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Article ; Online: Severity of COVID-19-Related Illness in Massachusetts, July 2021 to December 2022.

Azhir, Alaleh / Strasser, Zachary H / Murphy, Shawn N / Estiri, Hossein

JAMA network open

2023 Volume 6, Issue 4, Page(s) e238203

MeSH term(s)	Humans ; COVID-19 ; Massachusetts/epidemiology ; SARS-CoV-2
Language	English
Publishing date	2023-04-03
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	2574-3805
ISSN (online)	2574-3805
DOI	10.1001/jamanetworkopen.2023.8203
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Article ; Online: Reply to Li

Foer, Dinah / Strasser, Zachary H / Cui, Jing / Cahill, Katherine N / Boyce, Joshua A / Murphy, Shawn N / Karlson, Elizabeth W

American journal of respiratory and critical care medicine

2023 Volume 208, Issue 12, Page(s) 1346–1347

Language	English
Publishing date	2023-10-19
Publishing country	United States
Document type	Letter
ZDB-ID	1180953-x
ISSN	1535-4970 ; 0003-0805 ; 1073-449X
ISSN (online)	1535-4970
ISSN	0003-0805 ; 1073-449X
DOI	10.1164/rccm.202310-1721LE
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Article ; Online: Temporal characterization of Alzheimer's Disease with sequences of clinical records.

Estiri, Hossein / Azhir, Alaleh / Blacker, Deborah L / Ritchie, Christine S / Patel, Chirag J / Murphy, Shawn N

EBioMedicine

2023 Volume 92, Page(s) 104629

Abstract: Background: Alzheimer's Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls on an ageing global population. Real-world data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision ... ...

Abstract	Background: Alzheimer's Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls on an ageing global population. Real-world data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision drug development and scale epidemiological research on AD. A precise characterization of AD cohorts is needed to address the noise abundant in RWD. Methods: We conducted a retrospective cohort study to develop and test computational models for AD cohort identification using clinical data from 8 Massachusetts healthcare systems. We mined temporal representations from EHR data using the transitive sequential pattern mining algorithm (tSPM) to train and validate our models. We then tested our models against a held-out test set from a review of medical records to adjudicate the presence of AD. We trained two classes of Machine Learning models, using Gradient Boosting Machine (GBM), to compare the utility of AD diagnosis records versus the tSPM temporal representations (comprising sequences of diagnosis and medication observations) from electronic medical records for characterizing AD cohorts. Findings: In a group of 4985 patients, we identified 219 tSPM temporal representations (i.e., transitive sequences) of medical records for constructing the best classification models. The models with sequential features improved AD classification by a magnitude of 3-16 percent over the use of AD diagnosis codes alone. The computed cohort included 663 patients, 35 of whom had no record of AD. Six groups of tSPM sequences were identified for characterizing the AD cohorts. Interpretation: We present sequential patterns of diagnosis and medication codes from electronic medical records, as digital markers of Alzheimer's Disease. Classification algorithms developed on sequential patterns can replace standard features from EHRs to enrich phenotype modelling. Funding: National Institutes of Health: the National Institute on Aging (RF1AG074372) and the National Institute of Allergy and Infectious Diseases (R01AI165535).
MeSH term(s)	Humans ; Alzheimer Disease/diagnosis ; Retrospective Studies ; Algorithms ; Machine Learning ; Electronic Health Records
Language	English
Publishing date	2023-05-27
Publishing country	Netherlands
Document type	Review ; Journal Article
ZDB-ID	2851331-9
ISSN	2352-3964
ISSN (online)	2352-3964
DOI	10.1016/j.ebiom.2023.104629
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Article ; Online: Association of GLP-1 Receptor Agonists with Chronic Obstructive Pulmonary Disease Exacerbations among Patients with Type 2 Diabetes.

Foer, Dinah / Strasser, Zachary H / Cui, Jing / Cahill, Katherine N / Boyce, Joshua A / Murphy, Shawn N / Karlson, Elizabeth W

American journal of respiratory and critical care medicine

2023 Volume 208, Issue 10, Page(s) 1088–1100

Abstract: Rationale: ...

