Demo Data from SIGIR 2026 / reviewers in the wild / expert
David M. Liebovitz
dblp:31/10314
· DBLP profile ↗
18ranked-venue papers
1as first author
6since 2021 · last 2025
0000-0002-2518-5940ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 11 · 1 first-author · 3 since 2021Security and privacy · 4 · 1 since 2021Artificial intelligence and machine learning · 3 · 2 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
2 papers |
Privacy and data protection · 88% Authentication and access control · 12% | |
| Artificial intelligence
1 paper |
Transfer learning and domain adaptation · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Transfer learning and domain adaptation
domain generalization |
0.7 | 1 | 2023 | An Iterative Self-Learning Framework for Medical Domain Generalization · NeurIPS 2023 |
Machine learning › Transfer learning and domain adaptation › model adaptation
personalized model |
0.7 | 1 | 2023 | An Iterative Self-Learning Framework for Medical Domain Generalization · NeurIPS 2023 |
Privacy and data protection
anonymization |
0.7 | 1 | 2023 | How to Cover up Anomalous Accesses to Electronic Health Records · USENIX Security Symposium 2023 |
Medical and health informatics
electronic health records |
0.2 | 1 | 2023 | An Iterative Self-Learning Framework for Medical Domain Generalization · NeurIPS 2023 |
Authentication and access control
access control |
0.2 | 1 | 2014 | Decide Now or Decide Later?: Quantifying the Tradeoff between Prospective and Retrospective Access Decisions · CCS 2014 |
Methods — techniques the papers use, named apart from their topics
self-learning · 1.3domain decoupling · 1.3anomaly detection · 0.7classification · 0.2bispective analysis · 0.2ROC analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Large language models accurately identify immunosuppression in intensive care unit patientsabstractOBJECTIVE: Rule-based structured data algorithms and natural language processing (NLP) approaches applied to unstructured clinical notes have limited accuracy and poor generalizability for identifying immunosuppression. Large language models (LLMs) may effectively identify patients with heterogenous types of immunosuppression from unstructured clinical notes. We compared the performance of LLMs applied to unstructured notes for identifying patients with immunosuppressive conditions or immunosuppressive medication use against 2 baselines: (1) structured data algorithms using diagnosis codes and medication orders and (2) NLP approaches applied to unstructured notes. MATERIALS AND METHODS: We used hospital admission notes from a primary cohort of 827 intensive care unit (ICU) patients at Northwestern Memorial Hospital and a validation cohort of 200 ICU patients at Beth Israel Deaconess Medical Center, along with diagnosis codes and medication orders from the primary cohort. We evaluated the performance of structured data algorithms, NLP approaches, and LLMs in identifying 7 immunosuppressive conditions and 6 immunosuppressive medications. RESULTS: In the primary cohort, structured data algorithms achieved peak F1 scores ranging from 0.30 to 0.97 for identifying immunosuppressive conditions and medications. NLP approaches achieved peak F1 scores ranging from 0 to 1. GPT-4o outperformed or matched structured data algorithms and NLP approaches across all conditions and medications, with F1 scores ranging from 0.51 to 1. GPT-4o also performed impressively in our validation cohort (F1 = 1 for 8/13 variables). DISCUSSION: LLMs, particularly GPT-4o, outperformed structured data algorithms and NLP approaches in identifying immunosuppressive conditions and medications with robust external validation. CONCLUSION: LLMs can be applied for improved cohort identification for research purposes. Vijeeth Guggilla, Mengjia Kang, Melissa J. Bak, Steven D. Tran, Anna Pawlowski, Prasanth Nannapaneni, Luke V. Rasmussen, Helen K. Donnelly, Ankit Agrawal 0001, David M. Liebovitz, Alexander V. Misharin, G. R. Scott Budinger, Richard G. Wunderink, Theresa