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Leveraging electronic health records from two hospital systems identifies male infertility-associated comorbidities across time

Woldemariam et al. identify male infertility associated comorbidities using electronic health records (EHR) from the University of California. Findings, which are validated in Stanford University’s EHR, include hypothyroidism and anemia.

Sarah R. Woldemariam
Feng Xie
Marina Sirota
ResearchOpen Access01 Sept 2025
Communications Medicine

Volume: 5, P: 1-19
Similarities and differences in Alzheimer’s dementia comorbidities in racialized populations identified from electronic medical records

Woldemariam et al. use electronic medical records to explore comorbidities in individuals with Alzheimer’s Disease stratified by four identified race and ethnicity categories. Whilst most comorbidities are similar, a few comorbidities, including respiratory diseases, are associated with Black- and Latine- identified individuals.

Sarah R. Woldemariam
Alice S. Tang
Marina Sirota
ResearchOpen Access08 Apr 2023
Communications Medicine

Volume: 3, P: 1-14
Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations

Sex modifies Alzheimer’s Disease vulnerability, but the reasons for this are largely unknown. Here, the authors utilize two independent electronic medical record systems to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations.

Alice S. Tang
Tomiko Oskotsky
Marina Sirota
ResearchOpen Access03 Feb 2022
Nature Communications

Volume: 13, P: 1-15
Comorbidities associated with a clinically-recognized delirium diagnosis in the hospital using real world data

Kodama et al. use electronic health record data of patients with and without a delirium diagnosis to identify the comorbidities and laboratory values associated with inpatient delirium diagnoses. Longitudinal analysis also show that an inpatient delirium diagnosis is associated with an increased risk of mortality independent of comorbidity burden.

Lay Kodama
Sarah R. Woldemariam
Marina Sirota
ResearchOpen Access22 Jul 2025
Communications Medicine

Volume: 5, P: 1-12
Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights

Identifying individuals at risk of developing Alzheimer’s disease is an important task. Here Tang et al. leverage electronic health records to predict Alzheimer’s disease onset, and utilize knowledge networks to prioritize shared genes behind the clinical data as well as facilitate contextualization based on sex.

Alice S. Tang
Katherine P. Rankin
Marina Sirota
ResearchOpen Access21 Feb 2024
Nature Aging

Volume: 4, P: 379-395
Harnessing EHR data for health research

Electronic health records hold immense potential for providing clinically useful insights for populations and individuals; this Review summarizes the opportunities and challenges, with an emphasis on recognizing and reducing bias.

Alice S. Tang
Sarah R. Woldemariam
Marina Sirota
Reviews04 Jul 2024
Nature Medicine

Volume: 30, P: 1847-1855
Antidepressant use during pregnancy and the risk of preterm birth – a cohort study

Guy Amit
Chen Yanover
Marina Sirota
ResearchOpen Access20 Feb 2024
npj Women's Health

Volume: 2, P: 1-7

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Leveraging electronic health records from two hospital systems identifies male infertility-associated comorbidities across time

Similarities and differences in Alzheimer’s dementia comorbidities in racialized populations identified from electronic medical records

Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations

Comorbidities associated with a clinically-recognized delirium diagnosis in the hospital using real world data

Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights

Harnessing EHR data for health research

Antidepressant use during pregnancy and the risk of preterm birth – a cohort study

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