data Representativeness in Cardiovascular Disease Studies that use Consumer Wearables
    • Yash Travadi, Le Peng, Ying Cui and Ju Sun, (2023), "Direct Metric Optimization for Imbalanced Classification"
    • Direct Metric Optimization for Imbalanced Classification, (2023), "Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare"
    • Shaina Raza. , (2023), "Connecting Fairness in Machine Learning with Public Health Equity"
    • Mary Lucas, Chia-Hsuan Chang and Christopher Yang, (2023), "Resampling for Mitigating Bias in Predictive Model for Substance Use Disorder Treatment Completion"
    • Paul Heider. Algorithmic Bias in De-Identification Tools, (2023), "AI Fairness in Hip Bony Anatomy Segmentation: Analyzing and Mitigating Gender and Racial Bias in Plain Radiography Analysis"
    • Christina Letter, Puneet Gupta, Annie Kim, Guang-Ting Cong, Hongfang Liu and Ahmad P. Tafti, (2023), "Gender-Specific Machine Learning Models to Predict Unplanned Return to Operating Room Following Primary Total Shoulder Arthroplasty"
    • Nickolas Littlefield, Johannes F. Plate, Kurt R. Weiss, Ines Lohse, Avani Chhabra, Ismaeel A. Siddiqui, Zoe Menezes, George Mastorakos, Soheyla Amirian, Hamidreza Moradi and Ahmad P. Tafti, (2023), "Algorithmic Bias in De-Identification Tools"
    • Shaina Raza, (2023), "Auditing ICU Readmission Rates in an Clinical Database: An Analysis of Risk Factors and Clinical Outcomes"
Monday, June 26, 8:30AM - 12:00 PM @BRC 286

Data Science and AI Applications in Cancer Research
More detailed information is listed here: PDF file

Workshop Chair: W. Jim Zheng

Time Presentation Title
8:00 - 8:15 Introduction
W.Jim Zheng UTHealth Houston
8:15 - 9:00 Keynote Speech
Jill Barnholtz-Sloan, NCI 		Connecting Data and Novel Tools for Impact on Cancer – Updates from the NCI
9:00 - 9:20
Hua Xu, Yale 		Natural language processing to facilitate cancer research using electronic health records
9:20 - 9:40
Laila Bekhet, UTHealth Houston 		AI models trained on large EHR data for Pancreatic Cancer prediction
9:40 - 10:00
Liang Li, MDACC 		GPU accelerated estimation of a shared random effect joint model for dynamic prediction
10:00 - 10:20 Coffee Break
10:20 - 10:40 Fei Wang, Weill Cornell Medicine Multi-modal learning for biomedicine
10:40 - 11:00 Zhongming Zhao, UTHealth Houston Deep generative neural network for accurate drug response prediction
11:00 - 12:00 Panel Discussion
Funda Meric-Bernstam, MDACC Robert T.Ripley, BCM Zhiqiang An, UTHealth Houston Nagireddy Putluri, BCM 		Data science and AI for cancer research: addressing challenges, embracing opportunities, and sharing a wish list – insights from the perspectives of basic and clinical cancer researchers and engaging in a dialogue with Data Science and AI experts.
12:00 End of Workshop
Monday, June 26, 1:00PM - 2:00 PM @BRC 286

The 1st International Workshop on Network and Pathway Analysis in Health Informatics (NPAHI 2023)
More detailed information is listed here: https://sites.google.com/unicz.it/npahi

Workshop Chairs:
Mario Cannataro, University Magna Graecia of Catanzaro (Italy)
Pietro Cinaglia, University Magna Graecia of Catanzaro (Italy)
Marianna Milano, University Magna Graecia of Catanzaro (Italy)
Giuseppe Agapito, University Magna Graecia of Catanzaro (Italy)

  • Paper presentations (1:00 PM - 2:00 PM)
    Accepted Papers (20 minutes each, 15 minutes for presentation, 5 minutes for discussion)
    • Edward Tsien and Dezhi Wu, (2023), "Introducing Task-Adaptive Loss to Multitask Learning for Electronic Healthcare Prediction"
    • Sabah Mohammed and Jinan Fiaidhi, (2023), "Investigation into Scaling-Up the SOAP Problem-Oriented Medical Record into a Clinical Case Study"
Monday, June 26, 2:15PM - 16:30 PM @BRC 286

