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Article ; Online: A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.

Oh, Wonsuk / Jayaraman, Pushkala / Tandon, Pranai / Chaddha, Udit S / Kovatch, Patricia / Charney, Alexander W / Glicksberg, Benjamin S / Nadkarni, Girish N

Artificial intelligence in medicine

2023 Volume 148, Page(s) 102750

Abstract: Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. ...

Abstract	Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.
MeSH term(s)	Humans ; Time Factors ; Critical Illness ; Cross-Sectional Studies ; COVID-19 ; Algorithms
Language	English
Publishing date	2023-12-20
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	645179-2
ISSN	1873-2860 ; 0933-3657
ISSN (online)	1873-2860
ISSN	0933-3657
DOI	10.1016/j.artmed.2023.102750
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Article ; Online: Local large language models for privacy-preserving accelerated review of historic echocardiogram reports.

Vaid, Akhil / Duong, Son Q / Lampert, Joshua / Kovatch, Patricia / Freeman, Robert / Argulian, Edgar / Croft, Lori / Lerakis, Stamatios / Goldman, Martin / Khera, Rohan / Nadkarni, Girish N

Journal of the American Medical Informatics Association : JAMIA

2024

Abstract: Objectives: The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient's entire echocardiogram report history. This approach is intended to streamline the ... ...

Abstract	Objectives: The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient's entire echocardiogram report history. This approach is intended to streamline the extraction of clinical insights from multiple echocardiogram reports, particularly in patients with complex cardiac diseases, thereby enhancing both patient care and research efficiency. Materials and methods: Data from over 10 years were collected, comprising echocardiogram reports from patients with more than 10 echocardiograms on file at the Mount Sinai Health System. These reports were converted into a single document per patient for analysis, broken down into snippets and relevant snippets were retrieved using text similarity measures. The LLaMA-2 70B model was employed for analyzing the text using a specially crafted prompt. The model's performance was evaluated against ground-truth answers created by faculty cardiologists. Results: The study analyzed 432 reports from 37 patients for a total of 100 question-answer pairs. The LLM correctly answered 90% questions, with accuracies of 83% for temporality, 93% for severity assessment, 84% for intervention identification, and 100% for diagnosis retrieval. Errors mainly stemmed from the LLM's inherent limitations, such as misinterpreting numbers or hallucinations. Conclusion: The study demonstrates the feasibility and effectiveness of using a local, open-source LLM for querying and interpreting echocardiogram report data. This approach offers a significant improvement over traditional keyword-based searches, enabling more contextually relevant and semantically accurate responses; in turn showing promise in enhancing clinical decision-making and research by facilitating more efficient access to complex patient data.
Language	English
Publishing date	2024-04-30
Publishing country	England
Document type	Journal Article
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocae085
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Article ; Online: Prediction of individual COVID-19 diagnosis using baseline demographics and lab data.

Zhang, Jimmy / Jun, Tomi / Frank, Jordi / Nirenberg, Sharon / Kovatch, Patricia / Huang, Kuan-Lin

Scientific reports

2021 Volume 11, Issue 1, Page(s) 13913

Abstract: The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning- ... ...

Abstract	The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting individual COVID-19 positive diagnosis relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, temperature, age, lymphocyte count, smoking, hemoglobin levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 diagnosis prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.
MeSH term(s)	Adult ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19 Testing/methods ; Cohort Studies ; Demography/methods ; Humans ; Machine Learning ; Prognosis ; ROC Curve ; SARS-CoV-2/pathogenicity
Language	English
Publishing date	2021-07-06
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-93126-7
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Article ; Online: Autoencoders for sample size estimation for fully connected neural network classifiers.

Gulamali, Faris F / Sawant, Ashwin S / Kovatch, Patricia / Glicksberg, Benjamin / Charney, Alexander / Nadkarni, Girish N / Oermann, Eric

NPJ digital medicine

2022 Volume 5, Issue 1, Page(s) 180

Abstract: Sample size estimation is a crucial step in experimental design but is understudied in the context of deep learning. Currently, estimating the quantity of labeled data needed to train a classifier to a desired performance, is largely based on prior ... ...

