CHINMAY BERA

Assistant Professor

University of Arkansas for Medical Sciences

United States

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Chinmay Bera is an Assistant Professor at the University of Arkansas for Medical Sciences in the United States. His research focuses on the application of machine learning in predicting clinical outcomes in patients with liver disease, particularly cirrhosis and metabolic dysfunction-associated steatotic liver disease. Recent publications have explored various aspects of liver health, including prognostication, non-invasive assessment techniques, and the impact of microbiome and metabolomics on clinical biomarkers.

Articles (10)

Year:

2025

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2024

Year:

2023

Collaborators (2)

Matthew Kappus

UNITED STATES

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Iliana Mani

Second Academic Department of Internal Medicine University of Athens

GREECE

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CHINMAY BERA

Research Interests

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Articles (10)

Enhancement of Inpatient Mortality Prognostication With Machine Learning in a Prospective Global Cohort of Patients With Cirrhosis With External Validation.

Salivary microbiome and serum metabolomics add to clinical biomarkers to predict 6-month hospitalizations in a multicenter cirrhosis outpatient cohort.

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Acute kidney injury incidence and clinical features: Refractory versus non-refractory ascites.

Validation of FIB-4 for the Diagnosis of Liver Cirrhosis in Metabolic Dysfunction-Associated Steatotic Liver Disease.

Comparison of the magnetic resonance elastography and FIB-4 (MEFIB) Index and vibration-controlled transient elastography for significant metabolic dysfunction–associated steatotic liver disease fibrosis

Global prevalence and characteristics of infections and clinical outcomes in hospitalised patients with cirrhosis: a prospective cohort study for the CLEARED Consortium

Long-Term Survival of Canadian Liver Transplant Recipients Remains Stagnant— A Cohort Study on the Evolution of Cause-Specific Mortality

Non-Invasive Assessment of Liver Fibrosis in Hepatitis B Patients

Predictors of Respiratory Failure Development in a Multicenter Cohort of Inpatients With Cirrhosis

Collaborators (2)

Matthew Kappus

Iliana Mani

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CHINMAY BERA

Research Interests

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Contact this professor

About

Articles (10)

Enhancement of Inpatient Mortality Prognostication With Machine Learning in a Prospective Global Cohort of Patients With Cirrhosis With External Validation.

Salivary microbiome and serum metabolomics add to clinical biomarkers to predict 6-month hospitalizations in a multicenter cirrhosis outpatient cohort.

Machine learning models using non-invasive tests &amp; B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Acute kidney injury incidence and clinical features: Refractory versus non-refractory ascites.

Validation of FIB-4 for the Diagnosis of Liver Cirrhosis in Metabolic Dysfunction-Associated Steatotic Liver Disease.

Comparison of the magnetic resonance elastography and FIB-4 (MEFIB) Index and vibration-controlled transient elastography for significant metabolic dysfunction–associated steatotic liver disease fibrosis

Global prevalence and characteristics of infections and clinical outcomes in hospitalised patients with cirrhosis: a prospective cohort study for the CLEARED Consortium

Long-Term Survival of Canadian Liver Transplant Recipients Remains Stagnant— A Cohort Study on the Evolution of Cause-Specific Mortality

Non-Invasive Assessment of Liver Fibrosis in Hepatitis B Patients

Predictors of Respiratory Failure Development in a Multicenter Cohort of Inpatients With Cirrhosis

Collaborators (2)

Matthew Kappus

Iliana Mani

Make your next move.

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.