{'fraction_non_alphanumeric': 0.015604681404421327, 'fraction_numerical': 0.0039011703511053317, 'mean_word_length': 5.062992125984252, '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}}
Application of Hidden Markov Model to Identify Disease Progression Process in Medical Research

Disease progression process means a change of disease stages during the times (1). Identifying how is progress the disease and the number of its stages can be very helpful to increase correct recognition and appropriate prescribing of medications in different disease treatments. Markov models are a ubiquitous and unique statistical model which can modeling different stages of diseases during time and identify the disease progression process (Fig. 1). The basic assumption of these models is a Markov property, it means that the current stage of the disease must be depend like a chain only on the previous time stage not on other stages that occurred earlier times (2).