The 1st International Workshop on AI for Pharmaceutical Discovery and Development (AIPHA 2023)
More detailed information is listed here: https://zhang-informatics.github.io/AIPHA2023/

Workshop Chairs:
Rui Zhang, PhD, Associate Professor, University of Minnesota, Minneapolis, MN, USA
Fei Wang, PhD, Associate Professor, Weill Cornell Medicine, Cornell University. NYC, NY, USA
Chang Su, PhD, Assistant Professor, Temple University, Philadelphia, PA, USA
You Chen, PhD, Assistant Professor, Vanderbilt University, Nashville, TN, USA

  • Paper presentations (2:15 PM - 4:30 PM)
    Accepted Papers (20 minutes each, 15 minutes for presentation, 5 minutes for discussion)
    • Eugene Jeong, Scott Nelson, Yu Su, Bradley Malin, Lang Li and You Chen, (2023), "Detecting drug-drug interactions between therapies for COVID-19 and concomitant medications through the FDA adverse event reporting system"
    • Ko-Hong Lin, Jay-Jiguang Zhu, Judith A. Smith, Yejin Kim and Xiaoqian Jiang, (2023), "An End-to-end In-Silico and In-Vitro Drug Repurposing Pipeline for Glioblastoma"
    • Raseen Tariq, Sheza Malik, Mousumi Roy, Meena Islam, Umair Rasheed, Jiang Bian, Kai Zheng and Rui Zhang, (2023), "Assessing ChatGPT for Text Summarization, Simplification and Extraction Tasks
    • Yongkang Xiao, Yu Hou, Huixue Zhou, Gayo Diallo, Marcelo Fiszman, Julia Wolfson, Halil Kilicoglu, You Chen, Hua Xu, William G. Mantyh and Rui Zhang, (2023), "Repurposing Drugs for Alzheimer's Diseases through Link Prediction on Biomedical Literature"
    • S M Shamimul Hasan, Greeshma Agasthya, Daniel Santel, Surbhi Bhatnagar, Ian Goethert, Tracy Glauser and John Pestian, (2023), "Application of Unified Medical Language System (UMLS) to Standardize Pediatric Drug Data"
    • Aokun Chen, Qian Li, Elizabeth Shenkman, Yonghui Wu, Yi Guo and Jiang Bian, (2023), "Exploring the Effect of Eligibility Criteria on AD Severity and Severe Adverse Event in Eligible Patients"
Monday, June 26, 13:00 PM - 16:30 PM @Auditorium

Doctoral Consortium

  • 1:00 - 4:30 PM: Student presentations
    • 1:00 - 1:25 PM: Sirui Ding, Towards Precise and Equitable Organ Allocation in Transplant with Machine Learning
    • 1:25 - 1:40 PM: Ahmed Mohammed Patel, The Design & Evaluation of an Information Visualization to Improve the Efficiency and Understanding of Patient Health State
    • 1:40 - 1:55 PM: Zidu Xu, ElCombo: Knowledge-based Personalized Meal Recommendation System for Chinese Community-dwelling Elders
    • 1:55 - 2:10 PM: Ge Gao, Trace Augmentation with Missing EHRs for Sepsis Treatments
    • 2:10 - 2:25 PM: Zehan Li, Suicide Tendency Prediction from Psychiatric Notes Using Transformer Models
    • 2:25 - 2:40 PM: Bingyu Mao, A deep-learning-based two-compartment predictive model (PKRNN-2CM) for vancomycin therapeutic drug monitoring
    • 2:40 - 2:55 PM: Sayantani Basu, Machine Learning and Visualizations for Time-Series Healthcare Data
    • 3:15 - 4:15 PM: Faculty Panel