Walunas, Catherine A. Gao, Alan R. Hauser, Alec Peltekian, Alexis Rose Wolfe, Alison L. Szabo, Alok N. Choudhary, Amy Ludwig, Anahid Amani Moghadam, Anjana V. Yeldandi, Ankit Bharat, Anna E. Pawlowski, Anthony M. Joudi, Arjun Prakash Tambe, Ashley J. Smith-Nunez, Benjamin D. Singer, Benjamin J. Ulrich, Betty Tran, Cara J. Gottardi, Chiagozie O. Pickens, Clara J. Schroedl, Daniel Meza, Dulce Sarai Garcia, Egon A. Ozer, Elen Gusman, Elisheva D. Shanes, Emily Mower Provost, Emily M. Olson, Erica Marie Hartmann, Erin A. Korth, Estefani Diaz, Estefany R. Guzman, Francisco J. Martinez, Gabrielle Matias, Hiam Abdala-Valencia, Jack T. Sumner, Jacob I Sznajder, Jacqueline M. Kruser, Jakub Glowala, James M. Walter, Jamie H. Rowell, Jason M. Arnold, John Coleman, Jon W. Lomasney, Joseph Isaac Bailey, Judd F. Hultquist, Justin A. Fiala, Justin Starren, Karen M. Ridge, Karolina Senkow, Kathryn A. Helmin, Khalilah L. Gates, Lacy Simmons, Lesley Pinzon, Lindsey D. Gradone, Lisa F. Wolfe, Lucy Luo, Luisa Morales-Nebreda, Manu Jain, Marc Sala, Maxwell Schleck, Melissa H. Ross, Melissa Querrey, Michael J. Cuttica, Michelle Hinsch Prickett, Nandita R. Nadig, Nathaniel Rhodes, Navdeep S. Chandel, Nikolay S. Markov, Peter H. S. Sporn, Qianli Liu, Rachel B. Kadar, Rachel L. Medernach, Ramon Lorenzo-Redondo, Ravi Kalhan, Rebecca K. Clepp, Richard I. Morimoto, Rogan A. Grant, Ruben J. Mylvaganam, Samuel Fenske, Scott A. Laurenzo, Seung Hye Han, Sophia Nozick, Srinivas Panchamukhi, Stephanie C. Eisenbarth, Suchitra Swaminathan, Susan R. Russell, Taylor A. Poor, Thaddeus Cybulski, Theresa A. Lombardo, Thomas Bolig, Thomas Stoeger, Tien Doan, Timothy Rowe, Wan-Ting Liao, Yuan Luo 0001, Yuliana Sokolenko, Ziyan Lu |
J. Am. Medical Informatics Assoc. | 11 |
| 2023 | An Iterative Self-Learning Framework for Medical Domain GeneralizationabstractDeep learning models have been widely used to assist doctors with clinical decision-making. However, these models often encounter a significant performance drop when applied to data that differs from the distribution they were trained on. This challenge is known as the domain shift problem. Existing domain generalization algorithms attempt to address this problem by assuming the availability of domain IDs and training a single model to handle all domains. However, in healthcare settings, patients can be classified into numerous latent domains, where the actual domain categorizations are unknown. Furthermore, each patient domain exhibits distinct clinical characteristics, making it sub-optimal to train a single model for all domains. To overcome these limitations, we propose SLGD, a self-learning framework that iteratively discovers decoupled domains and trains personalized classifiers for each decoupled domain. We evaluate the generalizability of SLGD across spatial and temporal data distribution shifts on two real-world public EHR datasets: eICU and MIMIC-IV. Our results show that SLGD achieves up to 11% improvement in the AUPRC score over the best baseline. Zhenbang Wu, Huaxiu Yao, David M. Liebovitz, Jimeng Sun 0001 |
NeurIPS | 3 |
| 2023 | How to Cover up Anomalous Accesses to Electronic Health Records
Qingying Hao, Bo Li 0026, David M. Liebovitz, Gang Wang 0011, Carl A. Gunter |
USENIX Security Symposium | 5 |
| 2022 | AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment
Zhenbang Wu, Cao Xiao, Lucas Glass, David M. Liebovitz, Jimeng Sun 0001 |
ECML/PKDD (2) | 4 |
