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Articles by Sunyang Fu
A multi-site benchmarking framework for scalable extraction of geriatric care constructs from electronic health records
Abstract Current Natural Language Processing (NLP) algorithms for detecting geriatric conditions are largely limited to domain-specific models that fail to capture the interdependent, multidimensional nature of comprehensive geriatric assessment.
MedADL: High-Throughput Information Extraction of Functional Status From Electronic Health Records to Advance Frailty Assessment in Older Adults
Faculty, Staff and Student Publications Publication Date 7-1-2026 Journal The Journals of Gerontology: Series A DOI 10.1093/gerona/glag154 PubMedCentral® Posted Date 8-13-2026 PubMedCentral® Full Text Version Author MSS Abstract Background: Functional status is essential for assessing frailty and planning care in older adults but is often under-documented in the structured fields of electronic health records.
A machine-assisted framework for systematic error analysis in clinical concept extraction
Abstract Error analysis is critical for evaluating and improving clinical natural language processing (NLP) models, yet no standardized framework exists to support systematic error analysis for clinical concept extraction. Here we present MedError, a machine-assisted, human-in-the-loop framework integrating a validated error taxonomy, large language model (LLM)-assisted classification, and a structured annotation workflow.
A landscape analysis of access, equity, and reporting trends in digital health randomized controlled trials
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Abstract The merger of technology and healthcare has given rise to a new era of evidence-based medicine. As global regulatory efforts in digital health expand, important questions emerge about who participates in clinical trial evaluations and how findings about digital interventions are documented.
By Taylor Harrison Verified, Dian Xing Hu, Fang Chen, Qiuhao Lu, Heling Jia, Jennifer St. Sauver, Sunyang Fu, Hongfang Liu
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Nature
Verified
PathInteract: a video analysis suite for capturing and analyzing pathology slide reviewing interactions
Abstract Digital pathology has transformed how pathologists review and interpret tissue specimens, enabling remote access, efficient storage, and advanced visualization. Systematically analyzing pathologists’ slide reviewing interactions can uncover opportunities to design AI-assisted tools that integrate seamlessly into routine practice. We present a proof-of-concept suite, PathInteract, for extracting pathology interaction signals from recorded slide-review videos.
A dynamic risk prediction framework for Alzheimer's disease and related dementias with interpretability
Abstract Alzheimer’s disease and related dementias (ADRD) develop years before symptoms emerge, making early prediction critical. Electronic health records (EHR) offer a scalable alternative to neuroimaging but are challenged by irregular encounters, data sparsity, and limited interpretability. We propose Gated Recurrent Unit with Decay & Attention (GRU-DA), which integrates GRU-D missingness modeling with RETAIN-style attention for interpretable risk monitoring.
Gated recurrent unit with decay has real-time capability for postoperative ileus surveillance and offers cross-hospital transferability - Communications Medicine
Abstract Ileus, a postoperative complication after colorectal surgery, increases morbidity, costs, and hospital stays. Assessing risk of ileus is crucial, especially with the trend towards early discharge. Prior studies assessed risk of ileus with regression models, the role of deep learning remains unexplored.
Social determinants of health extraction from clinical notes across institutions using large language models - npj Digital Medicine
Abstract Detailed social determinants of health (SDoH) is often buried within clinical text in EHRs. Most current NLP efforts for SDoH have limitations, investigating limited factors, deriving data from a single institution, using specific patient cohorts/note types, with reduced focus on generalizability.
Hybrid and Federated Framework for Functional Status Ascertain" by Sunyang Fu, Heling Jia et al.
Abstract Introduction: Patients' functional status assesses their independence in performing activities of daily living, including basic ADLs (bADL), and more complex instrumental activities (iADL). Existing studies have discovered that patients' functional status is a strong predictor of health outcomes, particularly in older adults. Depite their usefulness, much of the functional status information is stored in electronic health records (EHRs) in either semi-structured or free text formats.
Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing
Abstract Purpose: The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice.
The ENACT Network Is Acting on Housing Instability and the Unhoused Using the Open Health Natural Language Processing Toolkit
Abstract Housing is an environmental social determinant of health that is linked to mortality and clinical outcomes. We developed a lexicon of housing-related concepts and rule-based natural language processing methods for identifying these housing-related concepts within clinical text.
Characterizing the Progression From Mild Cognitive Impairment to Dementia: A Network Analysis of Longitudinal Clinical Visits
Background: With the recent surge in the utilization of electronic health records for cognitive decline, the research community has turned its attention to conducting fine-grained analyses of dementia onset using advanced techniques. Previous works have mostly focused on machine learning-based prediction of dementia, lacking the analysis of dementia progression and its associations with risk factors over time.
