Luke V. Rasmussen

VLDB 2026 Research / reviewers in the wild / expert

dblp:25/11037 · DBLP profile ↗

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63ranked-venue papers

11as first author

14since 2021 · last 2026

0000-0002-4497-8049ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 63 · 11 first-author · 14 since 2021

Year	Publication	Venue	Position
2026	PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse abstract BACKGROUND: Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse. OBJECTIVE: To develop a framework for assessing phenotyping algorithm fitness for purpose and reuse. FITNESS FOR PURPOSE: Phenotyping algorithms are fit for purpose when they identify the intended population with performance characteristics appropriate for the intended application. FITNESS FOR REUSE: Phenotyping algorithms are fit for reuse when the algorithm is implementable and generalizable-that is, it identifies the same intended population with similar performance characteristics when applied to a new setting. CONCLUSIONS: The PhenoFit framework provides a structured approach to evaluate and adapt phenotyping algorithms for new contexts increasing efficiency and consistency of identifying patient populations from EHRs. Laura K. Wiley, Luke V. Rasmussen, Rebecca T. Levinson, Jennifer Malinowski, Sheila Manemann, Melissa P. Wilson, Martin Chapman, Jennifer A. Pacheco, Theresa Walunas, Justin Starren, Suzette J. Bielinski, Rachel L. Richesson	J. Am. Medical Informatics Assoc.	2
2026	Ten quick tips for using the NIH Comparative Genomics Resource Eric S. Tvedte, Cecilia Arighi, Matthew B. Carson, Luke V. Rasmussen, Kristi L. Holmes, Terence D. Murphy	PLoS Comput. Biol.	4
2025	Large language models accurately identify immunosuppression in intensive care unit patients abstract OBJECTIVE: 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.	7
2023	Characterizing variability of electronic health record-driven phenotype definitions abstract OBJECTIVE: The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used. MATERIALS AND METHODS: A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries. RESULTS: Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27. DISCUSSION: Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints. CONCLUSIONS: The phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic. Pascal S. Brandt, Abel N. Kho, Yuan Luo 0001, Jennifer A. Pacheco, Theresa Walunas, Hakon Hakonarson, George Hripcsak, Cong Liu 0020, Ning Shang 0004, Chunhua Weng, Nephi Walton, David Carrell, Paul K. Crane, Eric B. Larson, Christopher G. Chute, Iftikhar J. Kullo, Robert J. Carroll, Joshua C. Denny, Andrea H. Ramirez, Wei-Qi Wei, Jyotishman Pathak, Laura K. Wiley, Rachel L. Richesson, Justin Starren, Luke V. Rasmussen	J. Am. Medical Informatics Assoc.	25
2023	AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease Chengsheng Mao, Jie Xu 0012, Luke V. Rasmussen, Yikuan Li, Prakash Adekkanattu, Jennifer A. Pacheco, Borna Bonakdarpour, Robert Vassar, Li Shen 0001, Guoqian Jiang, Fei Wang 0001, Jyotishman Pathak, Yuan Luo 0001	J. Biomed. Informatics	3
2022	Design and validation of a FHIR-based EHR-driven phenotyping toolbox abstract OBJECTIVES: To develop and validate a standards-based phenotyping tool to author electronic health record (EHR)-based phenotype definitions and demonstrate execution of the definitions against heterogeneous clinical research data platforms. MATERIALS AND METHODS: We developed an open-source, standards-compliant phenotyping tool known as the PhEMA Workbench that enables a phenotype representation using the Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) standards. We then demonstrated how this tool can be used to conduct EHR-based phenotyping, including phenotype authoring, execution, and validation. We validated the performance of the tool by executing a thrombotic event phenotype definition at 3 sites, Mayo Clinic (MC), Northwestern Medicine (NM), and Weill