Machine-learning

/Tag:Machine-learning

Abstract Number: 186

UTILIZATION OF WEATHER AND CALENDER DATA IN PREDICTING DAILY NUMBER OF HOSPITALIZED PATIENTS-APPLICATION OF CONVENTIONAL REGRESSION ANALYSIS AND MACHINE LEARNING.

Background: The number of hospitalized patients varies significantly throughout the year. Fine prediction of the number of hospitalized patients is essential to provide adequate staffing and quality care. We aimed to identify the factors affecting [...]

By | 2019-03-18T17:59:41-04:00 March 18th, 2019|Hospital Medicine 2019, Outcomes Research, Research|Comments Off on UTILIZATION OF WEATHER AND CALENDER DATA IN PREDICTING DAILY NUMBER OF HOSPITALIZED PATIENTS-APPLICATION OF CONVENTIONAL REGRESSION ANALYSIS AND MACHINE LEARNING.

Abstract Number: Oral

DERIVATION AND VALIDATION OF A COPD READMISSION RISK PREDICTION TOOL

Background: Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of hospital readmissions in America. Over one-third of patients admitted for COPD exacerbations are readmitted within 90 days, with readmissions costing $15 billion. Medicare [...]

By | 2019-03-12T15:46:24-04:00 March 11th, 2019|Hospital Medicine 2019, Oral Presentations, Research, Technology in Hospital Medicine|Comments Off on DERIVATION AND VALIDATION OF A COPD READMISSION RISK PREDICTION TOOL

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 | 2019-03-11T14:25:18-04:00 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: Plenary presentation

DEEPLY-PERSONALIZED MEDICINE: BRINGING DEEP LEARNING TO SEPSIS CARE

Background: Clinical decision support tools based on predictive analytics can provide actionable information and improve clinical outcomes for patients at risk of developing sepsis. Scoring systems such as Systemic Inflammatory Response Syndrome (SIRS) and National [...]

By | 2018-03-29T15:34:47-04:00 March 29th, 2018|Hospital Medicine 2018, Plenary Presentations|Comments Off on DEEPLY-PERSONALIZED MEDICINE: BRINGING DEEP LEARNING TO SEPSIS CARE

HM2017 Abstract Number: 231

IMPLEMENTATION AND IMPACT OF AN AUTOMATED EARLY WARNING SYSTEM TO PREDICT SEPSIS

Background: Sepsis is a leading cause of death among hospitalized patients. Early detection of sepsis has the potential to reduce mortality by facilitating timely evidence-based interventions. Past studies have used electronic health records (EHR) to [...]

By | 2017-04-26T02:44:35-04:00 April 20th, 2017|Research Abstracts, Technology in Hospital Medicine|Comments Off on IMPLEMENTATION AND IMPACT OF AN AUTOMATED EARLY WARNING SYSTEM TO PREDICT SEPSIS