Abstract	Sample size estimation is a crucial step in experimental design but is understudied in the context of deep learning. Currently, estimating the quantity of labeled data needed to train a classifier to a desired performance, is largely based on prior experience with similar models and problems or on untested heuristics. In many supervised machine learning applications, data labeling can be expensive and time-consuming and would benefit from a more rigorous means of estimating labeling requirements. Here, we study the problem of estimating the minimum sample size of labeled training data necessary for training computer vision models as an exemplar for other deep learning problems. We consider the problem of identifying the minimal number of labeled data points to achieve a generalizable representation of the data, a minimum converging sample (MCS). We use autoencoder loss to estimate the MCS for fully connected neural network classifiers. At sample sizes smaller than the MCS estimate, fully connected networks fail to distinguish classes, and at sample sizes above the MCS estimate, generalizability strongly correlates with the loss function of the autoencoder. We provide an easily accessible, code-free, and dataset-agnostic tool to estimate sample sizes for fully connected networks. Taken together, our findings suggest that MCS and convergence estimation are promising methods to guide sample size estimates for data collection and labeling prior to training deep learning models in computer vision.
Language	English
Publishing date	2022-12-13
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-022-00728-0
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Article: Multi-ethnic Investigation of Risk and Immune Determinants of COVID-19 Outcomes.

Jun, Tomi / Mathew, Divij / Sharma, Navya / Nirenberg, Sharon / Huang, Hsin-Hui / Kovatch, Patricia / Wherry, E John / Huang, Kuan-Lin

Research square

2022

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2022-03-22
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-1055587/v1
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Article ; Online: Multiethnic Investigation of Risk and Immune Determinants of COVID-19 Outcomes.

Jun, Tomi / Mathew, Divij / Sharma, Navya / Nirenberg, Sharon / Huang, Hsin-Hui / Kovatch, Patricia / Wherry, Edward John / Huang, Kuan-Lin

Frontiers in cellular and infection microbiology

2022 Volume 12, Page(s) 933190

Abstract: Background: Disparate COVID-19 outcomes have been observed between Hispanic, non-Hispanic Black, and White patients. The underlying causes for these disparities are not fully understood.: Methods: This was a retrospective study utilizing electronic ... ...

Abstract	Background: Disparate COVID-19 outcomes have been observed between Hispanic, non-Hispanic Black, and White patients. The underlying causes for these disparities are not fully understood. Methods: This was a retrospective study utilizing electronic medical record data from five hospitals within a single academic health system based in New York City. Multivariable logistic regression models were used to identify demographic, clinical, and lab values associated with in-hospital mortality. Results: A total of 3,086 adult patients with self-reported race/ethnicity information presenting to the emergency department and hospitalized with COVID-19 up to April 13, 2020, were included in this study. While older age (multivariable odds ratio (OR) 1.06, 95% CI 1.05-1.07) and baseline hypoxia (multivariable OR 2.71, 95% CI 2.17-3.36) were associated with increased mortality overall and across all races/ethnicities, non-Hispanic Black (median age 67, interquartile range (IQR) 58-76) and Hispanic (median age 63, IQR 50-74) patients were younger and had different comorbidity profiles as compared to non-Hispanic White patients (median age 73, IQR 62-84; p < 0.05 for both comparisons). Among inflammatory markers associated with COVID-19 mortality, there was a significant interaction between the non-Hispanic Black population and interleukin-1-beta (interaction p-value 0.04). Conclusions: This analysis of a multiethnic cohort highlights the need for inclusion and consideration of diverse populations in ongoing COVID-19 trials targeting inflammatory cytokines.
MeSH term(s)	Adult ; Black or African American ; Aged ; COVID-19 ; Humans ; Middle Aged ; Retrospective Studies ; SARS-CoV-2 ; White People
Language	English
Publishing date	2022-07-22
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2619676-1
ISSN	2235-2988 ; 2235-2988
ISSN (online)	2235-2988
ISSN	2235-2988
DOI	10.3389/fcimb.2022.933190
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Article: Optimizing High-Performance Computing Systems for Biomedical Workloads.

Kovatch, Patricia / Gai, Lili / Cho, Hyung Min / Fluder, Eugene / Jiang, Dansha

IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum : [proceedings]. IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum

2020 Volume 2020, Page(s) 183–192

Abstract: The productivity of computational biologists is limited by the speed of their workflows and subsequent overall job throughput. Because most biomedical researchers are focused on better understanding scientific phenomena rather than developing and ... ...

