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Article ; Online: Advances in Clinical Decision Support Systems: Contributions from the 2022 Literature.

Lehmann, Christoph U / Subbian, Vignesh

2023 Volume 32, Issue 1, Page(s) 179–183

Abstract: Objective: To summarize significant research contributions published in 2022 in the field of clinical decision support (CDS) systems and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) ... ...

Abstract	Objective: To summarize significant research contributions published in 2022 in the field of clinical decision support (CDS) systems and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook 2023. Methods: A renewed search query for identifying CDS scholarship was developed using Medical Subject Headings (MeSH) terms and related keywords. The query was executed in PubMed in January 2023. The search results were reviewed in three stages by two reviewers: title-based triaging, followed by abstract screening, and then full text review. The resulting articles were sent for external review to identity best paper candidates. Results: A total of 1,939 articles related to CDS were retrieved. Of these, 11 articles were selected as candidates for best papers. The general themes of the final three best papers are (1) reducing documentation burden through in-line guidance for clinical notes, (2) clinician engagement for continuous improvement of CDS, and (3) mitigating healthcare-related carbon emissions using scalable and accessible CDS, respectively. Conclusion: The field of clinical decision support remains highly active and dynamic, with innovative contributions to a range of clinical domains from primary to acute care. Interoperability issues, documentation burden, clinician acceptance, and the need for effective integration into existing healthcare workflows are among the prominent challenges and areas of interest faced by CDS implementation efforts.
MeSH term(s)	Decision Support Systems, Clinical ; Medical Informatics ; Documentation
Language	English
Publishing date	2023-12-26
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2251229-9
ISSN	2364-0502 ; 2364-0502
ISSN (online)	2364-0502
ISSN	2364-0502
DOI	10.1055/s-0043-1768751
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.

Pungitore, Sarah / Subbian, Vignesh

Journal of healthcare informatics research

2023 Volume 7, Issue 3, Page(s) 313–331

Abstract: Temporal electronic health record (EHR) data are often preferred for clinical prediction tasks because they offer more complete representations of a patient's pathophysiology than static data. A challenge when working with temporal EHR data is problem ... ...

Abstract	Temporal electronic health record (EHR) data are often preferred for clinical prediction tasks because they offer more complete representations of a patient's pathophysiology than static data. A challenge when working with temporal EHR data is problem formulation, which includes defining the time windows of interest and the prediction task. Our objective was to conduct a systematic review that assessed the definition and reporting of concepts relevant to temporal clinical prediction tasks. We searched PubMed® and IEEE Xplore® databases for studies from January 1, 2010 applying machine learning models to EHR data for patient outcome prediction. Publications applying time-series methods were selected for further review. We identified 92 studies and summarized them by clinical context and definition and reporting of the prediction problem. For the time windows of interest, 12 studies did not discuss window lengths, 57 used a single set of window lengths, and 23 evaluated the relationship between window length and model performance. We also found that 72 studies had appropriate reporting of the prediction task. However, evaluation of prediction problem formulation for temporal EHR data was complicated by heterogeneity in assessing and reporting of these concepts. Even among studies modeling similar clinical outcomes, there were variations in terminology used to describe the prediction problem, rationale for window lengths, and determination of the outcome of interest. As temporal modeling using EHR data expands, minimal reporting standards should include time-series specific concerns to promote rigor and reproducibility in future studies and facilitate model implementation in clinical settings. Supplementary information: The online version contains supplementary material available at 10.1007/s41666-023-00143-4.
Language	English
Publishing date	2023-08-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2895595-X
ISSN	2509-498X ; 2509-4971
ISSN (online)	2509-498X
ISSN	2509-4971
DOI	10.1007/s41666-023-00143-4
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Article ; Online: Computable Phenotypes for Post-acute sequelae of SARS-CoV-2: A National COVID Cohort Collaborative Analysis.

Pungitore, Sarah / Olorunnisola, Toluwanimi / Mosier, Jarrod / Subbian, Vignesh

AMIA ... Annual Symposium proceedings. AMIA Symposium

2024 Volume 2023, Page(s) 589–598

Abstract: Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yet incompletely understood public health concern. Several studies have examined various ways to phenotype PASC to better characterize this heterogeneous condition. However, many gaps ...

