{'fraction_non_alphanumeric': 0.025221540558963872, 'fraction_numerical': 0.0027266530334014998, 'mean_word_length': 5.43859649122807, '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}}
Transfer and Collaborative Learning Method for Personalized Noninvasive Blood Glucose Measurement Modeling

Non-invasive Glucose Measurement (NGM) technology is promising and desired for patients with hyperglycemia or hypoglycemia. In various kinds of NGM technologies, a prediction algorithm model plays a special role that is to map a group of physical signals to a glucose level of a person at a given time. Unfortunately, there is no practical solution available to the public, under the circumstances of different skin color, skin thickness, physiological differences, etc. In this paper, a Transfer and Collaborative Learning (TCL) method for personalized NGM modeling is proposed, and an Artificial Neural Network Model with TCL (ANN-TCL) is established for predicting glucose concentration, in which model parameters can be tuned according to individual physiological conditions. To verify performance of the proposal, it is embedded into our developed NGM system, and compared with alternate solutions. Clinical trials in the PLA NAVY General Hospital demonstrate that this proposal can reduce the impact of individual discrepancies (IDs), and achieve expected results (R2=0.82) for all test subjects. Obviously, it is helpful to each patient with our NGM system for the purpose of glucose self-monitoring. The unique gain benefits from a special design of the self-learning strategy which can fully take advantage of both universal and personal information.