Tuesday, June 27

Tuesday, June 27, 8:30 AM - 12：00 PM @Auditorium

Doctoral Consortium

  • 8:30 - 8:45 AM:
    • Edward Tsien, Developing Multi-Task Learning Methods to Aid in Electronic Healthcare Prediction
  • 8:45 - 9:00 AM:
    • Benjamin Corriette, Using VR to Elicit Empathy in Current and Future Psychiatrists for their Patients of Color
  • 9:00 - 9:15 AM:
    • Pallabi Bhowmick, Technologies to Reduce Social Isolation among Older Adults: A Move from Digital to Tangible
  • 9:15 - 9:30 AM:
    • Ayomide Owoyemi, Development of a determinant framework to guide the translation of AI systems in clinical care
  • 9:30 - 9:45 AM:
    • Mikel Ngueajio Kengni, Explainable Microaggression Detector for Improving Docto–Patient Interactions.
  • 10:00 - 12:00 PM:
    • Local Houston healthcare facility site visits.
Tuesday, June 27, 8:30AM - 12:00 PM @BRC-282

Tutorial 1: Modifying the Design Sprint Methodology: Leveraging Online Tools for Collaborating and Prototyping in Research Settings

Juandalyn Coffen-Burke, Kai-Wen Yang, Casey Overby Taylor

The slide can be download here

This tutorial provides an overview of a human-centered design approach called a design sprint. The main structure of the tutorial includes a review of the literature and motivation for conducting a design sprint, the step-by-step process of a traditional design sprint and alternative approaches relevant for research environments.

The tutorial will incorporate tools for planning and preparing the research environment and research team, interactive technological tools for collaboration, guided instruction on conducting a data analysis and research-related challenges and solutions to be considered. We use examples from the literature and a user interface design research project related to the review process for genomic medicine services.

This tutorial is intended for participants interested in human-centered design and learning how to utilize state of the art tools and resources to apply a modified design sprint methodology in a research setting.

Tuesday, June 27, 8:30AM - 12:00 PM @BRC-284

Tutorial 2: Training Recurrent Neural Network-Based Model to Predict COVID-19 Patient Risk for PASC using Pytorch_EHR

Laila Rasmy, Ziqian Xie, Degui Zhi

Pytorch_EHR is a codebase enabling fast prototyping of deep learning-based predictive models using electronic health records structured data. Rather than a collection of vertical pipelines implementing methods from papers claiming state-of-the-art results, Pytorch_EHR offers efficient implementations of data flow, padding, embeddings, choices of popular recurrent neural networks (RNN) cells and layers structures, prediction heads, and also predictions explanation and visualization.

This tutorial will provide participants with a clear understanding of deep learning theoretical concepts behind Pytorch_EHR different components, as well as hands-on experience in building an explainable RNN-based model to solve a real-world clinical problem on a cloud-based platform hosting the National COVID Cohort Collaborative (N3C) data. URL: https://github.com/ZhiGroup/pytorch_ehr.

We are targeting researchers and clinicians who are interested to learn more about deep learning methods and how to apply them to answer a real-world clinical question. The audience should have basic python programming skills. In order for the audience to get the best benefit from the tutorial, we would recommend they apply for access to the N3C synthetic data following the instructions on https://covid.cd2h.org/N3C_synthetic_data.

Tuesday, June 27, 8:30AM - 12:00 PM @BRC-280

Tutorial 3: Enabling AI-Augmented Clinical Workflows by Accessing Patient Data in Real-Time with FHIR

Vincent Major, Walter Wang, Yindalon Aphinyanaphongs

The slide can be download here

AI systems developed for clinical applications often need to operate in real-time to achieve their intended impacts. However, sourcing data inputs in real-time remains a challenge. FHIR involves a set of interoperable resources that enable reading of patient data directly from the EHR and can be used to retrieve data inputs to feed into AI models. This tutorial will introduce FHIR and walk-through how to configure a FHIR app in Epic and develop python code to automatically read patient data and forward it to downstream AI systems.

Researchers, AI scientists, data scientists, data engineers, solutions developers, and systems architects are the intended audience of this tutorial but anyone with experience developing AI models with clinical data or an interest in deploying AI systems are welcome. While several others will be required to configure security permissions for the new application, these core roles will be able to design and build a custom system for integration into the EHR. Familiarity with different types of clinical data and how it is stored in the EHR will be beneficial but is not required.

