HAC Machine Learning in Healthcare 2

Question 1

Which evaluation metric is most appropriate when assessing a rare-disease diagnostic model where false negatives carry severe clinical consequences?

Accepted Answer

Recall (Sensitivity)

Answer

Recall (sensitivity) measures the proportion of actual positives correctly identified, making it critical when missing a true case (false negative) is clinically dangerous.

Question 2

In a clinical NLP pipeline, 'named entity recognition' (NER) is primarily used to:

Accepted Answer

Extract structured entities like diagnoses and medications from free text

Answer

NER identifies and classifies named entities (e.g., drug names, diagnoses, lab values) within unstructured clinical text.

Question 3

A hospital wants to predict 30-day readmission. The dataset has 5% readmitted patients. Which preprocessing strategy best addresses class imbalance?

Accepted Answer

Apply SMOTE to oversample the minority class

Answer

SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic minority-class examples to balance the dataset without simply duplicating records.

Question 4

Transfer learning in medical imaging typically involves:

Accepted Answer

Fine-tuning a model pre-trained on large non-medical image datasets

Answer

Transfer learning reuses weights from models trained on large datasets (e.g., ImageNet) and fine-tunes them on smaller medical imaging datasets.

Question 5

Which data splitting strategy is most appropriate for a time-series patient vitals prediction model to prevent data leakage?

Accepted Answer

Chronological (temporal) train/test split

Answer

A chronological split ensures the model is trained on past data and tested on future data, mimicking real deployment and preventing leakage of future information.

(HAC) Healthcare Analytics Certification Practice Test

(HAC) Healthcare Analytics Certification Practice Test

HAC Machine Learning in Healthcare 2