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Background Nonalcoholic fatty liver disease (NAFLD) is a risk factor for colorectal neoplasms. Our goal is to explore the relationship between NAFLD and colorectal cancer (CRC) and to analyze potential indicators for screening CRC in NAFLD based on clinical big data. Methods Demographic information and routine clinical indicators were extracted from Xiangya Medical Big Data Platform. 35,610 NAFLD cases without CRC (as group NAFLD-CRC), 306 NAFLD cases with CRC (as group NAFLD-NonCRC) and 10,477 CRC cases without NAFLD were selected and evaluated. The CRC incidence was compared between NAFLD population and general population by Chi-square test. Independent sample t-test was used to find differences of age, gender and routine clinical indicators in pairwise comparisons of NAFLD-CRC, NAFLD-NonCRC and nonNAFLD-CRC. Results NAFLD population had a higher CRC incidence than general population (7.779‰ vs 3.763‰, P  < 0.001). Average age of NAFLD-CRC (58.79 ± 12.353) or nonNAFLD-CRC (59.26 ± 13.156) was significantly higher than NAFLD-nonCRC (54.15 ± 14.167, p  < 0.001). But age had no significant difference between NAFLD-CRC and nonNAFLD-CRC ( P  > 0.05). There was no different gender distribution for three groups (P > 0.05). NAFLD-CRC had lower anaemia-related routine clinical indicators such as decrease of red blood cell count, mean hemoglobin content and hemoglobin than NAFLD-nonCRC ( P  < 0.05 for all). Anemia of NAFLD-CRC was typical but it might be slighter than nonNAFLD-CRC. More interestingly, NAFLD-CRC had distinct characteristics of leukocyte system such as lower white blood cell count (WBC) and neutrophil count (NEU_C) and higher basophil percentage (BAS_Per) than nonNAFLD-CRC and NAFLD-nonCRC ( P  < 0.05 for all). Compared with NAFLD-nonCRC, the change of WBC, BAS_Per and NEU_C in NAFLD-CRC was different from that in nonNAFLD-CRC. In addition, NAFLD-CRC had a higher level of low density lipoprotein (LDL) and high density lipoprotein (HDL), lower level of triglyceride (TG) and Albumin-to-globulin ratio (A/G) than NFLD-nonCRC ( P  < 0.05 for all). Conclusions NAFLD is associated with a high incidence of CRC. Age is an important factor for CRC and the CRC incidence increases with age. Anemia-related blood routine clinical indicators, leukocyte system and blood lipid indicators may be more important variables for identifying CRC in NAFLD. So blood routine test and liver function/blood lipid test are valuable for screening CRC in NAFLD.

Objective Pituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient’s physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs. Methods The clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods. Results Experimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%. Conclusions In this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.

Pituitary adenoma is one of the most common central nervous system tumors. The diagnosis and treatment of pituitary adenoma remain very difficult. Misdiagnosis and recurrence often occur, and experienced neurosurgeons are in serious shortage. A knowledge graph can help interns quickly understand the medical knowledge related to pituitary tumor. The aim of this study was to develop a data fusion method suitable for medical data using data of pituitary adenomas integrated from different sources. The overall goal was to construct a knowledge graph for pituitary adenoma (KGPA) to be used for knowledge discovery. A complete framework suitable for the construction of a medical knowledge graph was developed, which was used to build the KGPA. The schema of the KGPA was manually constructed. Information of pituitary adenoma was automatically extracted from Chinese electronic medical records (CEMRs) and medical websites through a conditional random field model and newly designed web wrappers. An entity fusion method is proposed based on the head-and-tail entity fusion model to fuse the data from heterogeneous sources. Data were extracted from 300 CEMRs of pituitary adenoma and 4 health portals. Entity fusion was carried out using the proposed data fusion model. The F1 scores of the head and tail entity fusions were 97.32% and 98.57%, respectively. Triples from the constructed KGPA were selected for evaluation, demonstrating 95.4% accuracy. This paper introduces an approach to fuse triples extracted from heterogeneous data sources, which can be used to build a knowledge graph. The evaluation results showed that the data in the KGPA are of high quality. The constructed KGPA can help physicians in clinical practice.

