A systematic review of the modelling of patient arrivals in emergency departments.

Background: Accident and Emergency Department (AED) is the frontline of providing emergency care in a hospital and research focusing on improving decision-makings and service level around AED has been driving a rising number of attentions in recent years. A retrospective review among the published papers shows that related research can be classified according to six planning modules: demand forecasting, days-off scheduling, shift scheduling, line-of-work construction, task assignment and staff assignment. As patient arrivals demand forecasts enable smooth AED operational planning and help decision-making, this article conducted a systematic review on the statistical modelling approaches aimed at predicting the volume of AED patients' arrival. Methods: We carried out a systematic review of AED patient arrivals prediction studies from 2004 to 2021. The Medline, ScienceDirect, and Scopus databases were searched. A two-step screening process was carried out based on the title and abstract or full text, and 35 of 1,677 articles were selected. Our methods and results follow the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. We categorise AED methods for modelling patient arrivals into four main classes: regression, time series, artificial intelligence and time series regression. Choice of prediction model, selection of factors and model performance are compared. Finally, we discuss the advantages and limitations of the models and suggest future research directions. Results: A total of 1,677 papers that fulfilled the initial searching criteria was obtained from the three databases. Based on the first exclusion criteria, 1,603 articles were eliminated. The remaining 74 full text articles were evaluated based on the second exclusion criteria. Finally, 35 articles were selected for full review. We find that the use of artificial intelligence-based model has risen in recent years, from the view of predictive model selection. The calendar-based factors are most commonly used compared with other types of dependent variables, from the view of dependent variable selection. Conclusions: All AEDs are inherently different and different covariables may have different effects on patient arrivals. Certain factors may play a key role in one AED but not others. Based on results of meta-analysis, when modelling patient arrivals, it is essential to understand the actual AED situation and carefully select relevant dominating factors and the most suitable modelling method. Local calibration is also important to ensure good estimates.

AGNet: Automatic generation network for skin imaging reports.

Medical imaging has been increasingly adopted in the process of medical diagnosis, especially for skin diseases, where diagnoses based on skin pathology are extremely accurate. The diagnostic reports of skin pathology images has the distinguishing features of extreme repetitiveness and rigid formatting. However, reports written by inexperienced radiologists and pathologists can have a high error rate, and even experienced clinicians can find the reporting task both tedious and time-consuming. To address this challenge, this paper studies the automatic generation of diagnostic reports based on images of skin pathologies. A novel deep learning-based image caption framework named the automatic generation network (AGNet), which is an effective network for the automatic generation of skin imaging reports, is proposed. The proposed AGNet consists of four parts: (1) the image model that extracts features and classifies images; (2) the language model that codes data and generates words using comprehensible language; (3) the attention module that connects the "tail" of the image model and the "head" of the language model, and computes the relationship between images and captions; (4) the embedding and labeling module that processes the input caption data. In case study, The AGNet is verified on a skin pathological image dataset and compared with several state-of-the-art models. The results show that the AGNet achieves the highest scores of the evaluation metrics of image caption among all comparison models, demonstrating the promising performance of the proposed method.

A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis.

Owing to the high incidence rate and the severe impact of skin cancer, the precise diagnosis of malignant skin tumors is a significant goal, especially considering treatment is normally effective if the tumor is detected early. Limited published histopathological image sets and the lack of an intuitive correspondence between the features of lesion areas and a certain type of skin cancer pose a challenge to the establishment of high-quality and interpretable computer-aided diagnostic (CAD) systems. To solve this problem, a light-weight attention mechanism-based deep learning framework, namely, DRANet, is proposed to differentiate 11 types of skin diseases based on a real histopathological image set collected by us during the last 10 years. The CAD system can output not only the name of a certain disease but also a visualized diagnostic report showing possible areas related to the disease. The experimental results demonstrate that the DRANet obtains significantly better performance than baseline models (i.e., InceptionV3, ResNet50, VGG16, and VGG19) with comparable parameter size and competitive accuracy with fewer model parameters. Visualized results produced by the hidden layers of the DRANet actually highlight part of the class-specific regions of diagnostic points and are valuable for decision making in the diagnosis of skin diseases.