Cornell Medicine (WCM), and used manual review to determine precision and recall. RESULTS: An initial version of the PhEMA Workbench has been released, which supports phenotype authoring, execution, and publishing to a shared phenotype definition repository. The resulting thrombotic event phenotype definition consisted of 11 CQL statements, and 24 value sets containing a total of 834 codes. Technical validation showed satisfactory performance (both NM and MC had 100% precision and recall and WCM had a precision of 95% and a recall of 84%). CONCLUSIONS: We demonstrate that the PhEMA Workbench can facilitate EHR-driven phenotype definition, execution, and phenotype sharing in heterogeneous clinical research data environments. A phenotype definition that integrates with existing standards-compliant systems, and the use of a formal representation facilitates automation and can decrease potential for human error. Pascal S. Brandt, Jennifer A. Pacheco, Prakash Adekkanattu, Evan Sholle, Sajjad Abedian, Daniel J. Stone, David Knaack, Jie Xu 0012, Yifan Peng 0002, Natalie C. Benda, Fei Wang 0001, Yuan Luo 0001, Guoqian Jiang, Jyotishman Pathak, Luke V. Rasmussen	J. Am. Medical Informatics Assoc.	16
2021	Multi-site Evaluation of Longitudinal Changes in Ejection Fraction in Heart Failure Patients Through Data-driven Phenotyping Prakash Adekkanattu, Jennifer A. Pacheco, Joseph Kabariti, Daniel J. Stone, Yue Yu 0012, Parag Goyal, Faraz S. Ahmad, Guoqian Jiang, Yuan Luo 0001, Luke V. Rasmussen, Pascal S. Brandt, Jie Xu 0012, Fei Wang 0001, Natalie C. Benda, Thomas R. Campion Jr., Jyotishman Pathak	AMIA	10
2021	Supporting EHR-based Cohort Discovery Through User-centered Design: Results of an Early Formative Usability Study Natalie C. Benda, Pascal S. Brandt, Jessica S. Ancker, Jennifer A. Pacheco, Prakash Adekkanattu, Guoqian Jiang, Jyotishman Pathak, Luke V. Rasmussen	AMIA	8
2021	Impact of Sex and Gender Disparities on Computational Phenotyping: A Potential Barrier to an Equitable Learning Health System Rebecca T. Levinson, Jennifer R. Malinowski, Luke V. Rasmussen, Suzette J. Bielinski, Véronique L. Roger, Quinn Stanton Wells, Laura K. Wiley	AMIA	3
2021	A Deep Learning Framework Using a Pre-trained BERT Model to Predict the Risk of Progression from Mild Cognitive Impairment to Alzheimer's Disease Chengsheng Mao, Jie Xu 0012, Luke V. Rasmussen, Jennifer A. Pacheco, Guoqian Jiang, Fei Wang 0001, Richard Isaacson, Jyotishman Pathak, Yuan Luo 0001	AMIA	3
2021	Evaluation of the Portability of Natural Language Processing-based Computable Phenotypes in the eMERGE Network Jennifer A. Pacheco, Luke V. Rasmussen, Ken Wiley, Thomas N. Person, David J. Cronkite, Sunghwan Sohn, Shawn N. Murphy, Justin H. Gundelach, Vivian S. Gainer, Victor M. Castro, Cong Liu 0020, Todd Lingren, Frank D. Mentch, Agnes S. Sundaresan, Garrett Eickelberg, Valerie Willis, Al'ona Furmanchuk, Roshan Patel, David Carrell, Marc S. Williams, Elizabeth W. Karlson, Jodell E. Linder, Yuan Luo 0001, Chunhua Weng, Wei-Qi Wei	AMIA	2
2021	FHIRTime: Standardizing Temporal Patterns Identified from Clinical Narratives Using HL7 FHIR Daniel J. Stone, Sijia Liu 0002, Yuan Luo 0001, Andrew Wen, Nansu Zong, Luke V. Rasmussen, Prakash Adekkanattu, Pascal S. Brandt, Jennifer A. Pacheco, Fei Wang 0001, Cui Tao, Jyotishman Pathak, Guoqian Jiang	AMIA	6
2021	On Constraints and Considerations for Extending Support for Natural Language Processing-Based FHIR Resource Generation Andrew Wen, Luke V. Rasmussen, Daniel J. Stone, Sijia Liu 0002, Prakash Adekkanattu, Pascal S. Brandt, Jennifer A. Pacheco, Yuan Luo 0001, Fei Wang 0001, Jyotishman Pathak, Guoqian Jiang	AMIA	2
2021	Genomic considerations for FHIR®; eMERGE implementation lessons Mullai Murugan, Lawrence J. Babb, Casey Overby Taylor, Luke V. Rasmussen, Robert R. Freimuth, Eric Venner, Victoria Yi, Stephen Granite, Hana Zouk, Samuel J. Aronson, Kevin Power, Alexander Fedotov, David R. Crosslin, David Fasel, Gail P. Jarvik, Hakon Hakonarson, Hana Bangash, Iftikhar J. Kullo, John J. Connolly, Jordan G. Nestor, Pedro J. Caraballo, Wei-Qi Wei, Ken Wiley, Heidi L. Rehm, Richard A. Gibbs	J. Biomed. Informatics	4
