Multivariate Conditional Anomaly Detection and Its Clinical Application
Authors: Charmgil Hong, Milos Hauskrecht
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our approach and demonstrate its effectiveness, we present experimental results on a clinical dataset obtained from Cincinnati Childrens Hospital Medical Center (Pestian et al. 2007). The dataset has 978 instances; each consists of 1,449 features (x) extracted from clinical progress notes and 45 binary class variables (y) representing the diseases diagnosed. We compared two of our chain variations chain.mod1 (Batal, Hong, and Hauskrecht 2013) and chain.mod2 (Hong and Hauskrecht 2015) with the binary relevance (BR) model (Boutell et al. 2004), which ignores the relationships between individual clinical decisions. We performed 10-fold cross validation with 3 repeats. On each round, we perturbed 15% of test data by randomly flipping 1 to 5 class variables, and see whether the methods can correctly identify the anomalies. The anomaly score is evaluated by the Mahalanobis distance on the posterior class probability P(y|x). Figure 1 shows the results in terms of the area under receiver operating characteristic (AUC). |
| Researcher Affiliation | Academia | Charmgil Hong and Milos Hauskrecht Computer Science Department University of Pittsburgh Pittsburgh, PA 15260 |
| Pseudocode | No | The paper describes its methods and equations in narrative text, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not contain any statement about releasing its source code or provide any links to a code repository for the described methodology. |
| Open Datasets | Yes | To validate our approach and demonstrate its effectiveness, we present experimental results on a clinical dataset obtained from Cincinnati Childrens Hospital Medical Center (Pestian et al. 2007). |
| Dataset Splits | Yes | We performed 10-fold cross validation with 3 repeats. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory, or computing infrastructure) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or specific libraries). |
| Experiment Setup | Yes | We compared two of our chain variations chain.mod1 (Batal, Hong, and Hauskrecht 2013) and chain.mod2 (Hong and Hauskrecht 2015) with the binary relevance (BR) model (Boutell et al. 2004), which ignores the relationships between individual clinical decisions. We performed 10-fold cross validation with 3 repeats. On each round, we perturbed 15% of test data by randomly flipping 1 to 5 class variables, and see whether the methods can correctly identify the anomalies. The anomaly score is evaluated by the Mahalanobis distance on the posterior class probability P(y|x). |