BackgroundThe accumulation of medical documents in China has rapidly increased in the past years. We focus on developing a method that automatically performs ICD-10 code assignment to Chinese diagnoses from the electronic medical records to support the medical coding process in Chinese hospitals.MethodsWe propose two encoding methods: one that directly determines the desired code (flat method), and one that hierarchically determines the most suitable code until the desired code is obtained (hierarchical method). Both methods are based on instances from the standard diagnostic library, a gold standard dataset in China. For the first time, semantic similarity estimation between Chinese words are applied in the biomedical domain with the successful implementation of knowledge-based and distributional approaches. Characteristics of the Chinese language are considered in implementing distributional semantics. We test our methods against 16,330 coding instances from our partner hospital.ResultsThe hierarchical method outperforms the flat method in terms of accuracy and time complexity. Representing distributional semantics using Chinese characters can achieve comparable performance to the use of Chinese words. The diagnoses in the test set can be encoded automatically with micro-averaged precision of 92.57 %, recall of 89.63 %, and F-score of 91.08 %. A sharp decrease in encoding performance is observed without semantic similarity estimation.ConclusionThe hierarchical nature of ICD-10 codes can enhance the performance of the automated code assignment. Semantic similarity estimation is demonstrated indispensable in dealing with Chinese medical text. The proposed method can greatly reduce the workload and improve the efficiency of the code assignment process in Chinese hospitals.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0269-4) contains supplementary material, which is available to authorized users.

Objective: Phenotyping algorithms can efficiently and accurately identify patients with a specific disease phenotype and construct electronic health records (EHR)-based cohorts for subsequent clinical or genomic studies. Previous studies have introduced unsupervised EHR-based feature selection methods that yielded algorithms with high accuracy. However, those selection methods still require expert intervention to tweak the parameter settings according to the EHR data distribution for each phenotype. To further accelerate the development of phenotyping algorithms, we propose a fully automated and robust unsupervised feature selection method that leverages only publicly available medical knowledge sources, instead of EHR data.

A novel approach for detecting anomaly in visual surveillance system is proposed in this paper. It is composed of three parts:(a) a dense motion field and motion statistics method, (b) one-class SVM for one-class classification, (c) motion directional PCA for feature dimensionality reduction. Experiments demonstrate the effectiveness of proposed algorithm in detecting abnormal events in surveillance video, while keeping a low false alarm rate. Moreover, it works well in complicated situation where the common tracking or detection module won't work.

We demonstrated the potential necessity of score normalization when estimating semantic similarity using ontology-based measures. The results of this study can also be extended to other language systems to implement semantic similarity estimation in biomedicine.

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