2020	Feasibility of Cross-Platform EHR-Driven Phenotyping Using Clinical Quality Language Pascal S. Brandt, Richard C. Kiefer, Jennifer A. Pacheco, Prakash Adekkanattu, Evan Sholle, Faraz S. Ahmad, Jie Xu 0012, Jessica S. Ancker, Fei Wang 0001, Yuan Luo 0001, Guoqian Jiang, Jyotishman Pathak, Luke V. Rasmussen	AMIA	14
2020	Identification of Alzheimer's Disease Subtypes from Electronic Health Records Using a Data-Driven Approach Jie Xu 0012, Fei Wang 0001, Prakash Adekkanattu, Pascal S. Brandt, Guoqian Jiang, Richard C. Kiefer, Yuan Luo 0001, Chengsheng Mao, Jennifer A. Pacheco, Luke V. Rasmussen, Yiye Zhang, Richard Isaacson, Jyotishman Pathak	AMIA	11
2020	Identifying sub-phenotypes of acute kidney injury using structured and unstructured electronic health record data with memory networks Jingyuan Chou, Xi Sheryl Zhang, Yuan Luo 0001, Tamara Isakova, Prakash Adekkanattu, Jessica S. Ancker, Guoqian Jiang, Richard C. Kiefer, Jennifer A. Pacheco, Luke V. Rasmussen, Jyotishman Pathak, Fei Wang 0001	J. Biomed. Informatics	11
2019	Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study Prakash Adekkanattu, Guoqian Jiang, Yuan Luo 0001, Paul R. Kingsbury, Luke V. Rasmussen, Jennifer A. Pacheco, Richard C. Kiefer, Daniel J. Stone, Pascal S. Brandt, Yizhen Zhong, Fei Wang 0001, Jessica S. Ancker, Thomas R. Campion Jr., Jyotishman Pathak	AMIA	6
2019	Development of a Genomic Data Flow Framework: Results of a Survey Administered to NIH-NHGRI IGNITE and eMERGE Consortia Participants Paul Richard Dexter, Henry H. Ong, Amanda Elsey, Gillian Bell, Nephi Walton, Wendy K. Chung, Luke V. Rasmussen, J. Kevin Hicks, Aniwaa Owusu-obeng, Stuart A. Scott, Stephen B. Ellis, Josh F. Peterson	AMIA	7
2019	Considerations for Improving the Portability of Electronic Health Record-Based Phenotype Algorithms Luke V. Rasmussen, Pascal S. Brandt, Guoqian Jiang, Richard C. Kiefer, Jennifer A. Pacheco, Prakash Adekkanattu, Jessica S. Ancker, Fei Wang 0001, Jyotishman Pathak, Yuan Luo 0001	AMIA	1
2019	Pharmacogenomic clinical decision support design and multi-site process outcomes analysis in the eMERGE Network abstract To better understand the real-world effects of pharmacogenomic (PGx) alerts, this study aimed to characterize alert design within the eMERGE Network, and to establish a method for sharing PGx alert response data for aggregate analysis. Seven eMERGE sites submitted design details and established an alert logging data dictionary. Six sites participated in a pilot study, sharing alert response data from their electronic health record systems. PGx alert design varied, with some consensus around the use of active, post-test alerts to convey Clinical Pharmacogenetics Implementation Consortium recommendations. Sites successfully shared response data, with wide variation in acceptance and follow rates. Results reflect the lack of standardization in PGx alert design. Standards and/or larger studies will be necessary to fully understand PGx impact. This study demonstrated a method for sharing PGx alert response data and established that variation in system design is a significant barrier for multi-site analyses. Timothy M. Herr, Josh F. Peterson, Luke V. Rasmussen, Pedro J. Caraballo, Peggy L. Peissig, Justin Starren	J. Am. Medical Informatics Assoc.	3
