Hierarchical Diagnosis Model Research Articles

Dual path parallel hierarchical diagnosis model for intracranial tumors based on multi-feature entropy weight

The grading diagnosis of intracranial tumors is a key step in formulating clinical treatment plans and surgical guidelines. To effectively grade the diagnosis of intracranial tumors, this paper proposes a dual path parallel hierarchical model that can automatically grade the diagnosis of intracranial tumors with high accuracy. In this model, prior features of solid tumor mass and intratumoral necrosis are extracted. Then the optimal division of the data set is achieved through multi-feature entropy weight. The multi-modal input is realized by the dual path structure. Multiple features are superimposed and fused to achieve the image grading. The model has been tested on the actual clinical medical images provided by the Second Affiliated Hospital of Dalian Medical University. The experiment shows that the proposed model has good generalization ability, with an accuracy of 0.990. The proposed model can be applied to clinical diagnosis and has practical application prospects.

Emerging infectious disease surveillance using a hierarchical diagnosis model and the Knox algorithm

Emerging infectious diseases are a critical public health challenge in the twenty-first century. The recent proliferation of such diseases has raised major social and economic concerns. Therefore, early detection of emerging infectious diseases is essential. Subjects from five medical institutions in Beijing, China, which met the spatial-specific requirements, were analyzed. A quality control process was used to select 37,422 medical records of infectious diseases and 56,133 cases of non-infectious diseases. An emerging infectious disease detection model (EIDDM), a two-layer model that divides the problem into two sub-problems, i.e., whether a case is an infectious disease, and if so, whether it is a known infectious disease, was proposed. The first layer model adopts the binary classification model TextCNN-Attention. The second layer is a multi-classification model of LightGBM based on the one-vs-rest strategy. Based on the experimental results, a threshold of 0.5 is selected. The model results were compared with those of other models such as XGBoost and Random Forest using the following evaluation indicators: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The prediction performance of the first-layer TextCNN is better than that of other comparison models. Its average specificity for non-infectious diseases is 97.57%, with an average negative predictive value of 82.63%, indicating a low risk of misdiagnosing non-infectious diseases as infectious (i.e., a low false positive rate). Its average positive predictive value for eight selected infectious diseases is 95.07%, demonstrating the model's ability to avoid misdiagnoses. The overall average accuracy of the model is 86.11%. The average prediction accuracy of the second-layer LightGBM model for emerging infectious diseases reaches 90.44%. Furthermore, the response time of a single online reasoning using the LightGBM model is approximately 27 ms, which makes it suitable for analyzing clinical records in real time. Using the Knox method, we found that all the infectious diseases were within 2000 m in our case, and a clustering feature of spatiotemporal interactions (P < 0.05) was observed as well. Performance testing and model comparison results indicated that the EIDDM is fast and accurate and can be used to monitor the onset/outbreak of emerging infectious diseases in real-world hospitals.

Hydraulic system fault diagnosis of the chain jacks based on multi-source data fusion

In this study, a fault diagnosis approach for the hydraulic system of chain jacks based on multi-source sensor data fusion is proposed. We developed a hydraulic test rig for the chain jacks with a special measurement and control system to measure and collect real-time data on pressure, temperature and flow in different operating conditions. The proposed approach integrates convolutional neural networks (CNN) and long and short-term memory (LSTM) at the network level to extract the spatial–temporal features of the time-series data measured by sensors. Compared with the artificial neural network (ANN), the accuracy of the CNN-LSTM hierarchical diagnosis model was 96.4%, which improved the diagnosis accuracy by 4.4% and enhanced the generalization ability and stability. This study provides a hierarchical monitoring approach for the service status of marine spread mooring systems and chain jack equipment, which is essential for the safe operation of marine equipment.

