Anomaly prediction of CT equipment based on IoMT data.

BACKGROUND: Large-scale medical equipment, which is extensively implemented in medical services, is of vital importance for diagnosis but vulnerable to various anomalies and failures. Most hospitals that conduct regular maintenance have been suffering from medical equipment-related incidents for years. Currently, the Internet of Medical Things (IoMT) has emerged as a crucial tool in monitoring the real-time status of the medical equipment. In this paper, we develop an IoMT system of Computed Tomography (CT) equipment in the West China Hospital, Sichuan University and collected the system status time-series data. Novel multivariate time-series classification models and frameworks are proposed to predict the anomalies of CT equipment. The important features that are closely related to the equipment anomalies are identified with the model. METHODS: We extracted the real-time CT equipment status time-series data of 11 equipment between May 19, 2020 and May 19, 2021 from the IoMT, which includes the equipment oil temperature, anode voltage, etc. The arcs are identified as labels of anomalies due to their relationship with decreased imaging quality and CT equipment failures. To improve prediction accuracy, the statistics and transformations of the raw historical time-series data segment in the sliding time window are used to construct new features. Due to the particularity of time-series data, two frameworks are proposed for splitting the training and test sets. Then the Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayesian, and K-Nearest Neighbor classification models are used to classify the system status. We also compare our model to state-of-the-art models. RESULTS: The results show that the anomaly prediction accuracy and recall of our method are 79% and 77%, respectively. The oil temperature and anode voltage are identified as the decisive features that may lead to anomalies. The proposed model outperforms the others when predicting the anomalies of the CT equipment based on our dataset. CONCLUSIONS: The proposed method could predict the state of CT equipment and be used as a reference for practical maintenance, where unexpected anomalies of medical equipment could be reduced. It also brings new insights into how to handle non-uniform and imbalanced time series data in practical cases.

Identifying drug-related attributes to personalise antihypertensive agents: the outcome report of patients receiving metoprolol therapy.

BACKGROUND: Currently, numerous antihypertensive drugs from different pharmacological classes are available; however, blood pressure control is achieved in only less than a third of patients treated for hypertension. Moreover, providing optimal and personalised treatment for hypertension is challenging. Therefore, in this study, we propose a 'drug-related attributes' sensitive spectrum. This novel concept can assist clinicians in selecting an optimal antihypertensive drug and improve blood pressure control after examining the attributes of a patient. METHODS: We collected clinical data on attributes related to hypertension and its therapy of inpatients from West China Hospital who received metoprolol therapy and constructed the sensitive spectrum using data-visualisation tools. RESULTS: Our analysis revealed that haematocrit, haemoglobin, serum creatinine, serum cystatin C, serum urea, age, sex, systolic pressure, diastolic pressure, pulse pressure, and heart rate are metoprolol-related attributes. CONCLUSION: Our study showed that all metoprolol-related attributes identified are reasonable and helpful in improving the personalisation of metoprolol therapy. The proposed drug-related attributes spectrum can help personalise antihypertensive medication. Moreover, data-visualisation tools can be effectively used to mine the drug-related attributes sensitive spectrum.

Healthcare facilities management: A novel data-driven model for predictive maintenance of computed tomography equipment.

BACKGROUND: The breakdown of healthcare facilities is a huge challenge for hospitals. Medical images obtained by Computed Tomography (CT) provide information about the patients' physical conditions and play a critical role in diagnosis of disease. To deliver high-quality medical images on time, it is essential to minimize the occurrence frequencies of anomalies and failures of the equipment. METHODS: We extracted the real-time CT equipment status time series data such as oil temperature, of three equipment, between May 19, 2020, and May 19, 2021. Tube arcing is treated as the classification label. We propose a dictionary-based data-driven model SAX-HCBOP, where the two methods, Histogram-based Information Gain Binning (HIGB) and Coefficient improved Bag of Pattern (CoBOP), are implemented to transform the data into the bag-of-words paradigm. We compare our model to the existing predictive maintenance models based on statistical and time series classification algorithms. RESULTS: The results show that the Accuracy, Recall, Precision and F1-score of the proposed model achieve 0.904, 0.747, 0.417, 0.535, respectively. The oil temperature is identified as the most important feature. The proposed model is superior to other models in predicting CT equipment anomalies. In addition, experiments on the public dataset also demonstrate the effectiveness of the proposed model. CONCLUSIONS: The two proposed methods can improve the performance of the dictionary-based time series classification methods in predictive maintenance. In addition, based on the proposed real-time anomaly prediction system, the model assists hospitals in making accurate healthcare facilities maintenance decisions.

Perceptual load modulates contour integration in conscious and unconscious states.

Previous research has documented that contour detection and integration may either be affected by local features such as the distances between elements or by high-level cognitive factors such as attention in our visual system. Less is known about how low and high level factors interact to influence contour integration. In this paper, we investigated how attention modulates contour integration through saliency (different element spacing) and topological propert ies (circle or S-shaped) when the state of conscious awareness is manipulated. A modified inattentional blindness (IB) combined with the Posner cuing paradigm was adopted in our three-phased experiment (unconscious-training-conscious). Attention was manipulated with high or low perceptual load for a foveal go/no-go task. Cuing effects were utilized to assess the covert processing of contours prior to a peripheral orientation discrimination task. We found that (1) salient circles and S-contours induced different cuing effects under low perceptual load but not with high load; (2) no consistent pattern of cuing effects was found for non-salient contours in all the conditions; (3) a positive cuing effect was observed for salient circles either consciously or unconsciously while a negative cuing effect occurred for salient S-contours only consciously. These results suggest that conscious awareness plays a pivotal role in coordinating a closure effect with the level of perceptual load. Only salient circles can be successfully integrated in an unconscious state under low perceptual load although both salient circles and S-contours can be done consciously. Our findings support a bi-directional mechanism that low-level sensory features interact with high-level cognitive factors in contour integration.