2019	An ancillary genomics system to support the return of pharmacogenomic results abstract Existing approaches to managing genetic and genomic test results from external laboratories typically include filing of text reports within the electronic health record, making them unavailable in many cases for clinical decision support. Even when structured computable results are available, the lack of adopted standards requires considerations for processing the results into actionable knowledge, in addition to storage and management of the data. Here, we describe the design and implementation of an ancillary genomics system used to receive and process heterogeneous results from external laboratories, which returns a descriptive phenotype to the electronic health record in support of pharmacogenetic clinical decision support. Luke V. Rasmussen, Maureen E. Smith, Federico Almaraz, Stephen D. Persell, Laura Rasmussen-Torvik, Jennifer A. Pacheco, Rex L. Chisholm, Carl Christensen, Timothy M. Herr, Firas H. Wehbe, Justin Starren	J. Am. Medical Informatics Assoc.	1
2019	Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries Na Hong, Andrew Wen, Daniel J. Stone, Shintaro Tsuji, Paul R. Kingsbury, Luke V. Rasmussen, Jennifer A. Pacheco, Prakash Adekkanattu, Fei Wang 0001, Yuan Luo 0001, Jyotishman Pathak, Guoqian Jiang	J. Biomed. Informatics	6
2019	Facilitating phenotype transfer using a common data model George Hripcsak, Ning Shang 0004, Peggy L. Peissig, Luke V. Rasmussen, Cong Liu 0020, Barbara Benoit, Robert J. Carroll, David Carrell, Joshua C. Denny, Ozan Dikilitas, Vivian S. Gainer, Kayla Marie Howell, Jeffrey G. Klann, Iftikhar J. Kullo, Todd Lingren, Frank D. Mentch, Shawn N. Murphy, Karthik Natarajan, Chunhua Weng	J. Biomed. Informatics	4
2019	Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network Ning Shang 0004, Cong Liu 0020, Luke V. Rasmussen, Casey N. Ta, Robert J. Carroll, Barbara Benoit, Todd Lingren, Ozan Dikilitas, Frank D. Mentch, David Carrell, Wei-Qi Wei, Yuan Luo 0001, Vivian S. Gainer, Iftikhar J. Kullo, Jennifer A. Pacheco, Hakon Hakonarson, Theresa Walunas, Joshua C. Denny, Chunhua Weng	J. Biomed. Informatics	3
2018	Characterizing Design Patterns of EHR-Driven Phenotype Extraction Algorithms Yizhen Zhong, Luke V. Rasmussen, Jennifer A. Pacheco, Maureen E. Smith, Justin Starren, Wei-Qi Wei, Peter Speltz, Joshua C. Denny, Nephi Walton, George Hripcsak, Christopher G. Chute, Yuan Luo 0001	BIBM	2
2018	Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example abstract The eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians. Samuel J. Aronson, Lawrence J. Babb, Darren C. Ames, Richard A. Gibbs, Eric Venner, John J. Connelly, Keith Marsolo, Chunhua Weng, Marc S. Williams, Andrea L. Hartzler, Wayne H. Liang, James D. Ralston, Emily Beth Devine, Shawn N. Murphy, Christopher G. Chute, Pedro J. Caraballo, Iftikhar J. Kullo, Robert R. Freimuth, Luke V. Rasmussen, Firas H. Wehbe, Josh F. Peterson, Jamie R. Robinson, Ken Wiley, Casey Overby Taylor	J. Am. Medical Informatics Assoc.	19
2018	A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments abstract Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges. Jennifer A. Pacheco, Luke V. Rasmussen, Richard C. Kiefer, Thomas R. Campion Jr., Peter Speltz, Robert J. Carroll, Sarah C. Stallings, Huan Mo, Monika Ahuja, Guoqian Jiang, Eric LaRose, Peggy L. Peissig, Ning Shang 0004, Barbara Benoit, Vivian S. Gainer, Kenneth Borthwick, Kathryn L. Jackson, Ambrish Sharma, Andy Yizhou Wu, Abel N. Kho, Dan M. Roden, Jyotishman Pathak, Joshua C. Denny, William K. Thompson	J. Am. Medical Informatics Assoc.	2
2017	Challenges impacting data collection from Electronic Health Record (EHR) Systems in Small and Medium sized practices in the Midwest Pauline Kenly, Luke V. Rasmussen, Andrew Schriever, Faraz S. Ahmad, Kathryn L. Jackson, Richard Chagnon, Theresa Walunas, Abel N. Kho	AMIA	2
2017	Leveraging Value Sets from the Value Set Authority Center (VSAC) in a Standards-Based Clinical Data Repository Richard C. Kiefer, Luke V. Rasmussen, Jennifer A. Pacheco, Peter Speltz, Joshua C. Denny, William K. Thompson, Jyotishman Pathak, Guoqian Jiang	AMIA	2
2017	Design and Implementation of a Structured Sequencing Report Format: A Multi-Stakeholder Perspective from eMERGE Luke V. Rasmussen, Darren C. Ames, Samuel J. Aronson, Lawrence J. Babb, Casey Overby Taylor	AMIA	1
