{'fraction_non_alphanumeric': 0.018357487922705314, 'fraction_numerical': 0.0, 'mean_word_length': 5.434782608695652, '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}}
Using Latent Variable Autoregression to Monitor the Health of Individuals with Congestive Heart Failure

Sudden weight gain in patients living with Congestive Heart Failure (CHF) is often an indication that the individual is retaining fluid, which often means that patient's heart has weakened leading to increased risk of kidney or cardiac failure. Clinical interventions can be made at this stage, leading to better outcomes, however it is essential that the interventions take place before the patient's health declines too drastically. In this work, we present a latent variable autoregression model that tracks patient weight and blood pressure over time, allowing us to predict weight values into the future. We are also able to model continuous heart-rate signals and evaluate a subject's response to physical activity. This allows us to detect signs of health decline days earlier than existing rule-based systems, leading to the possibility of earlier clinical interventions, potentially preventing deadly medical emergencies.