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Article ; Online: Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR.

Xu, Ran / Yu, Yue / Zhang, Chao / Ali, Mohammed K / Ho, Joyce C / Yang, Carl

Proceedings of machine learning research

2023 Volume 193, Page(s) 259–278

Abstract: Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a ...

Abstract	Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide
Language	English
Publishing date	2023-05-31
Publishing country	United States
Document type	Journal Article
ISSN	2640-3498
ISSN (online)	2640-3498
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Evaluating Natural Language Processing Packages for Predicting Hospital-Acquired Pressure Injuries From Clinical Notes.

Gu, Siyi / Lee, Eric W / Zhang, Wenhui / Simpson, Roy L / Hertzberg, Vicki Stover / Ho, Joyce C

Computers, informatics, nursing : CIN

2024 Volume 42, Issue 3, Page(s) 184–192

Abstract: Incidence of hospital-acquired pressure injury, a key indicator of nursing quality, is directly proportional to adverse outcomes, increased hospital stays, and economic burdens on patients, caregivers, and society. Thus, predicting hospital-acquired ... ...

Abstract	Incidence of hospital-acquired pressure injury, a key indicator of nursing quality, is directly proportional to adverse outcomes, increased hospital stays, and economic burdens on patients, caregivers, and society. Thus, predicting hospital-acquired pressure injury is important. Prediction models use structured data more often than unstructured notes, although the latter often contain useful patient information. We hypothesize that unstructured notes, such as nursing notes, can predict hospital-acquired pressure injury. We evaluate the impact of using various natural language processing packages to identify salient patient information from unstructured text. We use named entity recognition to identify keywords, which comprise the feature space of our classifier for hospital-acquired pressure injury prediction. We compare scispaCy and Stanza, two different named entity recognition models, using unstructured notes in Medical Information Mart for Intensive Care III, a publicly available ICU data set. To assess the impact of vocabulary size reduction, we compare the use of all clinical notes with only nursing notes. Our results suggest that named entity recognition extraction using nursing notes can yield accurate models. Moreover, the extracted keywords play a significant role in the prediction of hospital-acquired pressure injury.
MeSH term(s)	Humans ; Natural Language Processing ; Pressure Ulcer/diagnosis ; Critical Care ; Hospitals
Language	English
Publishing date	2024-03-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	2078463-6
ISSN	1538-9774 ; 1538-2931
ISSN (online)	1538-9774
ISSN	1538-2931
DOI	10.1097/CIN.0000000000001053
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Article ; Online: An AdaBoost-based algorithm to detect hospital-acquired pressure injury in the presence of conflicting annotations.

Ho, Joyce C / Sotoodeh, Mani / Zhang, Wenhui / Simpson, Roy L / Hertzberg, Vicki Stover

Computers in biology and medicine

2023 Volume 168, Page(s) 107754

Abstract: Hospital-acquired pressure injury is one of the most harmful events in clinical settings. Patients who do not receive early prevention and treatment can experience a significant financial burden and physical trauma. Several hospital-acquired pressure ... ...

Abstract	Hospital-acquired pressure injury is one of the most harmful events in clinical settings. Patients who do not receive early prevention and treatment can experience a significant financial burden and physical trauma. Several hospital-acquired pressure injury prediction algorithms have been developed to tackle this problem, but these models assume a consensus, gold-standard label (i.e., presence of pressure injury or not) is present for all training data. Existing definitions for identifying hospital-acquired pressure injuries are inconsistent due to the lack of high-quality documentation surrounding pressure injuries. To address this issue, we propose in this paper an ensemble-based algorithm that leverages truth inference methods to resolve label inconsistencies between various case definitions and the level of disagreements in annotations. Application of our method to MIMIC-III, a publicly available intensive care unit dataset, gives empirical results that illustrate the promise of learning a prediction model using truth inference-based labels and observed conflict among annotators.
MeSH term(s)	Humans ; Pressure Ulcer/diagnosis ; Algorithms ; Intensive Care Units ; Hospitals
Language	English
Publishing date	2023-11-22
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107754
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Zs.A 653: Show issues

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Article ; Online: Mixed-donor faecal microbiota transplantation was associated with increased butyrate-producing bacteria for obesity.

Xu, Zhilu / Mak, Joyce Wing Yan / Lin, Yu / Yang, Keli / Liu, Qin / Zhang, Fen / Lau, Louis / Tang, Whitney / Ching, Jessica Yl / Tun, Hein M / Chan, Paul / Chan, Francis K L / Ng, Siew C

Gut

2024 Volume 73, Issue 5, Page(s) 875–878

MeSH term(s)	Humans ; Fecal Microbiota Transplantation ; Obesity/therapy ; Microbiota ; Butyrates ; Bacteria ; Feces/microbiology
Chemical Substances	Butyrates
Language	English
Publishing date	2024-04-05
Publishing country	England
Document type	Letter
ZDB-ID	80128-8
ISSN	1468-3288 ; 0017-5749
ISSN (online)	1468-3288
ISSN	0017-5749
DOI	10.1136/gutjnl-2022-328993
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Article: A Comprehensive and Improved Definition for Hospital-Acquired Pressure Injury Classification Based on Electronic Health Records: Comparative Study.

