IoT in Provenance Management
of Medical Data
Gennady Chuiko, Yaroslav Krainyk, Olga Dvornik, and Yevhen Darnapuk
Abstract In this chapter, we propose to investsigate the applicability of semantics
in the context of Internet-of-Things (IoT) to trace the origins of medical data. As IoTdevices have become the first-order source of information in the field of healthcare
in various systems, the challenge of correctness and reliability of retrieved data is
becoming of tremendous importance. This challenge is directly connected with the
quality of patient monitoring and treatment because the decision on the patient’s state
is made according to the set of measured parameters. Inaccuracy and low quality of
measurements that may be caused by sensor malfunction, incorrect measurement
procedure, etc. can lead to problems with comprehension of the current situation and
affect further decisions. The photometric calibrating curves of Melatonin-sulfate in
human urine were considered as a case-study. The Hill’s equation was used for ‘dose–
response’ relationship. The photometric calibrating graphs of Melatonin-sulfate in
human urine were considered as a case study. Hill’s equation imaged the ‘dose–
response’ relation. The photometric transmittance of analyzed solutions was the
response signal. The ordinary photometry of human urine can be in use as the simple
ex-press-analysis of melatonin instead of expensive analyzes. If, sure, the accord-ant
calibrators are reliable. The existing set of such calibrators yet unable warrants the
trusty calibrating. Thus, the medical photometry of urinary Melatonin-sulfate is yet
out of extensive use. The problem of reliable calibrators is mostly in the provenance
of data.
Keywords Medical data · Semantic · IoT · Provenance · Ontology
G. Chuiko · Y. Krainyk (B) · O. Dvornik · Y. Darnapuk
Petro Mohyla Black Sea National University, 68 Desantnykiv, 10, Mykolaiv 54003, Ukraine
e-mail: yaroslav.krainyk@chmnu.edu.ua
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_15
347
of Medical Data
Gennady Chuiko, Yaroslav Krainyk, Olga Dvornik, and Yevhen Darnapuk
Abstract In this chapter, we propose to investsigate the applicability of semantics
in the context of Internet-of-Things (IoT) to trace the origins of medical data. As IoTdevices have become the first-order source of information in the field of healthcare
in various systems, the challenge of correctness and reliability of retrieved data is
becoming of tremendous importance. This challenge is directly connected with the
quality of patient monitoring and treatment because the decision on the patient’s state
is made according to the set of measured parameters. Inaccuracy and low quality of
measurements that may be caused by sensor malfunction, incorrect measurement
procedure, etc. can lead to problems with comprehension of the current situation and
affect further decisions. The photometric calibrating curves of Melatonin-sulfate in
human urine were considered as a case-study. The Hill’s equation was used for ‘dose–
response’ relationship. The photometric calibrating graphs of Melatonin-sulfate in
human urine were considered as a case study. Hill’s equation imaged the ‘dose–
response’ relation. The photometric transmittance of analyzed solutions was the
response signal. The ordinary photometry of human urine can be in use as the simple
ex-press-analysis of melatonin instead of expensive analyzes. If, sure, the accord-ant
calibrators are reliable. The existing set of such calibrators yet unable warrants the
trusty calibrating. Thus, the medical photometry of urinary Melatonin-sulfate is yet
out of extensive use. The problem of reliable calibrators is mostly in the provenance
of data.
Keywords Medical data · Semantic · IoT · Provenance · Ontology
G. Chuiko · Y. Krainyk (B) · O. Dvornik · Y. Darnapuk
Petro Mohyla Black Sea National University, 68 Desantnykiv, 10, Mykolaiv 54003, Ukraine
e-mail: yaroslav.krainyk@chmnu.edu.ua
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_15
347