A Comparison of Patient Self‐Reported Fatigue in the FRAIL Scale With a Validated Fatigue Measure
Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request.
A generalist vision–language foundation model for diverse biomedical tasks - Nature Medicine
Abstract Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs.
Characterizing Performance Gaps of a Code-Based Dementia Algorithm in a Population-Based Cohort of Cognitive Aging
BACKGROUND: Multiple algorithms with variable performance have been developed to identify dementia using combinations of billing codes and medication data that are widely available from electronic health records (EHR). If the characteristics of misclassified patients are clearly identified, modifying existing algorithms to improve performance may be possible.
"Considerations for Quality Control Monitoring of Machine Learning Mode" by Louis Faust, Patrick Wilson et al.
Abstract Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions.
Contextual Variation of Clinical Notes induced by EHR Migration
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The Impact Framework and Implementation for Accessible in Silico Clinical Phenotyping in the Digital Era
Abstract Clinical phenotyping is often a foundational requirement for obtaining datasets necessary for the development of digital health applications. Traditionally done via manual abstraction, this task is often a bottleneck in development due to time and cost requirements, therefore raising significant interest in accomplishing this task via in-silico means.
"Dementia Prediction in Older Adults Using Sex-Specific Health Trajecto" by Omar A Ibrahim, Muskan Garg et al.
Publication Date 1-25-2024 Journal Studies in Health Technology and Informatics Abstract With increasing number of people living with dementia, the problem of late diagnosis significantly impacts a person's quality of life while early signs of dementia may provide useful insights to facilitate better treatment plans. With time, this progressive neurodegenerative syndrome could progress from mild cognitive impairment to dementia.
Digital Solutions Observed in Clinical Trials: A Formative Feasibility Scoping Review
Abstract Growing digital access accelerates digital transformation of clinical trials where digital solutions (DSs) are increasingly and widely leveraged for improving trial efficiency, effectiveness, and accessibility. Many factors impact DS success including technology barriers, privacy concerns, or user engagement activities. It is unclear how those factors are considered or reported in the literature.
Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D's Real-Time Capabilities and Cross-Hospital Transferability
The authors have declared no competing interest. This work was funded by National Institute of Biomedical Imaging and Bioengineering grant R01 EB019403 I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
A framework for human evaluation of large language models in healthcare derived from literature review - npj Digital Medicine
Abstract With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness.
Assessing document section heterogeneity across multiple electronic health record systems for computational phenotyping: A case study of heart-failure phenotyping algorithm (pdf) | Paperity
PLOS ONE, Mar 2023 This is a preview of a remote PDF: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0283800&type=printable Sungrim Moon, Sijia Liu, Bhavani Singh Agnikula Kshatriya, Sunyang Fu, Ethan D. Moser, Suzette J. Bielinski, Jungwei Fan, Hongfang Liu. , PLOS ONE, 2023, Volume 18, Issue 3, DOI: 10.1371/journal.pone.0283800
Assessing document section heterogeneity across multiple electronic health record systems for computational phenotyping: A case study of heart-failure phenotyping algorithm
Algorithm for the section classification A total of seven classifiers were developed (as shown in Fig 2). For example, in the following text segment from the problem section, “DIAGNOSIS: 1. Congestive Heart Failure. 2. Chronic Systolic (Congestive) Heart. 3. Anemia.”, Seg consists of three sentences, {“1. Congestive Heart Failure,” “2. Chronic Systolic (Congestive) Heart,” “3. Anemia”}.
Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing
Charting the Relevant Studies A standardized charting form was established to synthetize relevant publications. The information of interest can be categorized into four main sections: metadata, EHR data and targeted cancer types, oncology data elements and standards, and NLP methodology.
Automated Detection of Periprosthetic Joint Infections and Data Elements Using Natural Language Processing
Periprosthetic joint infection (PJI) data elements are contained in both structured and unstructured documents in electronic health records and require manual data collection. The goal of this study is to develop a natural language processing (NLP) algorithm to replicate manual chart review for PJI data elements. PJI was identified among all total joint arthroplasty (TJA) procedures performed at a single academic institution between 2000 and 2017.
Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation
Abstract Data is foundational to high-quality artificial intelligence (AI). Given that a substantial amount of clinically relevant information is embedded in unstructured data, natural language processing (NLP) plays an essential role in extracting valuable information that can benefit decision making, administration reporting, and research.
Natural Language Processing for the Identification of Silent Brain Infarcts From Neuroimaging Reports
Citation Please cite as: Loading authors... Download: Published on 21.04.19 in Vol 7, No 2 (2019): Apr-Jun Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/12109, first published Sep 05, 2018.
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