Implementation Of A Clinical Decision Support Systems-Based Neonatal Monitoring System Framework

A Clinical Decision Support-based information systems to monitor the vital signs of the neonate's conditions in prematurely born babies placed in infant incubators of Neonatal Intensive Care Unit (NICU) is developed in this work. A DMS was developed consisting of a supervisory microcomputer and sensitive sensors for measuring the vital signs. The Conventional Monitoring System (CMS) was used simultaneously with the DMS to collect the vital sign readings of thirty (30) neonates, over a period of one week. Fuzzy Inference System CDSS (FIS-CDSS) was developed for the three inputs: Temperature, Heart rate and Respiration rate (THR) based on their membership functions' value (low, medium, high) and twenty-seven (27) IF-THEN fuzzy rules using fuzzy logic toolbox in Matrix Laboratory 8.1 (R2014a). The FIS-CDSS maps the THR to an output status (Normal, Abnormal and Critical). The performance of the FIS-CDSS was evaluated using confusion matrix. The results showed that the system yielded sensitivity ranges of 90 - 100, 80 - 89, 70 - 79, 60 - 69 and 50 - 59% for five, eleven, seven, six and one neonates, respectively with an average sensitivity of 77.92%. The specificity of the system ranged from 5.00 to 66.67% with an associated average specificity of 35.10%. The accuracy of the FIS-CDSS ranged from 70 to 100, 60 to 69, 50 to 59 and 0 to 49% for nine, nine, eight and four neonates, respectively with an average accuracy of 60.94%. The developed system provides adequate and accurate information for on-the-spot assessment of neonates for decision making that improves the mortality rate and recovery period of neonates.