Accurately predicting the clinical endpoint in ICU based on the patient’s electronic medical records (EMRs) is essential for the timely treatment of critically ill patients and allocation of medical resources. However, the patient’s EMRs usually consist of a large amount of heterogeneous multivariate time series data such as laboratory tests and vital signs, which are produced irregularly. Most existing methods fail to effectively model the time irregularity inherent in longitudinal patient medical records and capture the interrelationships among different types of data. To tackle these limitations, we propose a novel time-aware transformer-based hierarchical attention network (TERTIAN) for clinical endpoint prediction. In this model, a time-aware transformer is introduced to learn the personalized irregular temporal patterns of medical events, and a hierarchical attention mechanism is deployed to get the accurate patient fusion representation by comprehensively mining the interactions and correlations among multiple types of medical data. We evaluate our model on the MIMIC-III dataset and MIMIC-IV dataset for the task of mortality prediction, and the results show that TERTIAN achieves higher performance than state-of-the-art approaches.

Identifying the histological phenotype of non-small cell lung cancer (NSCLC) is of crucial importance to its treatment and prognosis. The radiomics-based prediction model has the potential to non-invasively extract the tumor phenotype characteristics. However, the existing research ignores the stability of extracted features, which restricts the performance and robustness of the constructed model. While most of themethods in the literature use classification accuracy to solve theproblemofradiomics featuresstability, in this paper we propose the use ofSOM (Self-organizing Mapping) and K-means to evaluate the stability of different feature subsets. The subset with good clustering performance is selected as the optimal feature subset.When the optimal feature subset is used for modeling, compared with other feature subsets, the higher AUC(Area Under Curve) and lower SD(Standard Deviation) on the three classifiers show that the feature subset had excellent classification performance and good stability, and can distinguish NSCLC subtypes more accurately and robustly.

As one of the several effective solutions for personal privacy protection, a global unique identifier (GUID) is linked with hash codes that are generated from combinations of personally identifiable information (PII) by a one-way hash algorithm. On the GUID server, no PII is permitted to be stored, and only GUID and hash codes are allowed. The quality of PII entry is critical to the GUID system. The goal of our study was to explore a method of checking questionable entry of PII in this context without using or sending any portion of PII while registering a subject. According to the principle of GUID system, all possible combination patterns of PII fields were analyzed and used to generate hash codes, which were stored on the GUID server. Based on the matching rules of the GUID system, an error-checking algorithm was developed using set theory to check PII entry errors. We selected 200,000 simulated individuals with randomly-planted errors to evaluate the proposed algorithm. These errors were placed in the required PII fields or optional PII fields. The performance of the proposed algorithm was also tested in the registering system of study subjects. There are 127,700 error-planted subjects, of which 114,464 (89.64%) can still be identified as the previous one and remaining 13,236 (10.36%, 13,236/127,700) are discriminated as new subjects. As expected, 100% of nonidentified subjects had errors within the required PII fields. The possibility that a subject is identified is related to the count and the type of incorrect PII field. For all identified subjects, their errors can be found by the proposed algorithm. The scope of questionable PII fields is also associated with the count and the type of the incorrect PII field. The best situation is to precisely find the exact incorrect PII fields, and the worst situation is to shrink the questionable scope only to a set of 13 PII fields. In the application, the proposed algorithm can give a hint of questionable PII entry and perform as an effective tool. The GUID system has high error tolerance and may correctly identify and associate a subject even with few PII field errors. Correct data entry, especially required PII fields, is critical to avoiding false splits. In the context of one-way hash transformation, the questionable input of PII may be identified by applying set theory operators based on the hash codes. The count and the type of incorrect PII fields play an important role in identifying a subject and locating questionable PII fields.