Artificial Intelligence-Based Application to Explore Inhibitors of Neurodegenerative Diseases.

Neuroinflammation is a common factor in neurodegenerative diseases, and it has been demonstrated that galectin-3 activates microglia and astrocytes, leading to inflammation. This means that inhibition of galectin-3 may become a new strategy for the treatment of neurodegenerative diseases. Based on this motivation, the objective of this study is to explore an integrated new approach for finding lead compounds that inhibit galectin-3, by combining universal artificial intelligence algorithms with traditional drug screening methods. Based on molecular docking method, potential compounds with high binding affinity were screened out from Chinese medicine database. Manifold artificial intelligence algorithms were performed to validate the docking results and further screen compounds. Among all involved predictive methods, the deep learning-based algorithm made 500 modeling attempts, and the square correlation coefficient of the best trained model on the test sets was 0.9. The XGBoost model reached a square correlation coefficient of 0.97 and a mean square error of only 0.01. We switched to the ZINC database and performed the same experiment, the results showed that the compounds in the former database showed stronger affinity. Finally, we further verified through molecular dynamics simulation that the complex composed of the candidate ligand and the target protein showed stable binding within 100 ns of simulation time. In summary, combined with the application based on artificial intelligence algorithms, we unearthed the active ingredients 1,2-Dimethylbenzene and Typhic acid contained in Crataegus pinnatifida and Typha angustata might be the effective inhibitors of neurodegenerative diseases. The high prediction accuracy of the models shows that it has practical application value on small sample data sets such as drug screening.

A universal deep learning approach for modeling the flow of patients under different severities.

BACKGROUND AND OBJECTIVE: The Accident and Emergency Department (A&ED) is the frontline for providing emergency care in hospitals. Unfortunately, relative A&ED resources have failed to keep up with continuously increasing demand in recent years, which leads to overcrowding in A&ED. Knowing the fluctuation of patient arrival volume in advance is a significant premise to relieve this pressure. Based on this motivation, the objective of this study is to explore an integrated framework with high accuracy for predicting A&ED patient flow under different triage levels, by combining a novel feature selection process with deep neural networks. METHODS: Administrative data is collected from an actual A&ED and categorized into five groups based on different triage levels. A genetic algorithm (GA)-based feature selection algorithm is improved and implemented as a pre-processing step for this time-series prediction problem, in order to explore key features affecting patient flow. In our improved GA, a fitness-based crossover is proposed to maintain the joint information of multiple features during iterative process, instead of traditional point-based crossover. Deep neural networks (DNN) is employed as the prediction model to utilize their universal adaptability and high flexibility. In the model-training process, the learning algorithm is well-configured based on a parallel stochastic gradient descent algorithm. Two effective regularization strategies are integrated in one DNN framework to avoid overfitting. All introduced hyper-parameters are optimized efficiently by grid-search in one pass. RESULTS: As for feature selection, our improved GA-based feature selection algorithm has outperformed a typical GA and four state-of-the-art feature selection algorithms (mRMR, SAFS, VIFR, and CFR). As for the prediction accuracy of proposed integrated framework, compared with other frequently used statistical models (GLM, seasonal-ARIMA, ARIMAX, and ANN) and modern machine models (SVM-RBF, SVM-linear, RF, and R-LASSO), the proposed integrated "DNN-I-GA" framework achieves higher prediction accuracy on both MAPE and RMSE metrics in pairwise comparisons. CONCLUSIONS: The contribution of our study is two-fold. Theoretically, the traditional GA-based feature selection process is improved to have less hyper-parameters and higher efficiency, and the joint information of multiple features is maintained by fitness-based crossover operator. The universal property of DNN is further enhanced by merging different regularization strategies. Practically, features selected by our improved GA can be used to acquire an underlying relationship between patient flows and input features. Predictive values are significant indicators of patients' demand and can be used by A&ED managers to make resource planning and allocation. High accuracy achieved by the present framework in different cases enhances the reliability of downstream decision makings.