عنوان

Prioritizing Hospital Incident Reports Through Text Classification

Number

TL51487

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Prioritizing Hospital Incident Reports Through Text Classification

General Material Designation

[Thesis]

First Statement of Responsibility

Flood, Max

Subsequent Statement of Responsibility

Madathil, Sreenath C.

Name of Publisher, Distributor, etc.

State University of New York at Binghamton

Date of Publication, Distribution, etc.

2019

Text of Note

99 p.

Dissertation or thesis details and type of degree

M.S.

Body granting the degree

State University of New York at Binghamton

Text preceding or following the note

2019

Text of Note

This research explores the potential for natural language processing and binary text classification to identify and prioritize medical incident reports. The study addresses the concerns of how to efficiently process incident reports while detecting cases that yield potential for patient safety initiatives. Over 30,000 incident reports were used to build a prediction model in order to classify each incident as requiring a formal investigation or not. The prediction model implemented 10 folds cross-validation, TF-IDF bag of words, chi-squared feature selection, random under sampling, and multinomial naïve Bayes to avoid overfitting due to the data being heavily imbalanced. The model was optimized towards recall scores rather than precision or accuracy. Classification results were: accuracy = 69%, precision = 65%, recall = 84%, roc = 78%. Results showed that with basic techniques it is possible to apply text classification to medical incident reports.

Subject Term

Electrical engineering

Subject Term

Health care management

Subject Term

Information science

Flood, Max

Madathil, Sreenath C.

State University of New York at Binghamton

Electronic name

p

[Thesis]

276903

a

Y