Tuesday, June 27, 8:30AM - 12:00 PM @BRC-286

Tutorial 4: Trustworthy Computing in Biomedical Challenges

Haohan Wang

The slide can be download here

Given the crucial roles of trustworthy machine learning in the deployment of computational medicine problems, this tutorial aims to provide a comprehensive overview of trustworthy machine learning techniques with a focus on biomedical problems, including robustness across different data collections such as batch effects, population stratification, interpretability of machine learning models applied to medical images and genetic sequences, and the fairness challenges of machine learning across various realistic scenarios. Furthermore, in parallel with this tutorial that offers a comprehensive summary of major trustworthy machine learning techniques developed for various biomedical applications, we also aim to provide an overarching perspective on the development of trustworthy machine learning techniques that will not only cover the primary trustworthy machine learning techniques developed but also guide the future development of trustworthy machine learning.

The tutorial is targeted to the audience of two-fold: the audience with biomedical background and interests in machine learning that are seeking trustworthy machine learning solutions; and the audience with machine learning technical knowledge that either need to be familiarized with more practical challenges in biomedicine, or seeking a higher-level understanding of trustworthy machine learning.

Tuesday, June 27, 1:00 - 1:20 PM @Auditorium

Welcome Session

General Chairs: Hua Xu and Xia Hu will give an introduction talk

Tuesday, June 27, 1:20 - 2:15 PM @Auditorium

Keynote 1: Challenges and Opportunities for Improving Patient Safety Through Data Science and Informatics
Dr. Patricia C. Dykes

Dr. TBD Abstract: Patient safety informatics is an important area of research because despite our good intentions, medical harms are a principal cause of preventable injury and remain a leading cause of death in the Unites States and globally. Data science and informatics offer significant opportunities for improving patient safety, including the ability to develop predictive models that can identify patients at risk of adverse events and interventions including clinical decision support that can mitigate those risks. Additionally, advanced analytics can help healthcare providers identify patterns and trends that may indicate safety concerns, allowing for proactive interventions to prevent harm. This presentation will highlight data science and informatics tools and approaches that have the potential to significantly improve patient safety and reduce the risk of adverse events in healthcare settings.

Tuesday, June 27, 2:30 - 4:30 PM @Auditorium

ChatGPT Panel: Large Language Models: Opportunities and Challenges

More details about the confirmed speakers and panelistes can be found here: https://bionlplab.github.io/2023_ichi_llm/

Tuesday, June 27, 2:30 PM - 4:00 PM @BRC 282

A1 Analytics Session 1: Analytics

S Causal Structure Learning from Imperfect Longitudinal Data in Healthcare
Haoyu Yang
S Supporting Physicians Through Prescriptive Process Monitoring
Steven Mertens
S A BPMN-based Framework to Manage Enhanced Recovery After Surgery (ERAS) Pathway for Patients Undergoing Pancreatic Surgery
Matteo Mantovani
S Leveraging Social Support Types and Link Prediction for User Recommendation for Online Health Community in Pregnancy After Loss
Michal Monselise
Tuesday, June 27, 2:30 PM - 4:00 PM @BRC 284

S1 System Session 1: Design and Development

S Automated Assessment of Critical View of Safety in Laparoscopic Cholecystectomy
Yunfan Li
S Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology
Hilda Klasky
S Development of a Natural Language Processing Tool to Extract Acupuncture Point Location Terms
Yiming Li
S Closing the Loop for Patients with Chronic Diseases - from Problems to a Solution Architecture
Andri Färber
Tuesday, June 27, 2:30 PM - 4:00 PM @BRC 286

H1 Human Factors Session 1: Human-centered Design

S (Non-)Physiological Data for Wearables in VR Sport Applications and Exergames
Dirk Queck
S Characterizing the Users of Patient Portal Messaging: A Single Institutional Cohort Study
Ming Huang
S A Process for Evaluating Explanations for Transparent and Trustworthy AI Prediction Models
Erhan Pisirir
S Designing Software for Genomics Medicine Service Leadersto Engage Stakeholders
Casey Overby Taylor
S Patient Dashboards of Electronic Health Record Data to Support Clinical Care: A Systematic Review
Bárbara Ramalho
Tuesday, June 27, 4:00 PM - 5:30 PM @Event/Exhibition Space

