ASGE Masterclass: Artificial Intelligence (On-Demand) | June 2021-09-taposh

09-taposh_ai-ehr-asge-2021-v3 (1)

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AI in Predictive Modeling: EHR Data<br /><br />Electronic Health Records (EHR) are a rich source of data that can be leveraged through predictive modeling and artificial intelligence (AI) techniques. The primary goal of AI in EHR is to provide insights for both individual and population views. AI needs various types of data, including structured and unstructured text, images, videos, and audio data, which can be complex to analyze.<br /><br />Predictive modeling in EHR involves a life cycle that includes data collection, model development, verification of results, and model deployment. Machine learning paradigms, such as supervised learning, unsupervised learning, and reinforcement learning, are used in the analysis of EHR data. Classification, regression, clustering, and reinforcement learning are the general machine learning models employed for EHR predictions.<br /><br />To understand how AI solutions work, we can relate them to how humans perceive and understand things. For instance, we can classify objects, such as distinguishing between a cow and a dog based on height, horns, facial features, and tail characteristics. Machines can be trained to understand these differences using labeled data and a learning algorithm.<br /><br />Similarly, machines can be trained with past data to predict values, such as house prices, using regression models. Clustering algorithms can group similar data points together, similar to how humans organize toys and books in a room. Reinforcement learning enables machines to learn from trial and error, adjusting actions based on positive or negative rewards.<br /><br />Opportunities for AI in EHR include ensuring organization, fairness, transparency, explainability, and trust in models and outcomes. Models should be organized to guide decision-making, free from biases, and easily explainable. Model outcomes should be monitored and compared with actual results to ensure accuracy and trustworthiness.<br /><br />In conclusion, AI in predictive modeling for EHR data offers promising opportunities for improving healthcare. By utilizing various machine learning models, healthcare professionals can gain valuable insights from EHR data to aid in diagnosis, treatment, and decision-making.

Keywords

predictive modeling

EHR data

electronic health records

machine learning

supervised learning

unsupervised learning

reinforcement learning

classification

regression