{'fraction_non_alphanumeric': 0.014184397163120567, 'fraction_numerical': 0.0, 'mean_word_length': 5.086206896551724, '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}}
Identification of Disease States for Trauma Patients using Commonly Available Hospital Data

Trauma is one of the main causes of hospitalization. Time is of the essence in diagnosis and treatment of trauma patients with severe injuries. To assist in decision-making, we propose a hidden Markov model for identification of disease states through which patients progress. An important property of our model is that it is based on features which are routinely collected in hospital trauma centers. Using a hidden Markov model based on fifteen features, six different patient states are identified. The resulting Markov model can be useful in identifying patients’ states to assist in diagnosis and treatment.