Jiangtao Ren

AIIM Journal 2023 Journal Article

Differential diagnosis of secondary hypertension based on deep learning

• Lin Wu
• Liying Huang
• Mei Li
• Zhaojun Xiong
• Dinghui Liu
• Yong Liu
• Suzhen Liang
• Hua Liang

Secondary hypertension is associated with higher risks of target organ damage and cardiovascular and cerebrovascular disease events. Early aetiology identification can eliminate aetiologies and control blood pressure. However, inexperienced doctors often fail to diagnose secondary hypertension, and comprehensively screening for all causes of high blood pressure increases health care costs. To date, deep learning has rarely been involved in the differential diagnosis of secondary hypertension. Relevant machine learning methods cannot combine textual information such as chief complaints with numerical information such as the laboratory examination results in electronic health records (EHRs), and the use of all features increases health care costs. To reduce redundant examinations and accurately identify secondary hypertension, we propose a two-stage framework that follows clinical procedures. The framework carries out an initial diagnosis process in the first stage, on which basis patients are recommended for disease-related examinations, followed by differential diagnoses of different diseases based on the different characteristics observed in the second stage. We convert the numerical examination results into descriptive sentences, thus blending textual and numerical characteristics. Medical guidelines are introduced through label embedding and attention mechanisms to obtain interactive features. Our model was trained and evaluated using a cross-sectional dataset containing 11, 961 patients with hypertension from January 2013 to December 2019. The F1 scores of our model were 0. 912, 0. 921, 0. 869 and 0. 894 for primary aldosteronism, thyroid disease, nephritis and nephrotic syndrome and chronic kidney disease, respectively, which are four kinds of secondary hypertension with high incidence rates. The experimental results show that our model can powerfully use the textual and numerical data contained in EHRs to provide effective decision support for the differential diagnosis of secondary hypertension.

AIIM Journal 2020 Journal Article

Automated ICD-10 code assignment of nonstandard diagnoses via a two-stage framework

• Chengjie Mou
• Jiangtao Ren

An electronic medical record (EMR) is a rich source of clinical information for medical studies. Each physician usually has his or her own way to describe a patient's diagnosis. This results in many different ways to describe the same disease, which produces a large number of informal nonstandard diagnoses in EMRs. The Tenth Revision of International Classification of Diseases (ICD-10) is a medical classification list of codes for diagnoses. Automated ICD-10 code assignment of the nonstandard diagnosis is an important way to improve the quality of the medical study. However, manual coding is expensive, time-consuming and inefficient. Moreover, terminology in the standard diagnostic library comprises approximately 23, 000 subcategory (6-digit) codes. Classifying the entire set of subcategory codes is extremely challenging. ICD-10 codes in the standard diagnostic library are organized hierarchically, and each category code (3-digit) relates to several or dozens of subcategory (6-digit) codes. Based on the hierarchical structure of the ICD-10 code, we propose a two-stage ICD-10 code assignment framework, which examines the entire category codes (approximately 1900) and searches the subcategory codes under the specific category code. Furthermore, since medical coding datasets are plagued with a training data sparsity issue, we introduce more supervised information to overcome this issue. Compared with the method that searches within approximately 23, 000 subcategory codes, our approach requires examination of a considerably reduced number of codes. Extensive experiments show that our framework can improve the performance of the automated code assignment.

ECAI Conference 2020 Conference Paper

Sequence Prediction Model for Aspect-Level Sentiment Classification

• Qianlong Wang 0001
• Jiangtao Ren

Aspect-level sentiment classification aims to distinguish the sentiment polarity of each aspect in a given sentence. It is more complex than text-level sentiment classification in that it is a fine-grained task. Existing methods, which formulate this task as predicting the sentiment polarity of a provided (sentence, aspect) pair, tend to ignore the relationship between the sentiment polarity of aspects. In this paper, we propose a sequence prediction model with a sentiment polarity fusion module which sequentially predicts the sentiment polarity of each aspect within sentence. Besides, we use the temporal attention mechanism to keep track of what has been focused on, which discourages repeated attention to the context words with strong sentiment polarity when predicting the sentiment polarity of different aspects. Experimental results on five benchmarking collections illustrate that our proposed model3 outperforms a range of baseline models by a substantial margin, and further demonstrate that the relationship between the sentiment polarity of aspects is helpful to solve the aspect-level sentiment classification.

ICML Conference 2011 Conference Paper

Eigenvalue Sensitive Feature Selection

• Yi Jiang
• Jiangtao Ren