{'fraction_non_alphanumeric': 0.024975984630163303, 'fraction_numerical': 0.007684918347742555, 'mean_word_length': 5.6369426751592355, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Machine Learning Techniques for Predicting Hospital Length of Stay in Pennsylvania Federal and Specialty Hospitals

In this paper, we compare three different machine learning techniques for predicting length of stay (LOS) in Pennsylvania Federal and Specialty hospitals. Using the real-world data on 88 hospitals, we compare the performances of three different machine learning techniques—Classification and Regression Tree (CART), Chi-Square Automatic Interaction Detection (CHAID) and Support Vector Regression (SVR)—and find that there is no significant difference in performances of these three techniques. However, CART provides a decision tree that is easy to understand and interpret. The results from CART indicate that psychiatric care hospitals typically have higher LOS than nonpsychiatric care hospitals. For non-psychiatric care hospitals, the LOS depends on hospital capacity (beds staffed) with larger hospitals with beds staffed over 329 having average LOS of 13 weeks vs. smaller hospitals with average LOS of about 3 weeks.