A hierarchical diagnosis method of cable aged segment based on transfer function

Traditional offline cable diagnosis methods need power outages during detection, affecting power supply reliability. Here, a hierarchical diagnosis method of cable aged segment based on transfer function is proposed. Firstly, the calculation model of cable transfer function with the aged segment is established; on this basis, the correlation between transfer function and cable aging is analysed. Then, a structure with combined sparse autoencoder and convolutional neural network is trained to estimate the aging location, and a hierarchical diagnosis model of distribution cable based on transfer function is proposed. The sensitivity and accuracy of aged segment detection are improved after hierarchical diagnosis. Finally, the simulation results show that the method proposed in this paper can effectively realize the online identification and location of the cable aged segment. The proposed method makes use of the advantage that the cable transfer function can be obtained online. Compared with the existing methods, this method does not need power outages in the diagnosis process, and the aged segment can be located without a lot of additional equipment.

Combining External Medical Knowledge for Improving Obstetric Intelligent Diagnosis: Model Development and Validation

BackgroundData-driven medical health information processing has become a new development trend in obstetrics. Electronic medical records (EMRs) are the basis of evidence-based medicine and an important information source for intelligent diagnosis. To obtain diagnostic results, doctors combine clinical experience and medical knowledge in their diagnosis process. External medical knowledge provides strong support for diagnosis. Therefore, it is worth studying how to make full use of EMRs and medical knowledge in intelligent diagnosis.ObjectiveThis study aims to improve the performance of intelligent diagnosis in EMRs by combining medical knowledge.MethodsAs an EMR usually contains multiple types of diagnostic results, the intelligent diagnosis can be treated as a multilabel classification task. We propose a novel neural network knowledge-aware hierarchical diagnosis model (KHDM) in which Chinese obstetric EMRs and external medical knowledge can be synchronously and effectively used for intelligent diagnostics. In KHDM, EMRs and external knowledge documents are integrated by the attention mechanism contained in the hierarchical deep learning framework. In this way, we enrich the language model with curated knowledge documents, combining the advantages of both to make a knowledge-aware diagnosis.ResultsWe evaluate our model on a real-world Chinese obstetric EMR dataset and showed that KHDM achieves an accuracy of 0.8929, which exceeds that of the most advanced classification benchmark methods. We also verified the model’s interpretability advantage.ConclusionsIn this paper, an improved model combining medical knowledge and an attention mechanism is proposed, based on the problem of diversity of diagnostic results in Chinese EMRs. KHDM can effectively integrate domain knowledge to greatly improve the accuracy of diagnosis.

Transformer graded fault diagnosis based on neighborhood rough set and XGBoost

Aiming at the uncertainty of fault type reasoning based on fault data in transformer fault diagnosis model, this paper proposed a hierarchical diagnosis model based on neighborhood rough set and XGBoost. The model used arctangent transformation to preprocess the DGA data, which could reduce the distribution span of data features and the complexity of model training. Using 5 characteristic gases and 16 gas ratios as the input characteristic parameters of the XGBoost model at all levels, reduction was performed on these 21 input feature attributes, features that had a high contribution to fault classification were retained, and redundant features were removed to improve the accuracy and efficiency of model prediction. Taking advantage of XGBoost's strong ability to extract a few features, the output of the model was the superposition of leaf node scores for each type of fault, the maximum score was the type of failure the sample belonged to, and its value was also the probability value. The obtained probability was used as one of the evidence sources to use D-S evidence theory for information fusion to verify the reliability of the model. Experiments have proved that the XGBoost graded diagnosis model proposed in this article has the highest overall accuracy rate comparing with the traditional model, reaching 93.01%, the accuracy of XGBoost models at all levels has reached more than 90%, the average accuracy rate is higher than that of the traditional model by an average of more than 2.7%, and the average time-consuming is only 0.0695 s. After D-S multi-source information fusion, the reliability of the prediction results of the model proposed in this paper has been improved.

