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An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU

Critical care medicine, 2018-04, Vol.46 (4), p.547-553

2018

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eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19

PloS one, 2021-09, Vol.16 (9), p.e0257056-e0257056

2021

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Facilitating the Next Paradigm Shift in Critical Care Through Artificial Intelligence

Critical care clinics, 2023-10, Vol.39 (4), p.xiii-xiv

2023

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The Big Consequences of Small Discrepancies: Why Racial Differences in Pulse Oximetry Errors Matter

Critical care medicine, 2022-02, Vol.50 (2), p.335-337

2022

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Should AI models be explainable to clinicians?

Critical care (London, England), 2024-09, Vol.28 (1), p.301, Article 301

2024

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Leveraging clinical data across healthcare institutions for continual learning of predictive risk models

Scientific reports, 2022-05, Vol.12 (1), p.8380-8380, Article 8380

2022

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Fluid Response Evaluation in Sepsis Hypotension and Shock: A Randomized Clinical Trial

Chest, 2020-10, Vol.158 (4), p.1431-1445

2020

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A Locally Optimized Data-Driven Tool to Predict Sepsis-Associated Vasopressor Use in the ICU

Critical care medicine, 2021-12, Vol.49 (12), p.e1196-e1205

2021

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Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality

JAMA network open, 2021-11, Vol.4 (11), p.e2131674-e2131674

2021

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Using what you get: dynamic physiologic signatures of critical illness

Critical care clinics, 2015-01, Vol.31 (1), p.133-164

2015

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HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF)

Physiological measurement, 2023-10, Vol.44 (10), p.105006

2023

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Predictors of early progression to severe sepsis or shock among emergency department patients with nonsevere sepsis

International journal of emergency medicine, 2016-12, Vol.9 (1), Article 10

2016

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Serial Daily Organ Failure Assessment Beyond ICU Day 5 Does Not Independently Add Precision to ICU Risk-of-Death Prediction

Critical care medicine, 2017-12, Vol.45 (12), p.2014-2022

2017

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Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome

Frontiers in big data, 2020-11, Vol.3, p.579774-579774

2020

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Serial Measurements of Protein Biomarkers in Sepsis-Induced Acute Respiratory Distress Syndrome

Critical care explorations, 2022-10, Vol.4 (10), p.e0780-e0780

2022

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Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19

Critical care explorations, 2024-03, Vol.6 (3), p.e1059

2024

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Applied Physiology at the Bedside to Drive Resuscitation Algorithms

Journal of cardiothoracic and vascular anesthesia, 2014-12, Vol.28 (6), p.1642-1659

2014

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A dream deferred: the rise and fall of recombinant activated protein C

Critical care (London, England), 2013-01, Vol.17 (2), p.309-309

2013

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A Delphi Process to Identify Relevant Outcomes That May Be Associated With a Predictive Analytic Tool to Detect Hemodynamic Deterioration in the Intensive Care Unit

Curēus (Palo Alto, CA), 2023-12

2023

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Inhaled Carbon Monoxide Protects against the Development of Shock and Mitochondrial Injury following Hemorrhage and Resuscitation

PloS one, 2015-09, Vol.10 (9), p.e0135032

2015

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Increasing Cardiovascular Data Sampling Frequency and Referencing It to Baseline Improve Hemorrhage Detection

Critical care explorations, 2019-10, Vol.1 (10), p.e0058-e0058

2019

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Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data

Critical care explorations, 2021-05, Vol.3 (5), p.e0402-e0402

2021

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Data Science in Critical Care

Critical care clinics, 2023-10, Vol.39 (4), p.i-i

2023

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Overt and Occult Hypoxemia in Patients Hospitalized With COVID-19

Critical care explorations, 2023-01, Vol.5 (1), p.e0825

2023

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Using What You Get

Critical care clinics, 2015, Vol.31 (1), p.133-164

2015

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An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19

Facilitating the Next Paradigm Shift in Critical Care Through Artificial Intelligence

The Big Consequences of Small Discrepancies: Why Racial Differences in Pulse Oximetry Errors Matter

Should AI models be explainable to clinicians?

Leveraging clinical data across healthcare institutions for continual learning of predictive risk models

Fluid Response Evaluation in Sepsis Hypotension and Shock: A Randomized Clinical Trial

A Locally Optimized Data-Driven Tool to Predict Sepsis-Associated Vasopressor Use in the ICU

Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality

Using what you get: dynamic physiologic signatures of critical illness

HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF)

Predictors of early progression to severe sepsis or shock among emergency department patients with nonsevere sepsis

Serial Daily Organ Failure Assessment Beyond ICU Day 5 Does Not Independently Add Precision to ICU Risk-of-Death Prediction

Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome

Serial Measurements of Protein Biomarkers in Sepsis-Induced Acute Respiratory Distress Syndrome

Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19

Applied Physiology at the Bedside to Drive Resuscitation Algorithms

A dream deferred: the rise and fall of recombinant activated protein C

A Delphi Process to Identify Relevant Outcomes That May Be Associated With a Predictive Analytic Tool to Detect Hemodynamic Deterioration in the Intensive Care Unit

Inhaled Carbon Monoxide Protects against the Development of Shock and Mitochondrial Injury following Hemorrhage and Resuscitation

Increasing Cardiovascular Data Sampling Frequency and Referencing It to Baseline Improve Hemorrhage Detection

Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data

Data Science in Critical Care

Overt and Occult Hypoxemia in Patients Hospitalized With COVID-19

Using What You Get

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