Use Cases | Heart Failure
This year, the Heart Failure Awareness Days took place from 1 to 7 May.
Download our heart failure use cases, and discover how big data and technology can
- contribute to better and more effective screening for (rare) cardiac diseases, and
- provide further insights into the effectiveness of heart failure treatments in a real-world patient population.
Scroll down to read more.
Real-world evidence (RWE) and data (RWD) can play a crucial role in the early detection and intervention to improve heart failure (HF) patient outcomes. While structured clinical data provides a standardized and organized view of a patient's medical history, much of the valuable information about comorbidities and clinical manifestations of cardiovascular disease, including the risk of HF is recorded in narrative text within electronic health records (EHRs).
This is where unstructured data becomes crucial. By leveraging natural language processing (NLP) and machine learning (ML) technologies, we can analyze unstructured data within EHRs to extract valuable information that is not available in structured data alone. This can help clinicians identify patients who are at risk of developing heart failure, track the progression of the disease, and evaluate the effectiveness of different treatment options.
Discover some examples by clicking the download button at the top of the page:
- AI and Natural Language Processing (NLP) was used to extract insights from anonymized EHRs to investigate the effectiveness of Dapaglifozin in HF patients in the real world.
- An NLP-algorithm was used to detect unique combinations of cardiac and non-cardiac phenotypes in EHRs to enhance the diagnosis of aTTR-CM in a real-world heart failure population.
Real-world evidence (RWE) and data (RWD) can play a crucial role in the early detection and intervention to improve heart failure (HF) patient outcomes. While structured clinical data provides a standardized and organized view of a patient's medical history, much of the valuable information about comorbidities and clinical manifestations of cardiovascular disease, including the risk of HF is recorded in narrative text within electronic health records (EHRs).
This is where unstructured data becomes crucial. By leveraging natural language processing (NLP) and machine learning (ML) technologies, we can analyze unstructured data within EHRs to extract valuable information that is not available in structured data alone. This can help clinicians identify patients who are at risk of developing heart failure, track the progression of the disease, and evaluate the effectiveness of different treatment options.
Discover some examples by clicking the download button at the top of the page:
- AI and Natural Language Processing (NLP) was used to extract insights from anonymized EHRs to investigate the effectiveness of Dapaglifozin in HF patients in the real world.
- An NLP-algorithm was used to detect unique combinations of cardiac and non-cardiac phenotypes in EHRs to enhance the diagnosis of aTTR-CM in a real-world heart failure population.