| 2022 | Indication alerts to improve problem list documentationabstractBACKGROUND: Problem lists represent an integral component of high-quality care. However, they are often inaccurate and incomplete. We studied the effects of alerts integrated into the inpatient and outpatient computerized provider order entry systems to assist in adding problems to the problem list when ordering medications that lacked a corresponding indication. METHODS: We analyzed medication orders from 2 healthcare systems that used an innovative indication alert. We collected data at site 1 between December 2018 and January 2020, and at site 2 between May and June 2021. We reviewed random samples of 100 charts from each site that had problems added in response to the alert. Outcomes were: (1) alert yield, the proportion of triggered alerts that led to a problem added and (2) problem accuracy, the proportion of problems placed that were accurate by chart review. RESULTS: Alerts were triggered 131 134, and 6178 times at sites 1 and 2, respectively, resulting in a yield of 109 055 (83.2%) and 2874 (46.5%), P< .001. Orders were abandoned, for example, not completed, in 11.1% and 9.6% of orders, respectively, P<.001. Of the 100 sample problems, reviewers deemed 88% ± 3% and 91% ± 3% to be accurate, respectively, P = .65, with a mean of 90% ± 2%. CONCLUSIONS: Indication alerts triggered by medication orders initiated in the absence of a justifying diagnosis were useful for populating problem lists, with yields of 83.2% and 46.5% at 2 healthcare systems. Problems were placed with a reasonable level of accuracy, with 90% ± 2% of problems deemed accurate based on chart review. Anne Grauer, Jerard Kneifati-Hayek, Brian Reuland, Jo R. Applebaum, Jason S. Adelman, Robert A. Green, Jeanette Lisak-Phillips, David M. Liebovitz, Thomas F. Byrd, Preeti Kansal, Cheryl Wilkes, Suzanne Falck, Connie Larson, John Shilka, Elizabeth Vandril, Gordon D. Schiff, William L. Galanter, Bruce L. Lambert |
J. Am. Medical Informatics Assoc. | 8 |
| 2021 | Implementation of Medication Alerts to Reduce Wrong-Drug and Wrong-Patient Errors in CPOE Systems
Yuyang Yang, David M. Liebovitz, William L. Galanter, Jason S. Adelman, Thomas F. Byrd |
AMIA | 2 |
| 2020 | Poster: An Unexpected Pathway for Fentanyl Orders
David M. Liebovitz, Kelsey Waier, Seth Hartman |
AMIA | 1 |
| 2020 | AllergyMap: An Open Source Corpus of Allergy Mention Normalizations
Amy Y. Wang, John D. Osborne, Maria I. Danila, Andrew Naidech, David M. Liebovitz |
AMIA | 5 |
| 2018 | Learning bundled care opportunities from electronic medical records
You Chen 0001, Abel N. Kho, David M. Liebovitz, Catherine Ivory, Sarah Osmundson, Jiang Bian 0001, Bradley A. Malin |
J. Biomed. Informatics | 3 |
| 2016 | Learning Clinical Workflows to Identify Subgroups of Heart Failure Patients
Chao Yan 0004, You Chen 0001, Bo Li 0026, David M. Liebovitz, Bradley A. Malin |
AMIA | 4 |
| 2016 | The effect of tablet computers with a mobile patient portal application on hospitalized patients' knowledge and activationabstractOBJECTIVE: To assess the effect of tablet computers with a mobile patient portal application on hospitalized patients' knowledge and activation. METHODS: We developed a mobile patient portal application including pictures, names, and role descriptions of team members, scheduled tests and procedures, and a list of active medications. We evaluated the effect of the application using a controlled trial involving 2 similar units in a large teaching hospital. Patients on the intervention unit were offered use of tablet computers with the portal application during their hospitalization. We assessed patients' ability to correctly name their nurse, primary service physicians, physician roles, planned tests and procedures, medications started, and medications stopped since admission. We also administered the Short Form of the Patient Activation Measure. RESULTS: Overall, 100 intervention- and 102 control-unit patients participated. A higher percentage of intervention-unit patients correctly named ≥1 physician (56% vs 29.4%; P < .001) and ≥1 physician role (47% vs 15.7%; P < .001). Knowledge of nurses' names, planned tests, planned procedures, and medication changes was generally low and not significantly different between the study units. The Short Form of the Patient Activation Measure mean (SD) score was also not significantly different at 64.1 (13.4) vs 62.7 (12.8); P = .46. CONCLUSIONS: Additional research is needed to identify optimal methods to engage and inform patients during their hospitalization, which will improve preparation for self- management after discharge. Kevin J. O'Leary, Mary E. Lohman, Eckford Culver, Audrey Killarney, G. Randy Smith Jr., David M. Liebovitz |
