Patient Status Classification by using Rule based Sentence Extraction and BM 25-kNN based Classifier

By Eiji Aramaki, Takeshi Imai, Kengo Miyo, Kazuhiko Ohe. 2006

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Zexian Zeng
2. Yuan Luo
3. Yu Deng
4. Peter Szolovits
5. Xiaoyu Li
6. Tristan Naumann
7. Ephraim P. Hochberg
8. Korde

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[96] NA Self-defined keywords Narrative Similarity score between sentences Bejan, Vanderwende, et al.”
• TEXT CLASSIFICATION AND CLASSIFIERS: A SURVEY (2012, methodology): “K-NN classifier is a case-based learning [9] algorithm that is based on a distance or similarity function for pairs of observations, such as the Euclidean distance or Cosine similarity measure’s This method is try for many application [10] Because of its effectiveness, non-parametric and easy to imp”
• Automatic lymphoma classification with sentence subgraph mining from pathology reports. (2014, background): “Feature engineering details contributed significantly among the best performing systems [20][21][22].”