Abstract	The productivity of computational biologists is limited by the speed of their workflows and subsequent overall job throughput. Because most biomedical researchers are focused on better understanding scientific phenomena rather than developing and optimizing code, a computing and data system implemented in an adventitious and/or non-optimized manner can impede the progress of scientific discovery. In our experience, most computational, life-science applications do not generally leverage the full capabilities of high-performance computing, so tuning a system for these applications is especially critical. To optimize a system effectively, systems staff must understand the effects of the applications on the system. Effective stewardship of the system includes an analysis of the impact of the applications on the compute cores, file system, resource manager and queuing policies. The resulting improved system design, and enactment of a sustainability plan, help to enable a long-term resource for productive computational and data science. We present a case study of a typical biomedical computational workload at a leading academic medical center supporting over $100 million per year in computational biology research. Over the past eight years, our high-performance computing system has enabled over 900 biomedical publications in four major areas: genetics and population analysis, gene expression, machine learning, and structural and chemical biology. We have upgraded the system several times in response to trends, actual usage, and user feedback. Major components crucial to this evolution include scheduling structure and policies, memory size, compute type and speed, parallel file system capabilities, and deployment of cloud technologies. We evolved a 70 teraflop machine to a 1.4 petaflop machine in seven years and grew our user base nearly 10-fold. For long-term stability and sustainability, we established a chargeback fee structure. Our overarching guiding principle for each progression has been to increase scientific throughput and enable enhanced scientific fidelity with minimal impact to existing user workflows or code. This highly-constrained system optimization has presented unique challenges, leading us to adopt new approaches to provide constructive pathways forward. We share our practical strategies resulting from our ongoing growth and assessments.
Language	English
Publishing date	2020-07-28
Publishing country	United States
Document type	Journal Article
ISSN	2164-7062
ISSN	2164-7062
DOI	10.1109/ipdpsw50202.2020.00040
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Article ; Online: Analysis of sex-specific risk factors and clinical outcomes in COVID-19.

Jun, Tomi / Nirenberg, Sharon / Weinberger, Tziopora / Sharma, Navya / Pujadas, Elisabet / Cordon-Cardo, Carlos / Kovatch, Patricia / Huang, Kuan-Lin

Communications medicine

2021 Volume 1, Page(s) 3

Abstract: Background: Sex has consistently been shown to affect COVID-19 mortality, but it remains unclear how each sex's clinical outcome may be distinctively shaped by risk factors.: Methods: We studied a primary cohort of 4930 patients hospitalized with ... ...

Abstract	Background: Sex has consistently been shown to affect COVID-19 mortality, but it remains unclear how each sex's clinical outcome may be distinctively shaped by risk factors. Methods: We studied a primary cohort of 4930 patients hospitalized with COVID-19 in a single healthcare system in New York City from the start of the pandemic till August 5, 2020, and a validation cohort of 1645 patients hospitalized with COVID-19 in the same healthcare system from August 5, 2020, to January 13, 2021. Results: Here we show that male sex was independently associated with in-hospital mortality, intubation, and ICU care after adjusting for demographics and comorbidities. Using interaction analysis and sex-stratified models, we found that hypoxia interacted with sex to preferentially increase women's mortality risk while obesity interacted with sex to preferentially increase women's risk of intubation and intensive care in our primary cohort. In the validation cohort, we observed that male sex remained an independent risk factor for mortality, but sex-specific interactions were not replicated. Conclusions: We conducted a comprehensive sex-stratified analysis of a large cohort of hospitalized COVID-19 patients, highlighting clinical factors that may contribute to sex differences in the outcome of COVID-19.
Language	English
Publishing date	2021-06-30
Publishing country	England
Document type	Journal Article
ISSN	2730-664X
ISSN (online)	2730-664X
DOI	10.1038/s43856-021-00006-2
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Article ; Online: Mortality and risk factors among US Black, Hispanic, and White patients with COVID-19

Jun, Tomi / Nirenberg, Sharon / Kovatch, Patricia / Huang, Kuan-lin

medRxiv

Abstract: Background: Little is known about risk factors for COVID-19 outcomes, particularly across diverse racial and ethnic populations in the United States. Methods: In this prospective cohort study, we followed 3,086 COVID-19 patients hospitalized on or before ...