Abstract	Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yet incompletely understood public health concern. Several studies have examined various ways to phenotype PASC to better characterize this heterogeneous condition. However, many gaps in PASC phenotyping research exist, including a lack of the following: 1) standardized definitions for PASC based on symptomatology; 2) generalizable and reproducible phenotyping heuristics and meta-heuristics; and 3) phenotypes based on both COVID-19 severity and symptom duration. In this study, we defined computable phenotypes (or heuristics) and meta-heuristics for PASC phenotypes based on COVID-19 severity and symptom duration. We also developed a symptom profile for PASC based on a common data standard. We identified four phenotypes based on COVID-19 severity (mild vs. moderate/severe) and duration of PASC symptoms (subacute vs. chronic). The symptoms groups with the highest frequency among phenotypes were cardiovascular and neuropsychiatric with each phenotype characterized by a different set of symptoms.
MeSH term(s)	Humans ; SARS-CoV-2 ; COVID-19 ; Disease Progression ; Heuristics ; Phenotype
Language	English
Publishing date	2024-01-11
Publishing country	United States
Document type	Journal Article
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Data Sharing Between Jail and Community Health Systems: Missing Links and Lessons for Re-Entry Success.

Glowalla, Geoffrey / Subbian, Vignesh

Studies in health technology and informatics

2022 Volume 290, Page(s) 47–51

Abstract: Data sharing and interoperability between jail systems and community health providers are critical for successful re-entry of incarcerated individuals into the mainstream community. Using a case study approach, we present an account of interoperability ... ...

Abstract	Data sharing and interoperability between jail systems and community health providers are critical for successful re-entry of incarcerated individuals into the mainstream community. Using a case study approach, we present an account of interoperability efforts between jail and community health systems in the County of Orange (California, USA), including the overall infrastructure comprising of the jail management system, jail health system, and the community health system. We also describe outcomes and lessons from the Jail to Community Re-entry Program implemented in the County of Orange, along with recommendations and common data elements required for effective care transitions from custody to community.
MeSH term(s)	Community Health Planning ; Humans ; Information Dissemination ; Jails ; Public Health
Language	English
Publishing date	2022-06-08
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI220029
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Article ; Online: Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering.

Ghaderi, Hamid / Foreman, Brandon / Reddy, Chandan K / Subbian, Vignesh

ArXiv

2024

Abstract: Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI ... ...

Abstract	Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes (α, β, and γ), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype α represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype β signifies severe TBI with diverse clinical manifestations, and phenotype γ represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.
Language	English
Publishing date	2024-01-15
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
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Article ; Online: Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review.

Ardalan, Zaniar / Subbian, Vignesh

Frontiers in artificial intelligence

2022 Volume 5, Page(s) 780405

Abstract: Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling ... ...

Abstract	Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time.
Language	English
Publishing date	2022-02-21
Publishing country	Switzerland
Document type	Journal Article ; Review
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2022.780405
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Article ; Online: Managed critical care: impact of remote decision-making on patient outcomes.

Essay, Patrick / Zhang, Tianyi / Mosier, Jarrod / Subbian, Vignesh

The American journal of managed care

2023 Volume 29, Issue 7, Page(s) e208–e214

Abstract: Objectives: Tele-intensive care unit (tele-ICU) use has become increasingly common as an extension of bedside care for critically ill patients. The objective of this work was to illustrate the degree of tele-ICU involvement in critical care processes ... ...

