How AI can be used for patient risk stratification?
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AI can be utilized for patient risk stratification by combining various data sources like clinical features, genomics, social determinants of health (SDOH), and admission alerts . By employing machine learning algorithms, AI can analyze large-scale datasets to predict patient outcomes accurately. For instance, AI models can identify high-cost members more effectively by integrating traditional data sources with nontraditional ones like SDOH and admission alerts. Additionally, AI can predict personalized risk of imminent primary ischemic stroke by analyzing demographic parameters, disease diagnoses, and care dependence, leading to better individualized disease management. Through cognitive task analysis, AI and natural language processing can visualize unstructured Electronic Health Record (EHR) data, aiding clinicians in gathering patient information, filtering necessary data, and visualizing trends for efficient clinical workflow.
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| Papers (5) | Insight |
|---|---|
| AI and NLP were used to visualize unstructured EHR data for patient risk stratification, improving workflow efficiency and creating intuitive health analytics dashboards in a community health setting. | |
| AI can predict personalized risk of imminent stroke by analyzing 55 features like demographics and diagnoses, enabling accurate patient stratification for improved prevention and management. | |
| AI can enhance patient risk stratification by combining SDOH, ADT alerts, claims, and demographic data, leading to more accurate identification of high-cost members compared to traditional models. | |
| AI can be utilized for patient risk stratification by integrating omics and clinical biomarkers, aiding in predicting disease severity and outcomes accurately, as demonstrated in COVID-19 patients. | |
| AI leverages omics and clinical data to stratify COVID-19 patients based on severity and survival, aiding in personalized medicine by prioritizing therapeutics and predicting disease progression. |
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