https://www.ijert.org/development-of-mhealth-application-for-pregnancy-care-and-triage https://www.ijert.org/research/development-of-mhealth-application-for-pregnancy-care-and-triage-IJERTV3IS100984.pdf The Development Of mobile application for diagnosis and treatment of the pregnant women suffering from many general and serious health problems is described. This application is being developed focusing on the health problems occurring in the low and middle income countries, where the health facilities are not well developed and easily available. The patient or the pregnant woman who will use this application is able to predict the disease. The clinical data will be collected from the patient and the disease is prediction is done on the basis of her primary information as well as the symptoms entered by her. The unique feature of this application is that the patient is monitored remotely by her prescribed doctor. She can generate her own health report. The application will enable the patient to send the health report to doctor and he can use this data to diagnose the patient.
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Informatics in Medicine Unlocked, 2019
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for preeclampsia management in antenatal care Cover Page" />
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Journal of Biomedical Physics and Engineering, 2021
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A high-risk pregnancy is one in which pathological problems or abnormal conditions are latent during pregnancy and childbirth, increasing dangers to the mother’s or the infant’s health. Based on international standards and studies, most of the harms and risks to both the mother and the infant can be detected, treated, and prevented through proper pregnancy monitoring, as well as through appropriate and timely diagnosis. In this paper, we present the analysis, design, development, and usability assessment of a telemonitoring system focused on the remote monitoring and control of pregnancy in women suffering from hypertension, diabetes, or high-risk pregnancy. Our system is composed of two mobile web applications. One of these is designed for the medical area, allowing remote monitoring of the patient’s pregnancy, and the other one is directed towards the patient, who enters the alarm symptom data, hypertension data, diabetes data, and clinical analyses, allowing the detection of a ri.
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