2017	DocUBuild: A Collaborative System to Enhance Dissemination and Discovery of Genomic Clinical Content Luke V. Rasmussen, Casey Overby Taylor	AMIA	1
2017	Use of the popHealth Open-Source Quality Measure Engine to Support Cardiovascular Care at Small- and Medium-Sized Practices Luke V. Rasmussen, Andrew Schriever, Pauline Kenly, Dejan Jovanov, Stephen D. Persell, Theresa Walunas, Abel N. Kho	AMIA	1
2017	The Phenotype Execution and Modeling Architecture: A Roadmap Towards Next-generation Phenotyping Using EHRs Peter Speltz, Luke V. Rasmussen, Richard C. Kiefer, Jennifer A. Pacheco, William K. Thompson, Guoqian Jiang, Jyotishman Pathak, Joshua C. Denny	AMIA	2
2017	Classifying Clinical Trial Eligibility Criteria to Facilitate Phased Cohort Identification Using Clinical Data Repositories Amy Y. Wang, William J. Lancaster, Matthew C. Wyatt, Luke V. Rasmussen, Daniel Fort, James J. Cimino	AMIA	4
2016	Implementing Pharmacogenomic Clinical Decision Support: Design and Prescriber Response in the eMERGE Network Timothy M. Herr, Josh F. Peterson, Luke V. Rasmussen, Pedro J. Caraballo	AMIA	3
2016	An NLP Extension to the Quality Data Model for EHR-Driven Phenotype Algorithm Authoring and Execution Guoqian Jiang, William K. Thompson, Luke V. Rasmussen, Richard C. Kiefer, Jennifer A. Pacheco, Huan Mo, Peter Speltz, Joshua C. Denny, Jyotishman Pathak	AMIA	3
2016	Design and Implementation of an Ancillary Genomics System for the Return of Pharmacogenetic Results Luke V. Rasmussen, Maureen E. Smith, Federico Almaraz, Stephen D. Persell, Laura Rasmussen-Torvik, Jennifer A. Pacheco, Carl Christensen, Timothy M. Herr, Firas H. Wehbe, Justin Starren	AMIA	1
2016	Automatic identification and extraction of design patterns of EHR-driven phenotyping algorithms Yizhen Zhong, Luke V. Rasmussen, Justin Starren, Yuan Luo 0001	AMIA	2
2016	A multi-institution evaluation of clinical profile anonymization abstract BACKGROUND AND OBJECTIVE: There is an increasing desire to share de-identified electronic health records (EHRs) for secondary uses, but there are concerns that clinical terms can be exploited to compromise patient identities. Anonymization algorithms mitigate such threats while enabling novel discoveries, but their evaluation has been limited to single institutions. Here, we study how an existing clinical profile anonymization fares at multiple medical centers. METHODS: We apply a state-of-the-artk-anonymization algorithm, withkset to the standard value 5, to the International Classification of Disease, ninth edition codes for patients in a hypothyroidism association study at three medical centers: Marshfield Clinic, Northwestern University, and Vanderbilt University. We assess utility when anonymizing at three population levels: all patients in 1) the EHR system; 2) the biorepository; and 3) a hypothyroidism study. We evaluate utility using 1) changes to the number included in the dataset, 2) number of codes included, and 3) regions generalization and suppression were required. RESULTS: Our findings yield several notable results. First, we show that anonymizing in the context of the entire EHR yields a significantly greater quantity of data by reducing the amount of generalized regions from ∼15% to ∼0.5%. Second, ∼70% of codes that needed generalization only generalized two or three codes in the largest anonymization. CONCLUSIONS: Sharing large volumes of clinical data in support of phenome-wide association studies is possible while safeguarding privacy to the underlying individuals. Raymond Heatherly, Luke V. Rasmussen, Peggy L. Peissig, Jennifer A. Pacheco, Paul A. Harris, Joshua C. Denny, Bradley A. Malin	J. Am. Medical Informatics Assoc.	2
