Early identification of symptoms can help depreciate the cost of curing the disease or even preventing it. Healthcare providers started looking for reliable preventive healthcare models, which in turn can lead to tremendous cost savings and enhanced health outcomes.
Predicting the possibility of disease occurrence is a vital factor in aiding healthcare providers to improve patient health care.
The challenge is how to integrate the continuously flowing clinical data from various data sources and Machine Learning provides a promising approach to face this challenge. The clinical data is continuously curated using machine learning techniques which in turn result in more accurate predictions of likely disease occurrences which subsequently lead to disease prevention.
Cognub has developed a machine learning model that is designed to learn the pattern between disease and symptoms occurrence. The model learns the pattern based on the available medical data, analyzes the results and predicts the probable diseases, the individuals are most likely to get in near future with a confidence score for each disease. Further, the application also connects to Medical Data, keeps constant track of new data and analyzes it continuously on a real-time basis. The healthcare providers are able to make quick decisions in targeting those individuals and align care plan, thereby reducing the healthcare cost tremendously.