An Electronic Health Record is a digitalized collection of a patient’s clinical history. While usually, researchers have focused on analyzing structured data contained in a patient’s Electronic Health Record, like laboratory test results or physical measures, recent studies are leveraging the unstruc- tured textual data contained in them. In this thesis, sentiment analysis techniques are applied to nursing notes in order to analyze and classify whether a patient will die within the first 80 days post-first hospital admission based on the emotional tone and positive/negative words detected in his/her admission note. To this aim, topic modeling, a dictionary-based classification approach, a Random Forest, a Multinomial Inverse Regression, and a logistic regression model are fitted. The main benefit of using various techniques is to capture different nuances of the notes written by health practitioners. The dataset analyzed is the MIMIC-III ICU (“Medical Information Mart for Intensive Care”) database, which describes patients admitted to the Beth Israel Deaconess Medical Center between 2001 and 2012.

Text mining techniques applied on nursing notes to predict 80 days post first admission mortality

Rosolin, Gaia
2023/2024

Abstract

An Electronic Health Record is a digitalized collection of a patient’s clinical history. While usually, researchers have focused on analyzing structured data contained in a patient’s Electronic Health Record, like laboratory test results or physical measures, recent studies are leveraging the unstruc- tured textual data contained in them. In this thesis, sentiment analysis techniques are applied to nursing notes in order to analyze and classify whether a patient will die within the first 80 days post-first hospital admission based on the emotional tone and positive/negative words detected in his/her admission note. To this aim, topic modeling, a dictionary-based classification approach, a Random Forest, a Multinomial Inverse Regression, and a logistic regression model are fitted. The main benefit of using various techniques is to capture different nuances of the notes written by health practitioners. The dataset analyzed is the MIMIC-III ICU (“Medical Information Mart for Intensive Care”) database, which describes patients admitted to the Beth Israel Deaconess Medical Center between 2001 and 2012.
2023-10-27
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14247/4238