2016	PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability abstract OBJECTIVE: Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems.Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites. RESULTS: As of June 2015, PheKB contained 30 finalized phenotype algorithms and 62 algorithms in development spanning a range of traits and diseases. Phenotypes have had over 3500 unique views in a 6-month period and have been reused by other institutions. International Classification of Disease codes were the most frequently used component, followed by medications and natural language processing. Among algorithms with published performance data, the median PPV was nearly identical when evaluated at the authoring institutions (n = 44; case 96.0%, control 100%) compared to implementation sites (n = 40; case 97.5%, control 100%). DISCUSSION: These results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others. CONCLUSION: By providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data. Jacqueline Kirby, Peter Speltz, Luke V. Rasmussen, Melissa A. Basford, Omri Gottesman, Peggy L. Peissig, Jennifer A. Pacheco, Gerard Tromp, Jyotishman Pathak, David Carrell, Stephen B. Ellis, Todd Lingren, William K. Thompson, Guergana K. Savova, Jonathan L. Haines, Dan M. Roden, Paul A. Harris, Joshua C. Denny	J. Am. Medical Informatics Assoc.	3
2016	The genomic CDS sandbox: An assessment among domain experts Ayesha Aziz, Kensaku Kawamoto, Karen Eilbeck, Marc S. Williams, Robert R. Freimuth, Mark A. Hoffman, Luke V. Rasmussen, Casey Overby Taylor, Brian H. Shirts, James M. Hoffman, Brandon M. Welch	J. Biomed. Informatics	7
2016	Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution Guoqian Jiang, Richard C. Kiefer, Luke V. Rasmussen, Harold R. Solbrig, Huan Mo, Jennifer A. Pacheco, Jie Xu 0011, Enid N. H. Montague, William K. Thompson, Joshua C. Denny, Christopher G. Chute, Jyotishman Pathak	J. Biomed. Informatics	3
2015	Harmonization of Quality Data Model with HL7 FHIR to Support EHR-driven Phenotype Authoring and Execution: A Pilot Study Guoqian Jiang, Harold R. Solbrig, Richard C. Kiefer, Luke V. Rasmussen, Huan Mo, Jennifer A. Pacheco, Enid N. H. Montague, Jie Xu 0011, Peter Speltz, William K. Thompson, Joshua C. Denny, Christopher G. Chute, Jyotishman Pathak	AMIA	4
2015	Translating Electronic Clinical Quality Measures to Executable, Portable, and Customizable Workflows in KNIME Huan Mo, Jennifer A. Pacheco, Richard C. Kiefer, Luke V. Rasmussen, Jyotishman Pathak, Joshua C. Denny, William K. Thompson	AMIA	4
2015	Usability of a phenotype builder prototype and lessons learned for the design of phenotyping tools Enid N. H. Montague, Jie Xu 0011, Luke V. Rasmussen, Joshua C. Denny, Guoqian Jiang, Richard C. Kiefer, Jennifer A. Pacheco, Peter Speltz, William K. Thompson, Jyotishman Pathak	AMIA	3
2015	PhEMA: Phenotype Modeling, Sharing and Execution Architecture Jyotishman Pathak, Joshua C. Denny, William K. Thompson, Luke V. Rasmussen	AMIA	4
2015	Desiderata for computable representations of electronic health records-driven phenotype algorithms abstract BACKGROUND: Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). METHODS: A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. RESULTS: We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. CONCLUSION: A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages. Huan Mo, William K. Thompson, Luke V. Rasmussen, Jennifer A. Pacheco, Guoqian Jiang, Richard C. Kiefer, Qian Zhu 0003, Jie Xu 0011, Enid N. H. Montague, David Carrell, Todd Lingren, Frank D. Mentch, Yizhao Ni, Firas H. Wehbe, Peggy L. Peissig, Gerard Tromp, Eric B. Larson, Christopher G. Chute, Jyotishman Pathak, Joshua C. Denny, Peter Speltz, Abel N. Kho, Gail P. Jarvik, Cosmin Adrian Bejan, Marc S. Williams, Kenneth Borthwick, Terrie E. Kitchner, Dan M. Roden, Paul A. Harris	J. Am. Medical Informatics Assoc.	3
