{'fraction_non_alphanumeric': 0.014788732394366197, 'fraction_numerical': 0.004929577464788733, 'mean_word_length': 5.429864253393665, '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}}
Forecasting of biosensor technologies for emerging point of care and medical IoT applications using bibliometrics and patent analysis

Healthcare is beginning to embrace point of care (POC) diagnostics and medical applications that are based on the internet of things (IoT) and the ubiquitous smart phone. Advanced medical diagnostics will utilize biosensors for biological data acquisition. This paper introduces the forecasting of biosensors that have the potential to be used in POC and IoT applications. For this research three types of biosensors were selected. These are biosensors for testing of blood, saliva, and breath. Bibliometrics and patent analysis of these biosensors are used to develop technology maturity rates based on the Fisher-Pry model. The Science Citation Index (SCI) is used for bibliometrics and patent analysis is derived from global patent databases. The Fisher-Pry projections or S-curves enable insights into the maturity levels of the emerging biosensor technologies under consideration and forecasting their growth. Patent analysis based on cumulative annual patent count indicated that blood biosensors reached their technology maturity midpoint in 2009 with the midpoints of saliva and breath biosensors lagging by 8 and 14 years respectively. Bibliometrics with annual publication count did not appear to provide much value in forecasting the maturity growth of the three biosensors.