Health Information Science and Systems

The novel coronavirus (COVID-19) is continuing its spread across the world, claiming more than 160,000 lives and sickening more than 2,400,000 people as of April 21, 2020. Early research has reported a basic reproduction number (R0) between 2.2 to 3.6, implying that the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China in and outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. We also investigate the COVID-19 epidemic size in South Korea and find a similar effect of its “test, trace, isolate, and treat” strategy. Our findings are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world.

Image enhancement technology plays an important role in the diagnosis and treatment of medical diseases. In this paper, we propose a method to automatically enhance medical images. The proposed method could be used to support clinical medical diagnosis, adjuvant therapy and curative effect diagnosis. This scheme uses contrast limited adaptive histogram equalization (CLAHE) method in F-shift transformation domain. Firstly, we adjust the overall brightness of the underexposed or overexposed image. Secondly, we perform CLAHE to enhance the low-frequency components obtained by one-level two-dimensional F-shift transformation (TDFS) on the adjusted images. At this stage, most of the coefficients in the high-frequency component can be changed to zero through properly setting the error bound. We then use inverse transformation to reconstruct image which is further enhanced with CLAHE. Compared to previous work, this approach takes into account not only the image enhancement, but also the data compression. Experimental results and comparison with state-of-the-art methods show that our proposed method has a better enhancement performance. Moreover, it has a certain data compression ability.

Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal. Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices. Compared with the CA, the VA is closer to PW (r = 0.539, P < 0.001 to r = 0.589, P < 0.001 in men; r = 0.325, P < 0.001 to r = 0.400, P < 0.001 in women), IPA (r =  − 0.446, P < 0.001 to r =  − 0.534, P < 0.001 in men; r =  − 0.623, P < 0.001 to r =  − 0.660, P < 0.001 in women), RBA (r = 0.328, P < 0.001 to r = 0.371, P < 0.001 in women), AIx (r = 0.659, P < 0.001 to r = 0.738, P < 0.001 in men; r = 0.547, P < 0.001 to r = 0.573, P < 0.001 in women), DAI (r = 0.517, P < 0.001 to r = 0.532, P < 0.001 in men; r = 0.507, P < 0.001 to r = 0.570, P < 0.001 in women) and PTT (r = 0.526, P < 0.001 to r = 0.659, P < 0.001 in men; r = 0.577, P < 0.001 to r = 0.814, P < 0.001 in women). The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.

Individuals and groups who write systematic reviews and meta-analyses in evidence-based medicine regularly carry out literature searches across multiple search engines linked to different bibliographic databases, and thus have an urgent need for a suitable metasearch engine to save time spent on repeated searches and to remove duplicate publications from initial consideration. Unlike general users who generally carry out searches to find a few highly relevant (or highly recent) articles, systematic reviewers seek to obtain a comprehensive set of articles on a given topic, satisfying specific criteria. This creates special requirements and challenges for metasearch engine design and implementation. We created a federated search tool that is connected to five databases: PubMed, EMBASE, CINAHL, PsycINFO, and the Cochrane Central Register of Controlled Trials. Retrieved bibliographic records were shown online; optionally, results could be de-duplicated and exported in both BibTex and XML format. The query interface was extensively modified in response to feedback from users within our team. Besides a general search track and one focused on human-related articles, we also added search tracks optimized to identify case reports and systematic reviews. Although users could modify preset search options, they were rarely if ever altered in practice. Up to several thousand retrieved records could be exported within a few minutes. De-duplication of records returned from multiple databases was carried out in a prioritized fashion that favored retaining citations returned from PubMed. Systematic reviewers are used to formulating complex queries using strategies and search tags that are specific for individual databases. Metta offers a different approach that may save substantial time but which requires modification of current search strategies and better indexing of randomized controlled trial articles. We envision Metta as one piece of a multi-tool pipeline that will assist systematic reviewers in retrieving, filtering and assessing publications. As such, Metta may find wide utility for anyone who is carrying out a comprehensive search of the biomedical literature.

