EHR-Based Deep Learning for Clinical Prediction
Keywords:
Deep learning: Predictive Analytics: Electronic Health Records: EHR: Healthcare: Analytics: Healthcare software: EHR Software: Healthcare Services: predictive healthcare outcomes: Health Informatics: EHI: HIT: Clinical Informatics: Predictive Decision Support: Support Vector Machines: Multiple-Kernel Learning: data-mining tools: health: care: dynamics: temporal constraints Of Health Workplace: applications: field: stream: visibility: similarity: extracting: electronic: social: find: security: prevention electrocardiogram: technology: feature: linear: global: artificial intelligence.2Abstract
Deep learning techniques surprise experts and enthusiasts alike with constantly improving performance in an increasing number of application areas, such as image classification, natural language processing, and speech recognition. Early adopters observe tangible competitive advantages and are reaping the benefits of deploying deep learning systems in production. Healthcare has great potential for improving predictive analytics with rich data from electronic health records. Thanks to the extraordinary capacities of deep neural networks, large volumes of heterogeneous, multi-structured EHR data can be analyzed more accurately, leading to higher quality healthcare outcomes. Anticipating patient deterioration, identifying high-risk individuals, and predicting the development of specific diseases disenable timely and targeted interventions.
Despite the abundance of data, the clinical insights of risk profiles and predictive models have not yet materialized—major hurdles include high maintenance costs and associated resource requirements. Innovators must customize and deploy new systems with every project, and embedding predictive models into clinical decision support systems remains a dream rather than reality. The rich variety of EHR data is usually processed in the service of a specific task rather than for broader, production-scale deployments, limiting investment returns. First-serving deep learning models demonstrate evidence of predictive capacity, but validation, implementation, and integration across EHR data modalities often receive little attention
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