Fault Diagnosis System of Electrical System Based on Classified Hierarchical Model

In order to promote the maintenance and support capability of modern military engineering equipment under the complicated field circumstance, the fault diagnosis expert system is proposed based on neural network. The fault diagnosis characteristics of whole machine as well as the failure mode and mechanism of the electronic system are investigated in detail. Then the structure of classified hierarchical diagnosis model is studied, and the knowledge base of the fault diagnosis system is also been accomplished. The forward reasoning mechanism of the fault diagnosis expert system is bring forward based on neural network intelligent reasoning that can achieve fuzzy input and multiple fault diagnosis. This work is applied in military engineering fields. Hence it effectively enhances the fault maintenance, diagnosis and support capabilities of the military troops.

Application of Classified Hierarchical Model to Fault Diagnosis of Electrical System

The expert system for fault diagnosis based on classified hierarchical model is addressed so as to improve the maintenance capability of engineering vehicles under complex battle environment. The analysis of fault feature, fault pattern and failure mechanism of the whole machine and its electrical system is accomplished. Furthermore, the structure of classified hierarchical diagnosis model and method of knowledge representation are implemented so that the knowledge base of the fault diagnosis system can be developed. The forward reasoning mechanism of the fault diagnosis expert system is bring forward based on neural network intelligent reasoning that can achieve fuzzy input and multiple fault diagnosis. This work effectively increases the fault maintenance, diagnosis and support capabilities of the military troops.

A Hierarchical Diagnosis Model for Syndrome Prediction in TCM of Post-Hepatitic Cirrhosis

Owing to the typical vagueness of treatment processes in traditional Chinese medicine (TCM), determining the final diagnosis result is very difficult. In this paper, a novel hierarchical diagnosis model (HDM) for syndrome prediction problems in TCM is constructed, incorporating rough set theory, expert judgement, and an attribute hierarchical model (AHM). Firstly, using the concept of the attribute hierarchical model, a hierarchical model with three levels - patient level, symptom level and diagnosis level - is built to predict the syndrome of a new case. Secondly, by integrating the positive region, support degree in rough set theory and expert experience, the subjective and objective parts of each attribute on the three levels are acquired. Thirdly, the synthetic attribute-weights at diagnosis level are obtained to predict the syndrome. Finally, an example is studied to illustrate the detailed implementation process of the HDM approach, and a real post-hepatitic cirrhosis dataset is also diagnosed to evaluate the performance of the model. The encouraging results show the effectiveness of the presented method. 1

Hierarchical model‐based diagnosis applied to spacecraft

PurposeTo improve accuracy and efficiency of multi‐fault recognition and localization for large‐scale system such as satellite.Design/methodology/approachFirst, fault propagations of a system are modeled by a digraph, which composes of nodes and arcs. Each arc is associated with information about propagation probability and propagation strength. Then, based on consistency‐based theory and semantic theory of abstractions, hierarchical diagnosis model of a system is built. Finally, according to a two‐way hierarchical diagnosis strategy, two incorporated algorithms are adopted which are the Lagrangian relaxation algorithm and the “method of propagation strength”.FindingsHierarchical model can greatly improve efficiency of diagnosis compared with un‐hierarchical one. The combined qualitative and quantitative knowledge can improve fault resolution.Research limitations/implicationsThe propagation probability and propagation strength must been known.Practical implicationsThe method shows its superiority when it is applied to complex system such as spacecraft.Originality/valueA novel hierarchical framework for large‐scale system multi‐fault diagnosis, which include some new ideas and algorithm is put forward.

Operational fault diagnosis of manufacturing systems

Among all kinds of possible faults in a manufacturing system, operational faults occur most often (about 70%). Efficient diagnosis of these faults is critical for improving the availability and productivity of the manufacturing system. This paper presents a hierarchical diagnosis model based on fault tree analysis (FTA) and two other diagnosis models, respectively, based on the logic and sequential control of manufacturing systems which are usually controlled by a programmable logical controller (PLC). With these models working together, the operational faults of a manufacturing system can be diagnosed completely. The models have been successfully applied to a PLC-controlled flexible manufacturing system (FMS) and have achieved good results.