Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Issue 6 (21st December 2021)

• Record Type: Journal Article
• Authors: Pettit, Rowland W.; Fullem, Robert; Cheng, Chao; Amos, Christopher I.
• Is Part Of: Emerging topics in life sciences. Volume 5:Issue 6(2021)
• Page Start: 729
• Page End: 745
• Publication Date: 2021-12-21
• Subjects: artificial intelligence -- deep learning -- machine learning -- review; Life sciences -- Periodicals; 570.5
• DOI: 10.1042/ETLS20210246
• Languages: English
• ISSNs: 2397-8554

Abstract

Abstract : AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.