AsdKB: A Chinese Knowledge Base for the Early Screening and Diagnosis of Autism Spectrum Disorder

To easily obtain the knowledge about autism spectrum disorder and help its early screening and diagnosis, we create AsdKB, a Chinese knowledge base on autism spectrum disorder. The knowledge base is built on top of various sources, including 1) the disease knowledge from SNOMED CT and ICD-10 clinical descriptions on mental and behavioural disorders, 2) the diagnostic knowledge from DSM-5 and different screening tools recommended by social organizations and medical institutes, and 3) the expert knowledge on professional physicians and hospitals from the Web. AsdKB contains both ontological and factual knowledge, and is accessible as Linked Data at https://w3id.org/asdkb/. The potential applications of AsdKB are question answering, auxiliary diagnosis, and expert recommendation, and we illustrate them with a prototype which can be accessed at http://asdkb.org.cn/.Comment: 17 pages, Accepted by the Resource Track of ISWC 202

Fine-Grained Relation Extraction for Drug Instructions Using Contrastive Entity Enhancement

The extraction of relations between drug-related entities from drug instructions is essential for clinical diagnostic decision-making and drug use regulations, which is a critical task. However, due to the complexity of the textual descriptions in drug instructions, it is challenging to extract fine-grained relations, even with a considerable amount of training data. Moreover, since manually-labeled, high-quality datasets in the pharmaceutical domain are typically expensive, obtaining an extensive and accurate training dataset could be challenging. To overcome the above challenges, this paper proposes a drug relation extraction framework that combines entity information enhancement and contrastive feature learning, which can better extract fine-grained relations with limited data. More specifically, a sample generator creates a group of different samples with role semantic information from the training set, an entity encoder embeds the entity role information and context information to enhance the semantic representation, and a contrastive learning module employs a hybrid loss function to learn inter-sample and intra-sample differences. Empirical study indicates that the contrastive entity enhancement approach can achieve higher extraction accuracy and has better generalization capability. More specifically, the experimental results show that the F1 value of the model can reach 0.8892, which provides a 7.13% improvement compared to the baseline pre-training method

The Analysis of the Disease Spectrum in China

Analysis of the related risks of disease provides a scientific basis for disease prevention and treatment, hospital management, and policy formulation by the changes in disease spectrum of patients in hospital. Retrospective analysis was made to the first diagnosis, age, gender, daily average cost of hospitalized patients, and other factors in the First Affiliated Hospital of Nanjing Medical University during 2006–2013. The top 4 cases were as follows: cardiovascular disease, malignant tumors, lung infections, and noninsulin dependent diabetes mellitus. By the age of disease analysis, we found a younger age trend of cardiovascular disease, and the age of onset of cancer or diabetes was somewhat postponed. The average daily cost of hospitalization and the average daily cost of the main noncommunicable diseases were both on the rise. Noncommunicable diseases occupy an increasingly important position in the constitution of the disease, and they caused an increasing medical burden. People should pay attention to health from the aspects of lifestyle changing. Hospitals should focus on building the appropriate discipline. On the other hand, an integrated government response is required to tackle key risks. Multiple interventions are needed to lower the burden of these diseases and to improve national health