J. Am. Medical Informatics Assoc. | 6 |
| 2016 | Real-Time Tele-Monitoring of Patients with Chronic Heart-Failure Using a Smartphone: Lessons LearnedabstractWe present a smartphone-based system for real-time tele-monitoring of physical activity in patients with chronic heart-failure (CHF). We recently completed a pilot study with 15 subjects to evaluate the feasibility of the proposed monitoring in the real world and examine its requirements, privacy implications, usability, and other challenges encountered by the participants and healthcare providers. Our tele-monitoring system was designed to assess patient activity via minute-by-minute energy expenditure (EE) estimated from accelerometry. In addition, we tracked relative user location via global positioning system (GPS) to track outdoors activity and measure walking distance. The system also administered daily surveys to inquire about vital signs and general cardiovascular symptoms. The collected data were securely transmitted to a central server where they were analyzed in real time and were accessible to the study medical staff to monitor patient health status and provide medical intervention if needed. Although the system was designed for tele-monitoring individuals with CHF, the challenges, privacy considerations, and lessons learned from this pilot study apply to other chronic health conditions, such as diabetes and hypertension, that would benefit from continuous monitoring through mobile-health (mHealth) technologies. Daniel Aranki, Gregorij Kurillo, Posu Yan, David M. Liebovitz, Ruzena Bajcsy |
IEEE Trans. Affect. Comput. | 4 |
| 2015 | Inferring Clinical Workflow Efficiency via Electronic Medical Record Utilization
You Chen 0001, Wei Xie 0002, Carl A. Gunter, David M. Liebovitz, Sanjay Mehrotra, Bradley A. Malin |
AMIA | 4 |
| 2015 | Building bridges across electronic health record systems through inferred phenotypic topics
You Chen 0001, Joydeep Ghosh, Cosmin Adrian Bejan, Carl A. Gunter, Siddharth Gupta 0005, Abel N. Kho, David M. Liebovitz, Jimeng Sun 0001, Joshua C. Denny, Bradley A. Malin |
J. Biomed. Informatics | 7 |
| 2014 | Decide Now or Decide Later?: Quantifying the Tradeoff between Prospective and Retrospective Access DecisionsabstractOne of the greatest challenges an organization faces is determining when an employee is permitted to utilize a certain resource in a system. This "insider threat" can be addressed through two strategies: i) prospective methods, such as access control, that make a decision at the time of a request, and ii) retrospective methods, such as post hoc auditing, that make a decision in the light of the knowledge gathered afterwards. While it is recognized that each strategy has a distinct set of benefits and drawbacks, there has been little investigation into how to provide system administrators with practical guidance on when one or the other should be applied. To address this problem, we introduce a framework to compare these strategies on a common quantitative scale. In doing so, we translate these strategies into classification problems using a context-based feature space that assesses the likelihood that an access request is legitimate. We then introduce a technique called bispective analysis to compare the performance of the classification models under the situation of non-equivalent costs for false positive and negative instances, a significant extension on traditional cost analysis techniques, such as analysis of the receiver operator characteristic (ROC) curve. Using domain-specific cost estimates and access logs of several months from a large Electronic Medical Record (EMR) system, we demonstrate how bispective analysis can support meaningful decisions about the relative merits of prospective and retrospective decision making for specific types of hospital personnel. You Chen 0001, Thaddeus Cybulski, Daniel Fabbri, Carl A. Gunter, Patrick N. Lawlor, David M. Liebovitz, Bradley A. Malin |