Abstract	Rationale:
MeSH term(s)	Humans ; Diabetes Mellitus, Type 2/complications ; Diabetes Mellitus, Type 2/drug therapy ; Hypoglycemic Agents/therapeutic use ; Glucagon-Like Peptide-1 Receptor Agonists ; Retrospective Studies ; Dipeptidyl-Peptidase IV Inhibitors/therapeutic use ; Prospective Studies ; Sulfonylurea Compounds/therapeutic use ; Pulmonary Disease, Chronic Obstructive/complications ; Pulmonary Disease, Chronic Obstructive/drug therapy ; Pulmonary Disease, Chronic Obstructive/chemically induced
Chemical Substances	Hypoglycemic Agents ; Glucagon-Like Peptide-1 Receptor Agonists ; Dipeptidyl-Peptidase IV Inhibitors ; Sulfonylurea Compounds
Language	English
Publishing date	2023-08-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	1180953-x
ISSN	1535-4970 ; 0003-0805 ; 1073-449X
ISSN (online)	1535-4970
ISSN	0003-0805 ; 1073-449X
DOI	10.1164/rccm.202303-0491OC
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Article ; Online: Individualized prediction of COVID-19 adverse outcomes with MLHO

Hossein Estiri / Zachary H. Strasser / Shawn N. Murphy

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 9

Abstract: Abstract The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, ... ...

Abstract	Abstract The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients’ past medical records (before their COVID-19 infection). MLHO’s architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients’ pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may ...
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Individualized prediction of COVID-19 adverse outcomes with MLHO.

Estiri, Hossein / Strasser, Zachary H / Murphy, Shawn N

Scientific reports

2021 Volume 11, Issue 1, Page(s) 5322

Abstract: The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including ... ...

Abstract	The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients' past medical records (before their COVID-19 infection). MLHO's architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.
MeSH term(s)	Adult ; Age Factors ; Aged ; Aged, 80 and over ; COVID-19/diagnosis ; COVID-19/mortality ; COVID-19/therapy ; COVID-19/virology ; Data Mining/methods ; Electronic Health Records/statistics & numerical data ; Female ; Hospitalization/statistics & numerical data ; Humans ; Intensive Care Units/statistics & numerical data ; Machine Learning ; Male ; Middle Aged ; Models, Statistical ; Pandemics/statistics & numerical data ; Prognosis ; ROC Curve ; Reproducibility of Results ; Respiration, Artificial/statistics & numerical data ; Retrospective Studies ; Risk Assessment/methods ; Risk Factors ; SARS-CoV-2/isolation & purification ; SARS-CoV-2/pathogenicity
Language	English
Publishing date	2021-03-05
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Validation Study
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-84781-x
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Article ; Online: Semi-supervised encoding for outlier detection in clinical observation data.

Estiri, Hossein / Murphy, Shawn N

Computer methods and programs in biomedicine

2019 Volume 181, Page(s) 104830

Abstract: Background and objective: Electronic Health Record (EHR) data often include observation records that are unlikely to represent the "truth" about a patient at a given clinical encounter. Due to their high throughput, examples of such implausible ... ...

Abstract	Background and objective: Electronic Health Record (EHR) data often include observation records that are unlikely to represent the "truth" about a patient at a given clinical encounter. Due to their high throughput, examples of such implausible observations are frequent in records of laboratory test results and vital signs. Outlier detection methods can offer low-cost solutions to flagging implausible EHR observations. This article evaluates the utility of a semi-supervised encoding approach (super-encoding) for constructing non-linear exemplar data distributions from EHR observation data and detecting non-conforming observations as outliers. Methods: Two hypotheses are tested using experimental design and non-parametric hypothesis testing procedures: (1) adding demographic features (e.g., age, gender, race/ethnicity) can increase precision in outlier detection, (2) sampling small subsets of the large EHR data can increase outlier detection by reducing noise-to-signal ratio. The experiments involved applying 492 encoder configurations (involving different input features, architectures, sampling ratios, and error margins) to a set of 30 datasets EHR observations including laboratory tests and vital sign records extracted from the Research Patient Data Registry (RPDR) from Partners HealthCare. Results: Results are obtained from (30 × 492) 14,760 encoders. The semi-supervised encoding approach (super-encoding) outperformed conventional autoencoders in outlier detection. Adding age of the patient at the observation (encounter) to the baseline encoder that only included observation value as the input feature slightly improved outlier detection. Top-nine performing encoders are introduced. The best outlier detection performance was from a semi-supervised encoder, with observation value as the single feature and a single hidden layer, built on one percent of the data and one percent reconstruction error. At least one encoder configurations had a Youden's J index higher than 0.9999 for all 30 observation types. Conclusion: Given the multiplicity of distributions for a single observation in EHR data (i.e., same observation represented with different names or units), as well as non-linearity of human observations, encoding offers huge promises for outlier detection in large-scale data repositories. https://github.com/hestiri/superencoder.
MeSH term(s)	Algorithms ; Data Collection ; Data Interpretation, Statistical ; Electronic Health Records ; Female ; Humans ; Male ; Medical Informatics/methods ; Models, Statistical ; Neural Networks, Computer ; ROC Curve ; Registries ; Reproducibility of Results
Language	English
Publishing date	2019-01-12
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2019.01.002
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Article ; Online: Polar labeling: silver standard algorithm for training disease classifiers.