2015	CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record abstract OBJECTIVE: Clinicians' ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS). MATERIALS AND METHODS: The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement. RESULTS: There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information. CONCLUSION: Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended. Brian H. Shirts, Joseph S. Salama, Samuel J. Aronson, Wendy K. Chung, Stacy W. Gray, Lucia Hindorff, Gail P. Jarvik, Sharon E. Plon, Elena M. Stoffel, Peter Tarczy-Hornoch, Eliezer M. Van Allen, Karen E. Weck, Christopher G. Chute, Robert R. Freimuth, Robert Grundmeier, Andrea L. Hartzler, Rongling Li, Peggy L. Peissig, Josh F. Peterson, Luke V. Rasmussen, Justin Starren, Marc S. Williams, Casey Overby Taylor	J. Am. Medical Informatics Assoc.	20
2015	Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research abstract OBJECTIVE: To review and evaluate available software tools for electronic health record-driven phenotype authoring in order to identify gaps and needs for future development. MATERIALS AND METHODS: Candidate phenotype authoring tools were identified through (1) literature search in four publication databases (PubMed, Embase, Web of Science, and Scopus) and (2) a web search. A collection of tools was compiled and reviewed after the searches. A survey was designed and distributed to the developers of the reviewed tools to discover their functionalities and features. RESULTS: Twenty-four different phenotype authoring tools were identified and reviewed. Developers of 16 of these identified tools completed the evaluation survey (67% response rate). The surveyed tools showed commonalities but also varied in their capabilities in algorithm representation, logic functions, data support and software extensibility, search functions, user interface, and data outputs. DISCUSSION: Positive trends identified in the evaluation included: algorithms can be represented in both computable and human readable formats; and most tools offer a web interface for easy access. However, issues were also identified: many tools were lacking advanced logic functions for authoring complex algorithms; the ability to construct queries that leveraged un-structured data was not widely implemented; and many tools had limited support for plug-ins or external analytic software. CONCLUSIONS: Existing phenotype authoring tools could enable clinical researchers to work with electronic health record data more efficiently, but gaps still exist in terms of the functionalities of such tools. The present work can serve as a reference point for the future development of similar tools. Jie Xu 0011, Luke V. Rasmussen, Pamela L. Shaw, Guoqian Jiang, Richard C. Kiefer, Huan Mo, Jennifer A. Pacheco, Peter Speltz, Qian Zhu 0003, Joshua C. Denny, Jyotishman Pathak, William K. Thompson, Enid N. H. Montague	J. Am. Medical Informatics Assoc.	2
2014	What Is Asked in Clinical Data Request Forms? A Multi-site Thematic Analysis of Forms Towards Better Data Access Support David A. Hanauer, Gregory William Hruby, Daniel Fort, Luke V. Rasmussen, Eneida A. Mendonça, Chunhua Weng	AMIA	4
2014	A Template for Authoring and Adapting Genomic Medicine Content in the eMERGE Infobutton Project Casey Overby Taylor, Luke V. Rasmussen, Andrea L. Hartzler, John J. Connolly, Josh F. Peterson, RoseMary Hedberg, Robert R. Freimuth, Brian H. Shirts, Joshua C. Denny, Eric B. Larson, Christopher G. Chute, Gail P. Jarvik, James D. Ralston, Alan R. Shuldiner, Iftikhar J. Kullo, Peter Tarczy-Hornoch, Marc S. Williams	AMIA	2
2014	Evaluation of Existing Phenotype Authoring Tools for Clinical Research Luke V. Rasmussen, Jie Xu 0011, Ruijue Liu, Qian Zhu 0003, Jennifer A. Pacheco, Jyotishman Pathak, William K. Thompson, Joshua C. Denny, Huan Mo, Richard C. Kiefer, Peter Speltz, Enid N. H. Montague	AMIA	1
2014	Reproducibility of Health Care Datasets Daniel H. Schneider, Nathan D. Sisterson, Luke V. Rasmussen	AMIA	3
2014	Qualitative evaluation of three phenotype information models to find methotrexate liver injury Qian Zhu 0003, Huan Mo, Luke V. Rasmussen, Andrew R. Post, Jennifer A. Pacheco, Jie Xu 0011, Richard C. Kiefer, Peter Speltz, Enid N. H. Montague, William K. Thompson, Joshua C. Denny, Jyotishman Pathak	AMIA	3