The occurrence of disasters in deep mining engineering has been confirmed to be closely related to the external dynamic disturbances and geological discontinuities. Thus, a combined finite-element method was employed to simulate the failure process of an underground cavern, which provided insights into the failure mechanism of deep hard rock affected by factors such as the dynamic stress-wave amplitudes, disturbance direction, and dip angles of the structural plane. The crack-propagation process, stress-field distribution, displacement, velocity of failed rock, and failure zone around the circular cavern were analyzed to identify the dynamic response and failure properties of the underground structures. The simulation results indicate that the dynamic disturbance direction had less influence on the dynamic response for the constant in situ stress state, while the failure intensity and damage range around the cavern always exhibited a monotonically increasing trend with an increase in the dynamic load. The crack distribution around the circular cavern exhibited an asymmetric pattern, possibly owing to the stress-wave reflection behavior and attenuation effect along the propagation route. Geological discontinuities significantly affected the stability of nearby caverns subjected to dynamic disturbances, during which the failure intensity exhibited the pattern of an initial increase followed by a decrease with an increase in the dip angle of the structural plane. Additionally, the dynamic disturbance direction led to variations in the crack distribution for specific structural planes and stress states. These results indicate that the failure behavior should be the integrated response of the excavation unloading effect, geological conditions, and external dynamic disturbances.

Traffic flow prediction is an important part of transportation management and planning. For example, accurate demand prediction of taxis and online car-hailing can reduce the waste of resources caused by empty cars. The prediction of public bicycle flow can be more reasonable to plan the release and deployment of public bicycles. There are three difficulties in traffic flow prediction to achieve higher accuracy. Firstly, more accurately to capture the spatio-temporal correlation existing in historical flow data. Secondly, the weight of each channel in the traffic flow data at the same time interval affects the prediction results. Thirdly, the proportion of closeness, period and trend of traffic flow data affects the prediction results. In this paper, we design a deep learning algorithm for short-term traffic flow prediction, called SE-MAConvLSTM. First, we designed Spatio-Temporal Feature Extraction Module (STFEM), which is composed of Convolutional Neural Network (CNN), Squeeze-and-Excitation Network (SENet), Residual Network (ResNet) and Convolutional LSTM Network (ConvLSTM) to solve the above two problems mentioned. In addition, we design multi-attention modules (MAM) to model the closeness, period and trend of traffic flow data to solve the third problem mentioned above. Finally, the aggregation module was used to integrate the output of the last time interval in STFEM and the output of the multi-attention module. Experiments are carried out on two real data sets, and the results show that the proposed model reduces RMSE by 4.5% and 3.7% respectively compared with the best baseline model.

Piezoelectric nanobeams are important components in micro-nano electromechanical systems. They are often used as mechanical structures such as wireless sensors, biological probes and transistors. And their mechanical performance is a very important research topic. Based on the theory of surface elasticity and the \"core–shell\" model, post-buckling behavior of porous piezoelectric nanobeams is analyzed using the first-order shear deformation beam theory, where the surface effect is introduced by employing the surface energy model. The governing equations and boundary conditions of post-buckling of porous piezoelectric nanobeams under mechanical loading were derived by introducing the concept of median surface in physics and the principle of minimum potential energy. The influence of surface effect on post-buckling configuration, post-buckling path, amount of induced charge and critical load of porous piezoelectric nanobeams with different external constraints and porosities were discussed. The results show that considering surface effects, the effective elastic modulus and critical load of porous piezoelectric nanobeams will be increased, and the post-buckling configuration, post-buckling path and amount of induced charge will be reduced. Meanwhile, the mechanical properties of porous piezoelectric nanobeams can be effectively improved by appropriate pore distribution. These findings can be used as a theoretical basis for the accurate design and manufacture of micro-nano mechanical and electronic devices.