Poster & Demo Session

Board Number: X

1 Evaluation of Healthprompt for Zero-shot Clinical Text Classification
Sonish Sivarajkumar and Yanshan Wang
2 Medication Knowledge Graph Analysis Using the PageRank Algorithm
S M Shamimul Hasan, Heidi Hanson and Anuj Kapadia
3 Precise Segmentation of U.S. Adults from 24-Hour Wearable-based Physical Activity Profiles Using Machine Learning Clustering
Jinjoo Shim, Elgar Fleisch and Filipe Barata
4 Sample Size Determination for Electronic Phenotyping
Sai Dharmarajan, Satabdi Saha, Xinying Fang and Jaejoon Song
5 3D Trajectory Visualization for Robotic-Assisted Surgery Review
Ye Li and Kiran Bhattacharyya
6 Deriving Biomedical Lexicons from Social Media with Named Entity Recognition and Normalization
Yining Hua, Shixu Lin, Minghui Li, Yujie Zhang, Peilin Zhou, Ying-Chih Lo, Li Zhou and Jie Yang
7 Cataract Severity Rating from Small Sample of Anchor Images: A Thick Data Approach
Jinan Fiaidhi and Sabah Mohammed
8 A Taxonomy of Situation Awareness Failure Factors in Primary Care
Ahmed Patel, Talya Porat and Weston Baxter
9 Transfer Learning for Classification of Retinal Disease using Fundus Imaging
Jenny Ogden and Hamidreza Moradi
10 Feasibility of Categorizing Rehabilitation Gestures for Automated Fidelity Assessment on Strategy Training using Deep Learning
Hunter Osterhoudt, Liann Ching, Minmei Shih, Courtney Schneider, Alexandra Harper, Haneef Mohammad, Elizabeth Skidmore, Yanshan Wang and Leming Zhou
11 Health Literacy Drift in Online Health Communities
Jiayi Meng, Xi Wang, Zhiya Zuo and Hui Li
13 Medical Image Segmentation using Persistent Homology Net (PH-Net)
Ahmad Al Shami, Christian Young, Ahmad Tafti, Noah Garland, Dalton Grissom and Chance McDonald
14 Identifying Dynamic User Roles in Online Health Communities
Xinxi Zhu, Yangruohan Li, Zhiya Zuo and Xi Wang
15 Implementing an NLP Tool to Address SDOH Needs
Danielle Jungst, Emily Kwan and Urmila Ravichandran
16 Colorectal cancer prognosis prediction model based on sufficient causal representations
Yuanbing Qin, Yu Tian, Tianshu Zhou, Shengqiang Chi, Jun Li, Kefeng Ding and Jingsong Li
17 A Deep Learning-driven Approach for Transgender and Gender Diverse Patient Identification in EHRs
Yining Hua, Liqin Wang, Vi Nguyen, Dinah Foer and Li Zhou
18 Evaluating Pre-trained Language Models for Classifying Patient Portal Messages
Yang Ren, Dezhi Wu, Aditya Khurana, George Mastorakos, Sunyang Fu, Nansu Zong, Jungwei Fan, Hongfang Liu and Ming Huang
19 Learning Physician’s Treatment for Alzheimer's Disease based on Electronic Health Records and Reinforcement Learning
Kritib Bhattarai, Trisha Das, Yejin Kim, Yongbin Chen, Qiying Dai, Xiaoyang Li, Xiaoqian Jiang and Nansu Zong
20 Spatial Temporal Change of COVID-19 Vaccination Status in the US: An Exploration based on Space Time Cube
Yue Hao

Wednesday, June 28

Wednesday, June 28, 7:30 - 8:45 AM @Event/Exhibition Space

Women in Healthcare Informatics Meet & Greet

Wednesday, June 28, 9:00 - 10:00 AM @Auditorium

Keynote 2: Explainable Deep Tabular Learning Models for Biomedical and Healthcare Applications
Dr. Aidong Zhang

Dr. Aidong Zhang Abstract: Neural networks have achieved great success in many tasks. However, their black-box nature limits their use in sensitive areas such as medical applications in which both performance in accuracy and intelligibility are critically important. In this talk, I will discuss our recent research on explainable deep tabular learning, a specific kind of deep learning models for tabular data which is very common in medical and healthcare applications. In particular, I will show how the concept-based learning models and example-based learning models can be designed for explainable deep tabular learning. I will also discuss their applications in biomedicine and healthcare.

