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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: 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: High-throughput phenotyping with temporal sequences.

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

Journal of the American Medical Informatics Association : JAMIA

2020 Volume 28, Issue 4, Page(s) 772–781

Abstract: Objective: High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational ... ...

Abstract	Objective: High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs. Materials and methods: We develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms. Results: Phenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm's classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations. Discussion: The proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease. Conclusion: Sequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.
MeSH term(s)	Algorithms ; Data Mining/methods ; Diagnosis ; Drug Therapy ; Electronic Health Records ; Humans ; Machine Learning ; Time Factors
Language	English
Publishing date	2020-12-05
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/ocaa288
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Article ; Online: Estimates of SARS-CoV-2 Omicron BA.2 Subvariant Severity in New England.

Strasser, Zachary H / Greifer, Noah / Hadavand, Aboozar / Murphy, Shawn N / Estiri, Hossein

JAMA network open

2022 Volume 5, Issue 10, Page(s) e2238354

Abstract: Importance: The SARS-CoV-2 Omicron subvariant, BA.2, may be less severe than previous variants; however, confounding factors make interpreting the intrinsic severity challenging.: Objective: To compare the adjusted risks of mortality, hospitalization, ...

Abstract	Importance: The SARS-CoV-2 Omicron subvariant, BA.2, may be less severe than previous variants; however, confounding factors make interpreting the intrinsic severity challenging. Objective: To compare the adjusted risks of mortality, hospitalization, intensive care unit admission, and invasive ventilation between the BA.2 subvariant and the Omicron and Delta variants, after accounting for multiple confounders. Design, setting, and participants: This was a retrospective cohort study that applied an entropy balancing approach. Patients in a multicenter inpatient and outpatient system in New England with COVID-19 between March 3, 2020, and June 20, 2022, were identified. Exposures: Cases were assigned as being exposed to the Delta (B.1.617.2) variant, the Omicron (B.1.1.529) variant, or the Omicron BA.2 lineage subvariants. Main outcomes and measures: The primary study outcome planned before analysis was risk of 30-day mortality. Secondary outcomes included the risks of hospitalization, invasive ventilation, and intensive care unit admissions. Results: Of 102 315 confirmed COVID-19 cases (mean [SD] age, 44.2 [21.6] years; 63 482 women [62.0%]), 20 770 were labeled as Delta variants, 52 605 were labeled as the Omicron B.1.1.529 variant, and 28 940 were labeled as Omicron BA.2 subvariants. Patient cases were excluded if they occurred outside the prespecified temporal windows associated with the variants or had minimal longitudinal data in the Mass General Brigham system before COVID-19. Mortality rates were 0.7% for Delta (B.1.617.2), 0.4% for Omicron (B.1.1.529), and 0.3% for Omicron (BA.2). The adjusted odds ratio of mortality from the Delta variant compared with the Omicron BA.2 subvariants was 2.07 (95% CI, 1.04-4.10) and that of the original Omicron variant compared with the Omicron BA.2 subvariant was 2.20 (95% CI, 1.56-3.11). For all outcomes, the Omicron BA.2 subvariants were significantly less severe than that of the Omicron and Delta variants. Conclusions and relevance: In this cohort study, after having accounted for a variety of confounding factors associated with SARS-CoV-2 outcomes, the Omicron BA.2 subvariant was found to be intrinsically less severe than both the Delta and Omicron variants. With respect to these variants, the severity profile of SARS-CoV-2 appears to be diminishing after taking into account various factors including therapeutics, vaccinations, and prior infections.
MeSH term(s)	Humans ; Female ; Adult ; SARS-CoV-2 ; COVID-19/epidemiology ; Cohort Studies ; Retrospective Studies ; New England/epidemiology
Language	English
Publishing date	2022-10-03
Publishing country	United States
Document type	Multicenter Study ; Journal Article ; Research Support, N.I.H., Extramural
ISSN	2574-3805
ISSN (online)	2574-3805
DOI	10.1001/jamanetworkopen.2022.38354
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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: 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: Predicting COVID-19 mortality with electronic medical records.

Estiri, Hossein / Strasser, Zachary H / Klann, Jeffy G / Naseri, Pourandokht / Wagholikar, Kavishwar B / Murphy, Shawn N

NPJ digital medicine

2021 Volume 4, Issue 1, Page(s) 15

Abstract: This study aims to predict death after COVID-19 using only the past medical information routinely collected in electronic health records (EHRs) and to understand the differences in risk factors across age groups. Combining computational methods and ... ...

