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Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock

Critical care medicine, 2019-11, Vol.47 (11), p.1477-1484

2019

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Economic impacts of non-native forest insects in the continental United States

PloS one, 2011-09, Vol.6 (9), p.e24587-e24587

2011

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Sparse multi-output Gaussian processes for online medical time series prediction

BMC medical informatics and decision making, 2020-07, Vol.20 (1), p.152-152, Article 152

2020

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Oncologist phenotypes and associations with response to a machine learning-based intervention to increase advance care planning: Secondary analysis of a randomized clinical trial

PloS one, 2022-05, Vol.17 (5), p.e0267012-e0267012

2022

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Rising complexity and falling explanatory power in ecology

Frontiers in ecology and the environment, 2014-09, Vol.12 (7), p.412-418

2014

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Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations

PloS one, 2021-06, Vol.16 (6), p.e0252585-e0252585

2021

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Behavioral economic implementation strategies to improve serious illness communication between clinicians and high-risk patients with cancer: protocol for a cluster randomized pragmatic trial

Implementation science : IS, 2021-09, Vol.16 (1), p.90-90, Article 90

2021

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Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach

Journal of personalized medicine, 2022-04, Vol.12 (5), p.661

2022

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Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

Annals of internal medicine, 2020-07, Vol.173 (1), p.21-28

2020

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A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice

Critical care medicine, 2019-11, Vol.47 (11), p.1485-1492

2019

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Predicting invasions: alternative models of human-mediated dispersal and interactions between dispersal network structure and Allee effects

The Journal of applied ecology, 2012-10, Vol.49 (5), p.1113-1123

2012

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Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

Annals of internal medicine, 2020-10, Vol.173 (8), p.680-681

2020

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Electronic Health Record Mortality Prediction Model for Targeted Palliative Care Among Hospitalized Medical Patients: a Pilot Quasi-experimental Study

Journal of general internal medicine : JGIM, 2019-09, Vol.34 (9), p.1841-1847

2019

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Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning

American journal of obstetrics and gynecology, 2024-04

2024

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Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic

Journal of the American Medical Informatics Association : JAMIA, 2023-01, Vol.30 (2), p.348-354

2023

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Predicting invasions: alternative models of human‐mediated dispersal and interactions between dispersal network structure and A llee effects

The Journal of applied ecology, 2012-10, Vol.49 (5), p.1113-1123

2012

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Using electronic health records and claims data to identify high-risk patients likely to benefit from palliative care

The American journal of managed care, 2021-01, Vol.27 (1), p.e7-e15

2021

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Importing risk: quantifying the propagule pressure-establishment relationship at the pathway level

Diversity & distributions, 2013-08, Vol.19 (8), p.1020-1030

2013

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Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis

BMJ quality & safety, 2023-09, Vol.32 (9), p.503-516

2023

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Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer

JAMA network open, 2019-10, Vol.2 (10), p.e1915997-e1915997

2019

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Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial

JAMA oncology, 2020-12, Vol.6 (12), p.e204759

2020

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Oncologist phenotypes and associations with response to a behavioral intervention to increase serious illness conversations

Journal of clinical oncology, 2021-05, Vol.39 (15_suppl), p.12126-12126

2021

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Augmenting machine learning algorithms to predict mortality using patient-reported outcomes in oncology

Journal of clinical oncology, 2021-05, Vol.39 (15_suppl), p.1510-1510

2021

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Long-term effect of machine learning–triggered behavioral nudges on serious illness communication and end-of-life outcomes among patients with cancer: A randomized clinical trial

Journal of clinical oncology, 2022-06, Vol.40 (16_suppl), p.109-109

2022

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Palliative Connect: Triggered Palliative Care Consultation Using an EHR Prediction Model (FR420A)

Journal of pain and symptom management, 2019-02, Vol.57 (2), p.408-409

2019

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Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock

Economic impacts of non-native forest insects in the continental United States

Sparse multi-output Gaussian processes for online medical time series prediction

Oncologist phenotypes and associations with response to a machine learning-based intervention to increase advance care planning: Secondary analysis of a randomized clinical trial

Rising complexity and falling explanatory power in ecology

Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations

Behavioral economic implementation strategies to improve serious illness communication between clinicians and high-risk patients with cancer: protocol for a cluster randomized pragmatic trial

Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice

Predicting invasions: alternative models of human-mediated dispersal and interactions between dispersal network structure and Allee effects

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

Electronic Health Record Mortality Prediction Model for Targeted Palliative Care Among Hospitalized Medical Patients: a Pilot Quasi-experimental Study

Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning

Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic

Predicting invasions: alternative models of human‐mediated dispersal and interactions between dispersal network structure and A llee effects

Using electronic health records and claims data to identify high-risk patients likely to benefit from palliative care

Importing risk: quantifying the propagule pressure-establishment relationship at the pathway level

Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis

Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer

Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial

Oncologist phenotypes and associations with response to a behavioral intervention to increase serious illness conversations

Augmenting machine learning algorithms to predict mortality using patient-reported outcomes in oncology

Long-term effect of machine learning–triggered behavioral nudges on serious illness communication and end-of-life outcomes among patients with cancer: A randomized clinical trial

Palliative Connect: Triggered Palliative Care Consultation Using an EHR Prediction Model (FR420A)

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