As medical treatments continue to advance rapidly, minimally invasive surgery (MIS) has found extensive applications across various clinical procedures. Accurate identification of medical instruments plays a vital role in comprehending surgical situations and facilitating endoscopic image-guided surgical procedures. However, the endoscopic instrument detection poses a great challenge owing to the narrow operating space, with various interfering factors (e.g. smoke, blood, body fluids) and inevitable issues (e.g. mirror reflection, visual obstruction, illumination variation) in the surgery. To promote surgical efficiency and safety in MIS, this paper proposes a cross-layer aggregated attention detection network (CLAD-Net) for accurate and real-time detection of endoscopic instruments in complex surgical scenarios. We propose a cross-layer aggregation attention module to enhance the fusion of features and raise the effectiveness of lateral propagation of feature information. We propose a composite attention mechanism (CAM) to extract contextual information at different scales and model the importance of each channel in the feature map, mitigate the information loss due to feature fusion, and effectively solve the problem of inconsistent target size and low contrast in complex contexts. Moreover, the proposed feature refinement module (RM) enhances the network’s ability to extract target edge and detail information by adaptively adjusting the feature weights to fuse different layers of features. The performance of CLAD-Net was evaluated using a public laparoscopic dataset Cholec80 and another set of neuroendoscopic dataset from Sun Yat-sen University Cancer Center. From both datasets and comparisons, CLAD-Net achieves the $$AP_{0.5}$$ of 98.9% and 98.6%, respectively, that is better than advanced detection networks. A video for the real-time detection is presented in the following link: https://github.com/A0268/video-demo .

Lead, an environmental toxicant, accounts for 0.6% of the global burden of disease, with the highest burden in developing countries. Lead poisoning is very much preventable with adequate and timely action. Therefore, it is important to identify factors that contribute to maternal BLL and minimise them to reduce the transfer to the foetus. Literacy and awareness related to its impact are low and the clinical establishment for biological monitoring of blood lead level (BLL) is low, costly, and time-consuming. A significant contribution to an infant’s BLL load is caused by maternal lead transfer during pregnancy. This acts as the first pathway to the infant’s lead exposure. The social and demographic information that includes lifestyle and environmental factors are key to maternal lead exposure. We propose a novel approach to build a computational model framework that can predict lead toxicity levels in maternal blood using a set of sociodemographic features. To illustrate our proposed approach, maternal data comprising socio-demographic features and blood samples from the pregnant woman is collected, analysed, and modelled. The computational model is built that learns from the maternal data and then predicts lead level in a pregnant woman using a set of questionnaires that relate to the maternal’s social and demographic information as the first point of testing. The range of features identified in the built models can estimate the underlying function and provide an understanding of the toxicity level. Following feature selection methods, the 12-feature set obtained from the Boruta algorithm gave better prediction results (kNN = 76.84%, DT = 74.70%, and NN = 73.99%). The built prediction model can be beneficial in improving the point of care and hence reducing the cost and the risk involved. It is envisaged that in future, the proposed methodology will become a part of a screening process to assist healthcare experts at the point of evaluating the lead toxicity level in pregnant women. Women screened positive could be given a range of facilities including preliminary counselling to being referred to the health centre for further diagnosis. Steps could be taken to reduce maternal lead exposure; hence, it could also be possible to mitigate the infant’s lead exposure by reducing transfer from the pregnant woman.

Rapid developments in information technology have enabled wearable devices to be applied in the health field. In elderly adults, wearable devices aid in data collection and exerts a positive effect on their social capital. This study evaluated the changes in these two parameters among elderly adults using wearable devices, and analyzed the effect of these devices on their daily lives. We selected 18 elderly people using wearable devices, between February and May 2017. The data collected by the wearable devices included the number of steps taken, sleep duration, blood pressure, heart rate, respiratory rate, fatigue, and mood of the wearers. Using a questionnaire and the trajectory equifinality model, we interviewed and surveyed elderly adults in order to understand their health status and social capital. The health of the participants was generally good, and most were able to achieve > 8000 steps per day (p < 0.05). Mild and moderate fatigue symptoms were noted in elderly adults for 90% of the study period (p < 0.05). The number of steps, blood pressure, and heart rate changed significantly within a month. From the commencement of using the wearable devices, a steady increase was noted in the monthly number of steps. Interviews suggested that the elderly adults perceived wearable devices as having the potential to improve health and social capital. By using wearable devices, the participants had a better understanding of their own health, and were willing to take health-boosting measures. The participants were also more willing to increase their social capital and expand their social network.

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.