Predictive Modeling

/Tag:Predictive Modeling

Abstract Number: Oral

USING PREDICTIVE MODELING TO IDENTIFY EXCESS VITAL SIGN ASSESSMENT IN HOSPITALIZED PATIENTS

Background: Clinically stable inpatients may receive potentially unnecessary care, such as overnight vital sign assessment. Nighttime vital signs can disrupt sleep and adversely affect patient satisfaction and contribute to delirium. However, it may be difficult [...]

By | 2019-03-12T15:48:40+00:00 March 11th, 2019|Hospital Medicine 2019, Innovations, Oral Presentations, Technology in Hospital Medicine|Comments Off on USING PREDICTIVE MODELING TO IDENTIFY EXCESS VITAL SIGN ASSESSMENT IN HOSPITALIZED PATIENTS

Abstract Number: 393

DEPLOYMENT OF SEPSIS WATCH, A DEEP LEARNING SEPSIS DETECTION AND TREATMENT PLATFORM

Background: Sepsis is one of the top causes of inpatient mortality and rapid detection presents numerous challenges. In March, 2016, an interdisciplinary team consisting of top clinicians, data scientists and machine learning experts at a [...]

By | 2019-03-11T14:25:18+00:00 March 11th, 2019|Hospital Medicine 2019, Innovations, Technology in Hospital Medicine|Comments Off on DEPLOYMENT OF SEPSIS WATCH, A DEEP LEARNING SEPSIS DETECTION AND TREATMENT PLATFORM

HM2018 Abstract Number: 138

A STATISTICAL ANALYSIS OF METHODOLOGIES FOR THE REAL-TIME IDENTIFICATION OF PATIENTS WITH ACUTE EXACERBATIONS OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE

Background: Chronic Obstructive Pulmonary Disease (COPD) is a lung disease characterized by chronic, irreversible airway obstruction that can precipitate into acute exacerbations (AECOPD) of cough, dyspnea and sputum production, often requiring hospitalization. Hospital systems aiming [...]

By | 2018-03-19T12:53:33+00:00 March 19th, 2018|Hospital Medicine 2018, Outcomes Research, Research|Comments Off on A STATISTICAL ANALYSIS OF METHODOLOGIES FOR THE REAL-TIME IDENTIFICATION OF PATIENTS WITH ACUTE EXACERBATIONS OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE