This study explores the creation of a machine learning model to automatically identify whether a Neonatal Intensive Care Unit (NICU) patient was diagnosed with neonatal jaundice during a particular hospitalization based on their associated clinical notes. We develop a number of techniques for text preprocessing and feature selection and compare the effectiveness of different classification models. We show that using ensemble decision tree classification, both with AdaBoost and with bagging, outperforms support vector machines (SVM), the current state-of-the-art technique for neonatal jaundice coding.
At least one previous attempt has been made to build an automatic coding model for neonatal jaundice; @cite_10 uses an SVM on the text of clinical notes. Our research improves on this model by employing an additional preprocessing step and using ensemble classification methods. We explore and compare these alternative classification techniques and analyze their efficacy.
@cite_10
2106107776
Background Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times. @PARASPLIT Objective To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment. @PARASPLIT Materials and methods We selected notes from 20012008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx). @PARASPLIT Results Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task. @PARASPLIT Discussion Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and proc