Abstract	Objectives: Tele-intensive care unit (tele-ICU) use has become increasingly common as an extension of bedside care for critically ill patients. The objective of this work was to illustrate the degree of tele-ICU involvement in critical care processes and evaluate the impact of tele-ICU decision-making authority. Study design: Previous studies examining tele-ICU impact on patient outcomes do not sufficiently account for the extent of decision-making authority between remote and bedside providers. In this study, we examine patient outcomes with respect to different levels of remote intervention. Methods: Analysis and summary statistics were generated to characterize demographics and patient outcomes across different levels of tele-ICU intervention for 82,049 critically ill patients. Multivariate logistic regression was used to evaluate odds of mortality, readmission, and likelihood of patients being assigned to a particular remote intervention category. Results: Managing (vs consulting) physician type influenced the level of remote intervention (adjusted odds ratio [AOR], 2.42). A higher level of tele-ICU intervention was a significant factor for patient mortality (AOR, 1.25). Female sex (AOR, 1.05), illness severity (AOR, 1.01), and higher tele-ICU intervention level (AOR, 1.13) increased odds of ICU readmission, whereas length of stay in number of days (AOR, 0.93) and consulting (vs managing) physician type (AOR, 0.79) decreased readmission odds. Conclusions: This study's findings suggest that higher levels of tele-ICU intervention do not negatively affect patient outcomes. Our results are a step toward understanding tele-ICU impact on patient outcomes by accounting for extent of decision-making authority, and they suggest that the level of remote intervention may reflect patient severity. Further research using more granular data is needed to better understand assignment of intervention category and how variable levels of authority affect clinical decision-making in tele-ICU settings.
MeSH term(s)	Humans ; Female ; Critical Illness/therapy ; Telemedicine ; Critical Care/methods ; Intensive Care Units ; Odds Ratio
Language	English
Publishing date	2023-07-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2035781-3
ISSN	1936-2692 ; 1088-0224 ; 1096-1860
ISSN (online)	1936-2692
ISSN	1088-0224 ; 1096-1860
DOI	10.37765/ajmc.2023.89400
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Article ; Online: Transfer Learning Approaches for Neuroimaging Analysis

Zaniar Ardalan / Vignesh Subbian

Frontiers in Artificial Intelligence, Vol

A Scoping Review

2022 Volume 5

Abstract	Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time.
Keywords	neuroimaging ; medical imaging ; transfer learning ; convolutional neural network ; fine tuning ; domain adaptation ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2022-02-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Advances in Clinical Decision Support Systems: Contributions from the 2022 Literature

Lehmann, Christoph U. / Subbian, Vignesh

Yearbook of Medical Informatics

2023 Volume 32, Issue 01, Page(s) 179–183

Abstract	Objective: To summarize significant research contributions published in 2022 in the field of clinical decision support (CDS) systems and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook 2023. Methods: A renewed search query for identifying CDS scholarship was developed using Medical Subject Headings (MeSH) terms and related keywords. The query was executed in PubMed in January 2023. The search results were reviewed in three stages by two reviewers: title-based triaging, followed by abstract screening, and then full text review. The resulting articles were sent for external review to identity best paper candidates. Results: A total of 1,939 articles related to CDS were retrieved. Of these, 11 articles were selected as candidates for best papers. The general themes of the final three best papers are (1) reducing documentation burden through in-line guidance for clinical notes, (2) clinician engagement for continuous improvement of CDS, and (3) mitigating healthcare-related carbon emissions using scalable and accessible CDS, respectively. Conclusion: The field of clinical decision support remains highly active and dynamic, with innovative contributions to a range of clinical domains from primary to acute care. Interoperability issues, documentation burden, clinician acceptance, and the need for effective integration into existing healthcare workflows are among the prominent challenges and areas of interest faced by CDS implementation efforts.
Keywords	Medical informatics ; clinical decision support system ; implementation, expert system
Language	English
Publishing date	2023-08-01
Publisher	Georg Thieme Verlag KG
Publishing place	Stuttgart ; New York
Document type	Article
ZDB-ID	2251229-9
ISSN	2364-0502 ; 0943-4747 ; 2364-0502
ISSN (online)	2364-0502
ISSN	0943-4747 ; 2364-0502
DOI	10.1055/s-0043-1768751
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Article ; Online: Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data.

Ghaderi, Hamid / Foreman, Brandon / Nayebi, Amin / Tipirneni, Sindhu / Reddy, Chandan K / Subbian, Vignesh

AMIA ... Annual Symposium proceedings. AMIA Symposium

2024 Volume 2023, Page(s) 379–388

Abstract: Determining clinically relevant physiological states from multivariate time-series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. ...

Abstract	Determining clinically relevant physiological states from multivariate time-series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.
MeSH term(s)	Humans ; Brain Injuries, Traumatic/diagnosis ; Algorithms ; Cluster Analysis ; Time Factors ; Benchmarking
Language	English
Publishing date	2024-01-11
Publishing country	United States
Document type	Journal Article
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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