2014	Design patterns for the development of electronic health record-driven phenotype extraction algorithms Luke V. Rasmussen, William K. Thompson, Jennifer A. Pacheco, Abel N. Kho, David Carrell, Jyotishman Pathak, Peggy L. Peissig, Gerard Tromp, Joshua C. Denny, Justin Starren	J. Biomed. Informatics	1
2012	Rethinking the "Honest Broker" in the Changing Face of Security and Privacy Luke V. Rasmussen, Brian D. Athey, Andrew D. Boyd, Bradley A. Malin, Shawn N. Murphy	AMIA	1
2012	Integrating Research Recruitment into a Clinical Patient Portal Luke V. Rasmussen, David Were, Jeff Lunt, Steve Lee, Carl Christensen, Warren A. Kibbe, Justin Starren	AMIA	1
2012	Grouping and Translating Value Sets Emre Motan, Luke V. Rasmussen, Andrew Winter, Justin Starren	AMIA	3
2012	An Evaluation of the NQF Quality Data Model for Representing Electronic Health Record Driven Phenotyping Algorithms William K. Thompson, Luke V. Rasmussen, Jennifer A. Pacheco, Peggy L. Peissig, Joshua C. Denny, Abel N. Kho, Aaron W. Miller, Jyotishman Pathak	AMIA	2
2012	Open Source Workflow Tools for Electronic Health Record Based Phenotyping Algorithms William K. Thompson, Luke V. Rasmussen, Jennifer A. Pacheco, Anna Roberts, Arun Muthalagu, Abel N. Kho	AMIA	2
2012	Importance of multi-modal approaches to effectively identify cataract cases from electronic health records abstract OBJECTIVE: There is increasing interest in using electronic health records (EHRs) to identify subjects for genomic association studies, due in part to the availability of large amounts of clinical data and the expected cost efficiencies of subject identification. We describe the construction and validation of an EHR-based algorithm to identify subjects with age-related cataracts. MATERIALS AND METHODS: We used a multi-modal strategy consisting of structured database querying, natural language processing on free-text documents, and optical character recognition on scanned clinical images to identify cataract subjects and related cataract attributes. Extensive validation on 3657 subjects compared the multi-modal results to manual chart review. The algorithm was also implemented at participating electronic MEdical Records and GEnomics (eMERGE) institutions. RESULTS: An EHR-based cataract phenotyping algorithm was successfully developed and validated, resulting in positive predictive values (PPVs) >95%. The multi-modal approach increased the identification of cataract subject attributes by a factor of three compared to single-mode approaches while maintaining high PPV. Components of the cataract algorithm were successfully deployed at three other institutions with similar accuracy. DISCUSSION: A multi-modal strategy incorporating optical character recognition and natural language processing may increase the number of cases identified while maintaining similar PPVs. Such algorithms, however, require that the needed information be embedded within clinical documents. CONCLUSION: We have demonstrated that algorithms to identify and characterize cataracts can be developed utilizing data collected via the EHR. These algorithms provide a high level of accuracy even when implemented across multiple EHRs and institutional boundaries. Peggy L. Peissig, Luke V. Rasmussen, Richard L. Berg, James G. Linneman, Catherine A. McCarty, Carol Waudby, Joshua C. Denny, Russell A. Wilke, Jyotishman Pathak, David Carrell, Abel N. Kho, Justin Starren	J. Am. Medical Informatics Assoc.	2
2012	Development of an optical character recognition pipeline for handwritten form fields from an electronic health record abstract BACKGROUND: Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms. METHODS: We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms. OBSERVATIONS: The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%. DISCUSSION: While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline. Luke V. Rasmussen, Peggy L. Peissig, Catherine A. McCarty, Justin Starren	J. Am. Medical Informatics Assoc.	1