Wagholikar, Kavishwar B / Estiri, Hossein / Murphy, Marykate / Murphy, Shawn N

Bioinformatics (Oxford, England)

2020 Volume 36, Issue 10, Page(s) 3200–3206

Abstract: Motivation: Expert-labeled data are essential to train phenotyping algorithms for cohort identification. However expert labeling is time and labor intensive, and the costs remain prohibitive for scaling phenotyping to wider use-cases.: Results: We ... ...

Abstract	Motivation: Expert-labeled data are essential to train phenotyping algorithms for cohort identification. However expert labeling is time and labor intensive, and the costs remain prohibitive for scaling phenotyping to wider use-cases. Results: We present an approach referred to as polar labeling (PL), to create silver standard for training machine learning (ML) for disease classification. We test the hypothesis that ML models trained on the silver standard created by applying PL on unlabeled patient records, are comparable in performance to the ML models trained on gold standard, created by clinical experts through manual review of patient records. We perform experimental validation using health records of 38 023 patients spanning six diseases. Our results demonstrate the superior performance of the proposed approach. Availability and implementation: We provide a Python implementation of the algorithm and the Python code developed for this study on Github. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Color ; Humans ; Machine Learning
Language	English
Publishing date	2020-02-07
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa088
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Article ; Online: Research data warehouse best practices: catalyzing national data sharing through informatics innovation.

Murphy, Shawn N / Visweswaran, Shyam / Becich, Michael J / Campion, Thomas R / Knosp, Boyd M / Melton-Meaux, Genevieve B / Lenert, Leslie A

Journal of the American Medical Informatics Association : JAMIA

2022 Volume 29, Issue 4, Page(s) 581–584

MeSH term(s)	Data Warehousing ; Information Dissemination ; Medical Informatics
Language	English
Publishing date	2022-03-14
Publishing country	England
Document type	Editorial
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocac024
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Article ; Online: MicroRNAs targeting TGF-β signaling exacerbate central nervous system autoimmunity by disrupting regulatory T cell development and function.

Rau, Christina N / Severin, Mary E / Lee, Priscilla W / Deffenbaugh, Joshua L / Liu, Yue / Murphy, Shawn P / Petersen-Cherubini, Cora L / Lovett-Racke, Amy E

European journal of immunology

2024 , Page(s) e2350548

Abstract: Transforming growth factor beta (TGF-β) signaling is essential for a balanced immune response by mediating the development and function of regulatory T cells (Tregs) and suppressing autoreactive T cells. Disruption of this balance can result in ... ...

Abstract	Transforming growth factor beta (TGF-β) signaling is essential for a balanced immune response by mediating the development and function of regulatory T cells (Tregs) and suppressing autoreactive T cells. Disruption of this balance can result in autoimmune diseases, including multiple sclerosis (MS). MicroRNAs (miRNAs) targeting TGF-β signaling have been shown to be upregulated in naïve CD4 T cells in MS patients, resulting in a limited in vitro generation of human Tregs. Utilizing the murine model experimental autoimmune encephalomyelitis, we show that perinatal administration of miRNAs, which target the TGF-β signaling pathway, enhanced susceptibility to central nervous system (CNS) autoimmunity. Neonatal mice administered with these miRNAs further exhibited reduced Treg frequencies with a loss in T cell receptor repertoire diversity following the induction of experimental autoimmune encephalomyelitis in adulthood. Exacerbated CNS autoimmunity as a result of miRNA overexpression in CD4 T cells was accompanied by enhanced Th1 and Th17 cell frequencies. These findings demonstrate that increased levels of TGF-β-associated miRNAs impede the development of a diverse Treg population, leading to enhanced effector cell activity, and contributing to an increased susceptibility to CNS autoimmunity. Thus, TGF-β-targeting miRNAs could be a risk factor for MS, and recovering optimal TGF-β signaling may restore immune homeostasis in MS patients.
Language	English
Publishing date	2024-04-18
Publishing country	Germany
Document type	Journal Article
ZDB-ID	120108-6
ISSN	1521-4141 ; 0014-2980
ISSN (online)	1521-4141
ISSN	0014-2980
DOI	10.1002/eji.202350548
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