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Predict Hospital Readmission

Hospital readmissions carry significant financial costs and are associated with negative patient outcomes. AI could be used to predict the Readmission Risk per patient, aimed at preventing readmissions and improving outcomes for patients.

Business impact: reduce hospital readmission cost, improve treatment efficacy, save more people’s lives and money 

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1. Get Inspired

Understand the Use-case under 5 minutes

2. Know More

Get to know more Business and Technical details about the use-case (15-30 minutes)

Deeper Intro

More detailed introduction covering business and technical aspects

Business Focused

Case studies, Organizational Aspects, Return on Investment examples

Tech focused

More details on the technical aspects of the use-case

3. Do

Technical resources that will help you implement the use-case (notebooks, tutorials..)

Data Sets

Data Sets you can use to build Demos, POCs, or test Algorithms

Nationwide Readmissions Database (NRD)

The NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges.

Diabetes 130-US Hospitals for years 1999-2008

10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes. Information was extracted from the database for encounters that satisfied the following criteria

MIMIC-IV electronic medical record (EMR) data

Real hospital stays for patients admitted to a tertiary academic medical center in Boston, MA, USA. MIMIC-IV contains comprehensive information for each patient: lab measurements, medications, vital signs, etc


Off-the-Shelf Products using AI for Predicting Hospital Readmission

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