CCS | 7 |
| 2013 | Modeling and detecting anomalous topic accessabstractThere has been considerable success in developing strategies to detect insider threats in information systems based on what one might call the random object access model or ROA. This approach models illegitimate users as ones who randomly access records. The goal is to use statistics, machine learning, knowledge of workflows and other techniques to support an anomaly detection framework that finds such users. In this paper we introduce and study a random topic access model or RTA aimed at users whose access may be illegitimate but is not fully random because it is focused on common semantic themes. We argue that this model is appropriate for a meaningful range of attacks and develop a system based on topic summarization that is able to formalize the model and provide anomalous user detection effectively for it. To this end, we use healthcare as an example and propose a framework for evaluating the ability to recognize various types of random users called random topic access detection or RTAD. Specifically, we utilize a combination of Latent Dirichlet Allocation (LDA), for feature extraction, a k-nearest neighbor (k-NN) algorithm for outlier detection and evaluate the ability to identify different adversarial types. We validate the technique in the context of hospital audit logs where we show varying degrees of success based on user roles and the anticipated characteristics of attackers. In particular, it was found that RTAD exhibits strong performance for roles are described by a few topics, but weaker performance when users are more topic-agnostic. Siddharth Gupta 0005, Casey Hanson, Carl A. Gunter, Mario Frank 0001, David M. Liebovitz, Bradley A. Malin |
ISI | 5 |
| 2013 | Evolving role definitions through permission invocation patternsabstractIn role-based access control (RBAC), roles are traditionally defined as sets of permissions. Roles specified by administrators may be inaccurate, however, such that data mining methods have been proposed to learn roles from actual permission utilization. These methods minimize variation from an information theoretic perspective, but they neglect the expert knowledge of administrators. In this paper, we propose a strategy to enable a controlled evolution of RBAC based on utilization. To accomplish this goal, we extend a subset enumeration framework to search candidate roles for an RBAC model that addresses an objective function which balances administrator beliefs and permission utilization. The rate of role evolution is controlled by an administrator-specified parameter. To assess effectiveness, we perform an empirical analysis using simulations, as well as a real world dataset from an electronic medical record system (EMR) in use at a large academic medical center (over 8000 users, 140 roles, and 140 permissions). We compare the results with several state-of-the-art role mining algorithms using 1) an outlier detection method on the new roles to evaluate the homogeneity of their behavior and 2)a set-based similarity measure between the original and new roles. The results illustrate our method is comparable to the state-of-the-art, but allows for a range of RBAC models which tradeoff user behavior and administrator expectations. For instance, in the EMR dataset, we find the resulting RBAC model contains 22% outliers and a distance of 0.02 to the original RBAC model when the system is biased toward administrator belief, and 13% outliers and a distance of 0.26 to the original RBAC model when biased toward permission utilization. You Chen 0001, Carl A. Gunter, David M. Liebovitz, Bradley A. Malin |
SACMAT | 4 |
| 2007 | Utility of commonly captured data from an EHR to identify hospitalized patients at risk for clinical deterioration
Abel N. Kho, David Rotz, Kinan Alrahi, Wendy Cárdenas, Kristin Ramsey, David M. Liebovitz, Gary Noskin, Chuck Watts |
AMIA | 6 |