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Table 3 Big data modeling techniques

From: Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15th ADQI Consensus Conference

Method Advantages Disadvantages
Neural Networks Discover non-linear relationships. Can assess multi-level interactions “Black Box” to clinicians; hard to implement into a DSS*
Random Forests Finds most probable solution set; robust against scaling influences Not always best in terms of prediction; hard to implement into a DSS
Cluster Analysis Finds groups of very similar patients; exploratory analysis Unsupervised technique
Principal Components Analysis Uncovers the variables contributing the most to outcome variation Not amenable to binary outcomes; assumes additive relationship
Support Vector Machines Robust against statistical assumptions Difficult to implement into a DSS