School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, People's Republic of China.
Alibaba Group, Bellevue, WA, USA.
BMC Bioinformatics. 2019 Jun 13;20(1):330. doi: 10.1186/s12859-019-2924-0.
Ontology has attracted substantial attention from both academia and industry. Handling uncertainty reasoning is important in researching ontology. For example, when a patient is suffering from cirrhosis, the appearance of abdominal vein varices is four times more likely than the presence of bitter taste. Such medical knowledge is crucial for decision-making in various medical applications but is missing from existing medical ontologies. In this paper, we aim to discover medical knowledge probabilities from electronic medical record (EMR) texts to enrich ontologies. First, we build an ontology by identifying meaningful entity mentions from EMRs. Then, we propose a symptom-dependency-aware naïve Bayes classifier (SDNB) that is based on the assumption that there is a level of dependency among symptoms. To ensure the accuracy of the diagnostic classification, we incorporate the probability of a disease into the ontology via innovative approaches.
We conduct a series of experiments to evaluate whether the proposed method can discover meaningful and accurate probabilities for medical knowledge. Based on over 30,000 deidentified medical records, we explore 336 abdominal diseases and 81 related symptoms. Among these 336 gastrointestinal diseases, the probabilities of 31 diseases are obtained via our method. These 31 probabilities of diseases and 189 conditional probabilities between diseases and the symptoms are added into the generated ontology.
In this paper, we propose a medical knowledge probability discovery method that is based on the analysis and extraction of EMR text data for enriching a medical ontology with probability information. The experimental results demonstrate that the proposed method can effectively identify accurate medical knowledge probability information from EMR data. In addition, the proposed method can efficiently and accurately calculate the probability of a patient suffering from a specified disease, thereby demonstrating the advantage of combining an ontology and a symptom-dependency-aware naïve Bayes classifier.
本体在学术界和工业界都受到了广泛关注。处理不确定性推理在研究本体时很重要。例如，当患者患有肝硬化时，出现腹部静脉曲胀的可能性是口苦的四倍。这种医学知识对于各种医疗应用的决策至关重要，但现有的医学本体中却没有。在本文中，我们旨在从电子病历（EMR）文本中发现医学知识概率，以丰富本体。首先，我们通过从 EMR 中识别有意义的实体提及来构建本体。然后，我们提出了一种基于症状依赖假设的朴素贝叶斯分类器（SDNB）。为了确保诊断分类的准确性，我们通过创新方法将疾病的概率纳入本体。
我们进行了一系列实验，以评估所提出的方法是否能够发现有意义和准确的医学知识概率。基于超过 30000 份去识别医疗记录，我们探索了 336 种腹部疾病和 81 种相关症状。在这 336 种胃肠道疾病中，有 31 种疾病的概率是通过我们的方法获得的。这些 31 种疾病的概率和 189 种疾病与症状之间的条件概率被添加到生成的本体中。
在本文中，我们提出了一种基于 EMR 文本数据分析和提取的医学知识概率发现方法，用于为医学本体丰富概率信息。实验结果表明，所提出的方法可以有效地从 EMR 数据中识别准确的医学知识概率信息。此外，所提出的方法可以高效准确地计算患者患指定疾病的概率，从而证明了本体和症状依赖的朴素贝叶斯分类器结合的优势。