Sotoodeh, Mani / Zhang, Wenhui / Simpson, Roy L / Hertzberg, Vicki Stover / Ho, Joyce C

JMIR medical informatics

2023 Volume 11, Page(s) e40672

Abstract: Background: Patients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare ... ...

Abstract	Background: Patients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare and Medicaid considers hospital-acquired PIs (HAPIs) as the most frequent preventable event, and they are the second most common claim in lawsuits. With the growing use of electronic health records (EHRs) in hospitals, an opportunity exists to build machine learning models to identify and predict HAPI rather than relying on occasional manual assessments by human experts. However, accurate computational models rely on high-quality HAPI data labels. Unfortunately, the different data sources within EHRs can provide conflicting information on HAPI occurrence in the same patient. Furthermore, the existing definitions of HAPI disagree with each other, even within the same patient population. The inconsistent criteria make it impossible to benchmark machine learning methods to predict HAPI. Objective: The objective of this project was threefold. We aimed to identify discrepancies in HAPI sources within EHRs, to develop a comprehensive definition for HAPI classification using data from all EHR sources, and to illustrate the importance of an improved HAPI definition. Methods: We assessed the congruence among HAPI occurrences documented in clinical notes, diagnosis codes, procedure codes, and chart events from the Medical Information Mart for Intensive Care III database. We analyzed the criteria used for the 3 existing HAPI definitions and their adherence to the regulatory guidelines. We proposed the Emory HAPI (EHAPI), which is an improved and more comprehensive HAPI definition. We then evaluated the importance of the labels in training a HAPI classification model using tree-based and sequential neural network classifiers. Results: We illustrate the complexity of defining HAPI, with <13% of hospital stays having at least 3 PI indications documented across 4 data sources. Although chart events were the most common indicator, it was the only PI documentation for >49% of the stays. We demonstrate a lack of congruence across existing HAPI definitions and EHAPI, with only 219 stays having a consensus positive label. Our analysis highlights the importance of our improved HAPI definition, with classifiers trained using our labels outperforming others on a small manually labeled set from nurse annotators and a consensus set in which all definitions agreed on the label. Conclusions: Standardized HAPI definitions are important for accurately assessing HAPI nursing quality metric and determining HAPI incidence for preventive measures. We demonstrate the complexity of defining an occurrence of HAPI, given the conflicting and incomplete EHR data. Our EHAPI definition has favorable properties, making it a suitable candidate for HAPI classification tasks.
Language	English
Publishing date	2023-02-23
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/40672
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Article: Integrated genetic profiling of archival pediatric high-grade glial tumors and reassessment with 2021 WHO classification of paediatric CNS tumours.

Cooley, Linda D / Lansdon, Lisa A / Laurence, Kris / Herriges, John C / Zhang, Lei / Repnikova, Elena A / Joyce, Julie / Thakor, Preeti / Warren, Lisa / Smith, Scott C / Yoo, Byunggil / Gener, Melissa / Ginn, Kevin F / Farooqi, Midhat S

Cancer genetics

2023 Volume 274-275, Page(s) 10–20

Abstract: Though rare, pediatric high-grade gliomas (pHGG) are a leading cause of cancer-related mortality in children. We wanted to determine whether our currently available clinical laboratory methods could better define diagnosis for pHGG that had been archived ...

Abstract	Though rare, pediatric high-grade gliomas (pHGG) are a leading cause of cancer-related mortality in children. We wanted to determine whether our currently available clinical laboratory methods could better define diagnosis for pHGG that had been archived at our institution for the past 20 years (1998 to 2017). We investigated 33 formalin-fixed paraffin-embedded pHGG using ThermoFisher Oncoscan SNP microarray with somatic mutation analysis, Sanger sequencing, and whole genome sequencing. These data were correlated with historical histopathological, chromosomal, clinical, and radiological data. Tumors were subsequently classified according to the 2021 WHO Classification of Paediatric CNS Tumours. All 33 tumors were found to have genetic aberrations that placed them within a 2021 WHO subtype and/or provided prognostic information; 6 tumors were upgraded from WHO CNS grade 3 to grade 4. New pHGG genetic features were found including two small cell glioblastomas with H3 G34 mutations not previously described; one tumor with STRN-NTRK2 fusion; and a congenital diffuse leptomeningeal glioneuronal tumor without a chromosomal 1p deletion but with KIAA1549-BRAF fusion. Overall, the combination of laboratory methods yielded key information for tumor classification. Thus, even small studies of these uncommon tumor types may yield new genetic features and possible new subtypes that warrant future investigations.
MeSH term(s)	Child ; Humans ; Brain Neoplasms/genetics ; Brain Neoplasms/pathology ; Glioma/genetics ; Glioma/pathology ; Central Nervous System Neoplasms/genetics ; Mutation/genetics ; World Health Organization
Language	English
Publishing date	2023-03-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2599227-2
ISSN	2210-7762
ISSN	2210-7762
DOI	10.1016/j.cancergen.2023.02.004
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Article ; Online: Author Correction: Online learning for orientation estimation during translation in an insect ring attractor network.

