Table 1.
Class proportions for brain compression and edema.
Fig 1.
Example of frequency and TF-IDF tokenization strategies illustrating how TF-IDF controls for words that frequently occur in the corpus.
TF-IDF = Term Frequency Inverse Document Frequency.
Fig 2.
Machine learning and deep learning model performance on brain compression data.
(A) Machine learning classifiers’ performance methods with both frequency (TF) and term frequency-inverse document frequency (TFIDF) tokenization strategies. (B) Deep learning classifiers’ performance methods with both frequency and term frequency-inverse document frequency (TFIDF) tokenization strategies. SVM = support vector machine; NB = Naïve bayes; Log = Logistic regression.
Fig 3.
Machine learning and deep learning model performance on brain edema data.
(A) Machine learning classifiers’ performance methods with both frequency (TF) and term frequency-inverse document frequency (tfidf) tokenization strategies. (B) Deep learning classifiers’ performance methods with both frequency and term frequency-inverse document frequency (TFIDF) tokenization strategies. SVM = support vector machine; NB = Naïve bayes; Log = Logistic regression.
Table 2.
Demographic and diagnostic characteristics for provider comparison cohort.
Fig 4.
Receiver operating characteristic (ROC) curves for random forest classifier with TF-IDF tokenization.
(A) Estimator trained for brain compression classification. (B) Estimator trained for brain edema classification. AUC = area under the curve.
Table 3.
A. Performance Metrics for ML and DL Models on Brain Compression. B. Performance Metrics for ML and DL Models on Brain Edema.
Fig 5.
Machine learning estimator and provider documentation comparison.
(A) Estimators for compression dataset. (B) Estimators for edema dataset. SVM = support vector machine; NB = Naïve bayes; Log = Logistic regression.
Table 4.
A. Estimator and Provider Performance Comparison for Brain Compression. B. Estimator and Provider Performance Comparison for Brain Compression.