Wednesday, June 28, 10:30 - 12:00 PM @BRC 282

A2 Analytics Session 2: Deep Learning

S A neural network system for rating student performance in surgical knot tying simulation
Yunzhe Xue
S AHP-CM: Attentional Homogeneous-Padded Composite Model for Respiratory Anomalies Prediction
Md Motiur Rahman
S End-to-End $n$-ary Relation Extraction for Combination Drug Therapies
Yuhang Jiang
S Mitigating Membership Inference in Deep Survival Analyses with Differential Privacy
Liyue Fan
S Inferring Personalized Treatment Effect of Antihypertensives on Alzheimer’s Disease Using Deep Learning
Xiaoqian Jiang
Wednesday, June 28, 10:30 - 12:00 PM @BRC 284

A3 Analytics Session 3: Representations

S Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction
Homa Hosseinmardi
S Optimizing Embedding Space with Sub-categorical Supervised Pre-training: A Theoretical Approach Towards Improving Sepsis Prediction
Tingyi Wanyan
S Beyond Labels: Visual Representations for Bone Marrow Cell Morphology Recognition
Shayan Fazeli
S Classification Graph to the Internet of Health Things Applications
Evilasio Costa Junior
Wednesday, June 28, 10:30 - 12:00 PM @BRC 286

H2 Human Factors Session 2:

S An LSTM-based Gesture-to-Speech Recognition System
Riyad Bin Rafiq
S Social Contextualization of Datasets for Mental Health AI: a Review of Gender-linked Sociotechnical Misalignments
Xing Chen
S The need for the human-centred explanation for ML-based clinical decision support systems
Yan Jia
S Population-Level Visual Analytics of Smartphone Sensed Health and Wellness Using Community Phenotypes
Hamid Mansoor (Remote)
S Sleep Monitoring: Enriching the Traditional Approach by Sensor-collected Data
Giuseppe Pozzi (Remote)
Wednesday, June 28, 1:00 - 2:30 PM @BRC 282

A4 Analytics Session 4: COVID Related

S Prediction of COVID-19 Patients’ Emergency Room Revisit using Multi-Source Transfer Learning
Yuelyu Ji
S Which Features Are Useful in Machine Learning-Based COVID-19 Prognostication? A Meta-Analysis
Md Zakir Hossain
S Long COVID Challenge: Predictive Modeling of Noisy Clinical Tabular Data
Mirna Elizondo
S Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention
Degan Hao
Wednesday, June 28, 1:00 - 2:30 PM @BRC 284

A5 Analytics Session 5: NLP

S Prompting for Few-shot Adverse Drug Reaction Recognition from Online Reviews
Chia-Hsuan Chang
S Classification of Patient Portal Messages with BERT-based Language Models
Ming Huang
S Identifying Major Depressive Disorder From Clinical Notes Using Neural Language Models with Distant Supervision
Kurt Miller
S Detecting Reddit Users with Depression Using A Hybrid Neural Network SBERT-CNN
Ming Huang
S Extracting periodontitis diagnosis in clinical notes with RoBERTa and regular expression
Yao-Shun Chuang
Wednesday, June 28, 1:00 - 2:30 PM @BRC 286

A6 Analytics Session 6: Spatio-Temporal

S Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data
Peikun Guo
S Cluster Analysis to Find Temporal Physical Activity Patterns Among US Adults
Jiaqi Guo
S Spatial-Temporal Networks for Antibiogram Pattern Prediction
Xingbo Fu
S CVAE-based Generator for Variable Length Synthetic ECG
Sagnik Dakshit
S Twelve Lead Double Stacked Generalization for ECG Classification
Sagnik Dakshit
Wednesday, June 28, 3:00 - 4:30 PM @BRC 282