Abstract	This study aims to predict death after COVID-19 using only the past medical information routinely collected in electronic health records (EHRs) and to understand the differences in risk factors across age groups. Combining computational methods and clinical expertise, we curated clusters that represent 46 clinical conditions as potential risk factors for death after a COVID-19 infection. We trained age-stratified generalized linear models (GLMs) with component-wise gradient boosting to predict the probability of death based on what we know from the patients before they contracted the virus. Despite only relying on previously documented demographics and comorbidities, our models demonstrated similar performance to other prognostic models that require an assortment of symptoms, laboratory values, and images at the time of diagnosis or during the course of the illness. In general, we found age as the most important predictor of mortality in COVID-19 patients. A history of pneumonia, which is rarely asked in typical epidemiology studies, was one of the most important risk factors for predicting COVID-19 mortality. A history of diabetes with complications and cancer (breast and prostate) were notable risk factors for patients between the ages of 45 and 65 years. In patients aged 65-85 years, diseases that affect the pulmonary system, including interstitial lung disease, chronic obstructive pulmonary disease, lung cancer, and a smoking history, were important for predicting mortality. The ability to compute precise individual-level risk scores exclusively based on the EHR is crucial for effectively allocating and distributing resources, such as prioritizing vaccination among the general population.
Language	English
Publishing date	2021-02-04
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-021-00383-x
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Article ; Online: High-throughput Phenotyping with Temporal Sequences

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

bioRxiv

Abstract: Objective High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs are often underutilized in developing computational ... ...

Abstract	Objective High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs are often underutilized in developing computational phenotypic definitions. The objective of this study is to develop a high-throughput phenotyping method, leveraging temporal sequential patterns of discrete events from electronic health records. Materials and Methods We develop a representation mining algorithm to extract five classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (AVR), the traditional immediate sequential patterns (SPM), the transitive sequential patterns (tSPM), as well as two hybrid classes of SPM+AVR and tSPM+AVR. A final small set of representations were selected from each class using the MSMR dimensionality reduction algorithm. Using EHR data on 10 phenotypes from Mass General Brigham Biobank, we trained regularized logistic regression algorithms, which we validated using labeled data. Results Phenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the AVR representations that are conventionally used in electronic phenotyping. Although this study only utilizes the diagnosis and medication records, the high-throughput algorithm’s classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations. Discussion The proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. A transitive sequence can offer a more accurate characterization of the phenotype, compared with its individual components. Additionally, the identified transitive sequences of a given phenotype reflect the actual lived experiences of the patients with that particular disease. Conclusion Sequential data representations provide a precise mechanism for incorporating raw EHR records into downstream Machine Learning.
Keywords	covid19
Publisher	BioRxiv
Document type	Article ; Online
DOI	10.1101/590307
Database	COVID19

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Article: Individualized Prediction of COVID-19 Adverse outcomes with MLHO

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

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

Abstract	The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of the COVID-19 adverse outcomes on individual patients could have led to better allocation of healthcare resources and more efficient targeted preventive measures. We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death from patients' past (before COVID-19 infection) medical records. MLHO is an end-to-end Machine Learning pipeline that implements iterative sequential representation mining and feature and model selection to predict health outcomes. MLHO's architecture enables a parallel and outcome-oriented calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested and leveraged to improve prediction of health outcomes. Using clinical data from a large cohort of over 14,000 patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' before-COVID health records. Overall, the best predictions were obtained from extreme and gradient boosting models. The median AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.79 and 0.83 for ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence on predicting each outcome. As COVID-19 cases are re-surging in the U.S. and around the world, a Machine Learning pipeline like MLHO is crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge in the near future.
Keywords	covid19
Publisher	ArXiv
Document type	Article
Database	COVID19

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Book ; Online: Individualized Prediction of COVID-19 Adverse outcomes with MLHO

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

2020

Abstract: 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 ... ...

Abstract	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 the 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 the 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.
Keywords	Statistics - Machine Learning ; Computer Science - Machine Learning ; Quantitative Biology - Quantitative Methods ; covid19
Subject code	006
Publishing date	2020-08-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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