Name
Title | ||
|---|---|---|
Classification of forensic autopsy reports through conceptual graph-based document representation model. |
Abstract | ||
|---|---|---|
•Novel conceptual graph-based model is proposed to classify forensic autopsy reports.•The performance of proposed model was evaluated on four manners of death datasets.•The proposed model outperformed compared to existing fully automated baseline models.•SVM, RF, and ensembled voted classifiers showed better classification performance.•Two-level classification showed better results compared to one-level classification. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.jbi.2018.04.013 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Text classification,Supervised machine learning,Graph-based text classification,SNOMED CT concepts and descriptors,Forensic autopsy reports | Information retrieval,Computer science,Conceptual graph,Document representation,Robustness (computer science),SNOMED CT,Classifier (linguistics),Discriminative model,Forensic autopsy,Polysemy | Journal |
Volume | ISSN | Citations |
82 | 1532-0464 | 1 |
PageRank | References | Authors |
0.34 | 22 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ghulam Mujtaba | 1 | 50 | 8.68 |
| Nor Liyana Mohd Shuib | 2 | 72 | 11.65 |
| Ram Gopal Raj | 3 | 50 | 5.54 |
| Retnagowri Rajandram | 4 | 3 | 1.05 |
| Khairunisa Shaikh | 5 | 3 | 1.38 |
| Mohammed Ali Al-Garadi | 6 | 104 | 5.69 |