Robinson, Brian S / Norman-Tenazas, Raphael / Cervantes, Martha / Symonette, Danilo / Johnson, Erik C / Joyce, Justin / Rivlin, Patricia K / Hwang, Grace M / Zhang, Kechen / Gray-Roncal, William

Scientific reports

2022 Volume 12, Issue 1, Page(s) 4663

Language	English
Publishing date	2022-03-18
Publishing country	England
Document type	Published Erratum
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-08814-9
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Article ; Online: Controversies in Respiratory Protective Equipment Selection and Use During COVID-19.

Zhang, Joyce C / Cram, Peter / Adisesh, Anil

Journal of hospital medicine

2020 Volume 15, Issue 5, Page(s) 292–294

MeSH term(s)	COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Dissent and Disputes ; Humans ; Infectious Disease Transmission, Patient-to-Professional/prevention & control ; Masks ; Pandemics/prevention & control ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Practice Guidelines as Topic ; Randomized Controlled Trials as Topic ; Respiratory Protective Devices ; Uncertainty
Keywords	covid19
Language	English
Publishing date	2020-04-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	2233783-0
ISSN	1553-5606 ; 1553-5592
ISSN (online)	1553-5606
ISSN	1553-5592
DOI	10.12788/jhm.3437
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: SMAT: An attention-based deep learning solution to the automation of schema matching.

Zhang, Jing / Shin, Bonggun / Choi, Jinho D / Ho, Joyce C

Advances in databases and information systems. ADBIS

2021 Volume 12843, Page(s) 260–274

Abstract: Schema matching aims to identify the correspondences among attributes of database schemas. It is frequently considered as the most challenging and decisive stage existing in many contemporary web semantics and database systems. Low-quality algorithmic ... ...

Abstract	Schema matching aims to identify the correspondences among attributes of database schemas. It is frequently considered as the most challenging and decisive stage existing in many contemporary web semantics and database systems. Low-quality algorithmic matchers fail to provide improvement while manually annotation consumes extensive human efforts. Further complications arise from data privacy in certain domains such as healthcare, where only schema-level matching should be used to prevent data leakage. For this problem, we propose SMAT, a new deep learning model based on state-of-the-art natural language processing techniques to obtain semantic mappings between source and target schemas using only the attribute name and description. SMAT avoids directly encoding domain knowledge about the source and target systems, which allows it to be more easily deployed across different sites. We also introduce a new benchmark dataset, OMAP, based on real-world schema-level mappings from the healthcare domain. Our extensive evaluation of various benchmark datasets demonstrates the potential of SMAT to help automate schema-level matching tasks.
Language	English
Publishing date	2021-08-16
Publishing country	Germany
Document type	Journal Article
DOI	10.1007/978-3-030-82472-3_19
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Article: Communication Efficient Tensor Factorization for Decentralized Healthcare Networks.

Ma, Jing / Zhang, Qiuchen / Lou, Jian / Xiong, Li / Bhavani, Sivasubramanium / Ho, Joyce C

Proceedings. IEEE International Conference on Data Mining

2022 Volume 2021, Page(s) 1216–1221

Abstract: Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients history of medical ... ...

Abstract	Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients history of medical procedures, medications, diagnosis, lab tests, etc., are converted to meaningful and interpretable medical concepts. Federated tensor factorization distributes the tensor computation to multiple workers under the coordination of a central server, which enables jointly learning the phenotypes across multiple hospitals while preserving the privacy of the patient information. However, existing federated tensor factorization algorithms encounter the single-point-failure issue with the involvement of the central server, which is not only easily exposed to external attacks, but also limits the number of clients sharing information with the server under restricted uplink bandwidth. In this paper, we propose CiderTF, a communication-efficient decentralized generalized tensor factorization, which reduces the uplink communication cost by leveraging a four-level communication reduction strategy designed for a generalized tensor factorization, which has the flexibility of modeling different tensor distribution with multiple kinds of loss functions. Experiments on two real-world EHR datasets demonstrate that CiderTF achieves comparable convergence with the communication reduction up to 99.99%.
Language	English
Publishing date	2022-01-24
Publishing country	United States
Document type	Journal Article
ISSN	1550-4786
ISSN	1550-4786
DOI	10.1109/icdm51629.2021.00147
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