A7 Analytics Session 7: Phenotyping, Fairness and Explainability

S Enforcing Explainable Deep Few-Shot Learning to Analyze Plain Knee Radiographs: Data from the Osteoarthritis Initiative
Ahmad P. Tafti
S Quantification of racial disparity on urinary tract infection recurrence and treatment resistance in Florida using algorithmic fairness methods
Inyoung Jun
S PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data
Xia Hu
S FROM DIGITAL PHENOTYPE IDENTIFICATIONTO DETECTION OF PSYCHOTIC RELAPSES
Niki Efthymiou
S A Lightweight Segmentation Method for Mandibular Canal Based on Arch Shape and Hough Transform
Maira Beatriz Hernandez Moran
Wednesday, June 28, 3:00 - 4:30 PM @BRC 284

A8 Analytics Session 8: Deep Learning (2)

S Improving prediction of late symptoms using LSTM and patient-reported outcomes for head and neck cancer patients
Yaohua Wang
S Early Diagnosis of Mild Cognitive Impairment Using Deep Neural Network with and without Multi-input Analysis
Pouneh Abbasian
S Zero-Shot Meta-Learning for Small-Scale Data From Human Subjects
Julie Jiang
Wednesday, June 28, 3:00 - 4:30 PM @Remote

Remote Session@BRC 286

Paper presentations (3:00 PM - 4:30 PM)
Accepted Papers (15 minutes each, 12 minutes for presentation, 3 minutes for discussion)

  • Chen Lin, Jianghong Zhou, Jing Zhang, Carl Yang and Eugene Agichtein, (2023), "Graph Neural Network Modeling of Web Search Activity for Real-time Pandemic Forecasting"
  • Lin Li, Mimi Liu, Cong Lai, Weidong Ji, Kewei Xu and Yi Zhou, (2023), "Analysis of residual stones in patients and related influencing factors after percutaneous nephrolithotomy: a retrospective study"
  • Arthur Ricardo Sousa Vitoria, Adriel Lennner Vinhal Mori, Diogo Fernandes Costa Silva, Daniel do Prado Pagotto, Clarimar José Coelho and Arlindo Rodrigues Galvão Filho, (2023), "Live births forecasting across health regions of Goiás using artificial neural networks: a clustering approach

Wednesday, June 28, 3:00 - 4:30 PM @Auditorium

Panel: Resuming Healthcare Informatics Research after CoViD-19: The Healthcare System Perspective

TBD

Thursday, June 29

Thursday, June 29, 8:45 - 9:45 AM @Ballroom 2

Keynote 3: Data and System Harmonization: Reflections and observations from 25 years of research in disparate fields Dr. Ed Nykaza

Dr. Ed Nykaza Abstract: Drawing on 25 years of personal and professional research experience in the fields of acoustics, psychology, diabetes, and data science, this presentation will share some of the general patterns (and problems) that exist across multiple and disparate disciplines. Perhaps these general patterns, or universal principles, hold the keys to solving complex system problems? This talk will cover some general topics such as the importance of starting with why, fusing human expertise and AI technology, and creating trusted interdependent systems. Real-world examples from the field of diabetes will be given with a focus on patient-centered care using just-in-time-adaptive interventions and clinician facing precision engagements.

Thursday, June 29, 10:00 AM - 12:00 PM @Auditorium

Industry Session

S Case Volumes in Users and Non-Users of a Digital Application to Visualize Surgical Workflow Data
Jeffrey Voien, Ameya Shere, Robert Mostellar and Gretchen Jackson
S Intent Recognition on Low-Resource Language Messages in a Health Marketplace Chatbot
David Tresner-Kirsch, Amanda Azari Mikkelson, Chika Yinka-Banjo, Mary Akinyemi and Siddhartha Goyal
S Document Understanding for Healthcare Referrals
Jimit Mistry and Natalia Arzeno
S Validation of a Hospital Digital Twin with Machine Learning
Muhammad Ahmad, Vijay Chickarmane, Farinaz Ali Sabzpour, Nima Shariari and Taposh Roy
S DATA PLATFORM TO ACCELERATE HEALTHCARE INSIGHTS GENERATION
Arun Sundararaman
Thursday, June 29, 12:00 - 12:30 PM @Auditorium

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