Asthma kills around 1000 people every day and affects as many as 339 million people – and prevalence is rising. One solution is prediction and early diagnosis of Asthma before it becomes severe.
Why Machine learning ?
– Machine learning(ML) is used across many spheres around the world. The healthcare industry is no exception.
– ML is being used for the analysis of the importance of clinical parameters and their combinations for prognosis, e.g prediction of disease progression, extraction of medical knowledge for outcome research, therapy planning and support and for overall patient management.
– Machine learning provides methods, techniques and tools that can help solving diagnostic and prognostic problems in a variety of medical domains.
– ML is also being used for data analysis, such as detection of regularities in data by appropriately dealing with imperfect data and intelligent alarming resulting in effective and efficient monitoring.
– If predicted well in advance, can provide important insights to doctors who can adapt their diagnosis and treatment per basis and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care.
About the Customer
Healthcare Provider ($5B+ market cap)
Consistently ranked under top 15 providers in the US
in North America with over 45 hospitals and >2+ million patients
– Asthma kills around 1000 people every day and affects as many as 339 million people – and prevalence is rising.
– One solution is prediction and early diagnosis of Asthma before it becomes severe.
– Machine learning is used across many spheres around the world. The healthcare industry is no exception.
– Machine learning can play an essential role in predicting early diagnosis of asthma.
– If predicted well in advance, can provide important insights to doctors who can adapt their diagnosis and treatment per basis.
– We worked on EHR data and trained a machine learning model on EHR data.
– The model developed predict early diagnosis of asthma.
What is their tech stack
Predera Health Care solution
Data Architecture: Processing PHI and PII information
Diagnostic Rule Engine:
Location Analysis: Identify the areas that are in dire need of government support and provide region specific incentives.
Correlation Identification: Study correlation between various indicators, leverage this information to provide better quality of life.
Community Leaders have a single platform that they can now resort to which will ease their strategizing process and there by efforts are more focussed on implementation.
Healthcare industries can study the impact of socio-demographic factors on health, they can use these indicators in their ML models to improve its accuracy.
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