Name
Abstract | ||
|---|---|---|
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We employ a Convolutional Neural Network (CNN) to encode plain text into a fixed-length sentence embedding vector. Since diagnoses are typically correlated, a deep residual network is employed on top of the CNN encoder, to capture label (diagnosis) dependencies and incorporate information directly from the encoded sentence vector. A real EHR dataset is considered, and we compare the proposed model with several well-known baselines, to predict diagnoses based on doctor notes. Experimental results demonstrate the superiority of the proposed convolutional residual model. |
Year | Venue | DocType |
|---|---|---|
2018 | MLHC | Conference |
Volume | Citations | PageRank |
abs/1801.05062 | 3 | 0.38 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyuan Zhang | 1 | 10 | 3.16 |
| Ricardo Henao | 2 | 4 | 5.56 |
| Zhe Gan | 3 | 319 | 32.58 |
| Yitong Li | 4 | 44 | 7.98 |
| L. Carin | 5 | 4603 | 339.36 |