Abstract	Background: Little is known about risk factors for COVID-19 outcomes, particularly across diverse racial and ethnic populations in the United States. Methods: In this prospective cohort study, we followed 3,086 COVID-19 patients hospitalized on or before April 13, 2020 within an academic health system in New York (The Mount Sinai Health System) until June 2, 2020. Multivariable logistic regression was used to evaluate demographic, clinical, and laboratory factors as independent predictors of in-hospital mortality. The analysis was stratified by self-reported race and ethnicity. Findings: A total of 3,086 COVID-19 patients were hospitalized, of whom 680 were excluded (78 due to missing race or ethnicity data, 144 were Asian, and 458 were of other unspecified race/ethnicity). Of the 2,406 patients included, 892 (37.1%) were Hispanic, 825 (34.3%) were black, and 689 (28.6%) were white. Black and Hispanic patients were younger than White patients (median age 67 and 63 vs. 73, p<0.001 for both), and they had different comorbidity profiles. Older age and baseline hypoxia were associated with increased mortality across all races. There were suggestive but non-significant interactions between Black race and diabetes (p=0.09), and obesity (p=0.10). Among inflammatory markers associated with COVID-19 mortality, there was a significant interaction between Black race and interleukin-1-beta (p=0.04), and a suggestive interactions between Hispanic ethnicity and procalcitonin (p=0.07) and interleukin-8 (p=0.09). Interpretation: In this large, racially and ethnically diverse cohort of COVID-19 patients in New York City, we identified similarities and important differences across racial and ethnic groups in risk factors for in-hospital mortality.
Keywords	covid19
Language	English
Publishing date	2020-09-11
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.09.08.20190686
Database	COVID19

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Article ; Online: Sex-specificity of mortality risk factors among hospitalized COVID-19 patients in New York City: prospective cohort study

Jun, Tomi / Nirenberg, Sharon / Kovatch, Patricia / Huang, Kuan-lin

medRxiv

Abstract: Objective: To identify sex-specific effects of risk factors for in-hospital mortality among COVID-19 patients admitted to a hospital system in New York City. Design: Prospective observational cohort study with in-hospital mortality as the primary outcome. ...

Abstract	Objective: To identify sex-specific effects of risk factors for in-hospital mortality among COVID-19 patients admitted to a hospital system in New York City. Design: Prospective observational cohort study with in-hospital mortality as the primary outcome. Setting: Five acute care hospitals within a single academic medical system in New York City. Participants: 3,086 hospital inpatients with COVID-19 admitted on or before April 13, 2020 and followed through June 2, 2020. Follow-up till discharge or death was complete for 99.3% of the cohort. Results: The majority of the cohort was male (59.6%). Men were younger (median 64 vs. 70, p<0.001) and less likely to have comorbidities such as hypertension (32.5% vs. 39.9%, p<0.001), diabetes (22.6% vs. 26%, p=0.03), and obesity (6.9% vs. 9.8%, p=0.004) compared to women. Women had lower median values of laboratory markers associated with inflammation compared to men: white blood cells (5.95 vs. 6.8 K/uL, p<0.001), procalcitonin (0.14 vs 0.21 ng/mL, p<0.001), lactate dehydrogenase (375 vs. 428 U/L, p<0.001), C-reactive protein (87.7 vs. 123.2 mg/L, p<0.001). Unadjusted mortality was similar between men and women (28.8% vs. 28.5%, p=0.84), but more men required intensive care than women (25.2% vs. 19%, p<0.001). Male sex was an independent risk factor for mortality (OR 1.26, 95% 1.04-1.51) after adjustment for demographics, comorbidities, and baseline hypoxia. There were significant interactions between sex and coronary artery disease (p=0.038), obesity (p=0.01), baseline hypoxia (p<0.001), ferritin (p=0.002), lactate dehydrogenase (p=0.003), and procalcitonin (p=0.03). Except for procalcitonin, which had the opposite association, each of these factors was associated with disproportionately higher mortality among women. Conclusions: Male sex was an independent predictor of mortality, consistent with prior studies. Notably, there were significant sex-specific interactions which indicated a disproportionate increase in mortality among women with coronary artery disease, obesity, and hypoxia. These new findings highlight patient subgroups for further study and help explain the recognized sex differences in COVID-19 outcomes.
Keywords	covid19
Language	English
Publishing date	2020-08-01
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.07.29.20164640
Database	COVID19

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