Data-driven approach for Patient Care

Data-driven Approach for Diabetes, Hypertensive, Heart Patient Care

A review article by

Dr Sunil Kumar  Feel Free to Connet on Linkedin 


The process of determining, examining, and modelling vast amounts of health data in order to find undiscovered patterns or relationships that are helpful to doctors.

A data-driven approach to dynamic treatment planning for chronic diseases like Diabetes, Hypertensive (Blood Pressure), and Cardiovascular disease ( CVD ). 

Data-driven approach according to the patient health data has been cross-checked with multiple factors. The quality of decision-making in the era of precision health and precision medicine is influenced by three groups of data that are related to time. The first group is data describing the patient’s current condition. The second group is data representing the patient’s history, such as the patient’s initial condition and its changes over the time preceding their current condition. The third group of data is related to a description of the patient’s specific living conditions and their future changes, which are not included in the EHR.

These approaches should allow the clinician(s) and patient(s) to evaluate together the qualitative and quantitative contributions of numerous factors to the medical risk, such as the disease, treatment response, failure, complication and/or prognosis in individual patients.

Well, the Data of the diabetes mellitus patients is essential in the study of diabetes management usually people think annual health check-up make them to prevent from the deadly condition but it’s a half-truth in reality the body always undergoes multiple changes and it also include the medicine and its response.


Moreover, because diabetes is a lifelong disease and data available for an individual patient are massive and difficult to interpret in a normal EHR. Finally INIGIMA Cloud Diagnosis bring the capability of interpreting blood glucose readings is important not only in diabetes patients but to doctors also when monitoring patients in intensive care units. The goals of INIGIMA data analytics can be classified into two tasks: description and prediction. While the purpose of description is to extract understandable patterns and associations from data, the goal of prediction is to forecast one or more variables of interest.


Reference :

Bellazzi R, Abu-Hanna A. Data mining technologies for blood glucose and diabetes management. J Diabetes Sci Technol. 2009 May 1;3(3):603-12. doi:  10.1177/193229680900300326. PMID: 20144300; PMCID: PMC2769885. 


Christof Naumzik, Stefan Feuerriegel, Anne Molgaard Nielsen, Data-driven dynamic treatment planning for chronic diseases, European Journal of Operational Research,

Volume 305, Issue 2, 2023, Pages 853-867,


The diabetes-related problem always increases over a period of time, so it’s very important to observe health in a more detailed and effective way. INIGIMA observe the symptoms of type 2 diabetes hypertensive (blood pressure) and helps to bring normal sugar level, and normal BP range and prevent myocardial infarction, and heart attack.




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