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Public Health Public Health

Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method.

In Neural computing & applications

** : To predict the mortality of patients with coronavirus disease 2019 (COVID-19). We collected clinical data of COVID-19 patients between January 18 and March 29 2020 in Wuhan, China . Gradient boosting decision tree (GBDT), logistic regression (LR) model, and simplified LR were built to predict the mortality of COVID-19. We also evaluated different models by computing area under curve (AUC), accuracy, positive predictive value (PPV), and negative predictive value (NPV) under fivefold cross-validation. A total of 2924 patients were included in our evaluation, with 257 (8.8%) died and 2667 (91.2%) survived during hospitalization. Upon admission, there were 21 (0.7%) mild cases, 2051 (70.1%) moderate case, 779 (26.6%) severe cases, and 73 (2.5%) critically severe cases. The GBDT model exhibited the highest fivefold AUC, which was 0.941, followed by LR (0.928) and LR-5 (0.913). The diagnostic accuracies of GBDT, LR, and LR-5 were 0.889, 0.868, and 0.887, respectively. In particular, the GBDT model demonstrated the highest sensitivity (0.899) and specificity (0.889). The NPV of all three models exceeded 97%, while their PPV values were relatively low, resulting in 0.381 for LR, 0.402 for LR-5, and 0.432 for GBDT. Regarding severe and critically severe cases, the GBDT model also performed the best with a fivefold AUC of 0.918. In the external validation test of the LR-5 model using 72 cases of COVID-19 from Brunei, leukomonocyte (%) turned to show the highest fivefold AUC (0.917), followed by urea (0.867), age (0.826), and SPO2 (0.704). The findings confirm that the mortality prediction performance of the GBDT is better than the LR models in confirmed cases of COVID-19. The performance comparison seems independent of disease severity.

Supplementary Information : The online version contains supplementary material available at(10.1007/s00521-020-05592-1).

Li Simin, Lin Yulan, Zhu Tong, Fan Mengjie, Xu Shicheng, Qiu Weihao, Chen Can, Li Linfeng, Wang Yao, Yan Jun, Wong Justin, Naing Lin, Xu Shabei


COVID-19, China, Machine learning, Mortality, Prediction

General General

Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond.

In International journal of nursing studies advances

As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.

Keim-Malpass Jessica, Moorman Liza P


Acuity assessment, COVID-19, Clinical deterioration, Continuous predictive analytics monitoring, Nursing, Precision surveillance

General General

Using LSTM and ARIMA to Simulate and Predict Limestone Price Variations.

In Mining, metallurgy & exploration

There have been many improvements and advancements in the application of neural networks in the mining industry. In this study, two advanced deep learning neural networks called recurrent neural network (RNN) and autoregressive integrated moving average (ARIMA) were implemented in the simulation and prediction of limestone price variation. The RNN uses long short-term memory layers (LSTM), dropout regularization, activation functions, mean square error (MSE), and the Adam optimizer to simulate the predictions. The LSTM stores previous data over time and uses it in simulating future prices based on defined parameters and algorithms. The ARIMA model is a statistical method that captures different time series based on the level, trend, and seasonality of the data. The auto ARIMA function searches for the best parameters that fit the model. Different layers and parameters are added to the model to simulate the price prediction. The performance of both network models is remarkable in terms of trend variability and factors affecting limestone price. The ARIMA model has an accuracy of 95.7% while RNN has an accuracy of 91.8%. This shows that the ARIMA model outperforms the RNN model. In addition, the time required to train the ARIMA is than that of the RNN. Predicting limestone prices may help both investors and industries in making economical and technical decisions, for example, when to invest, buy, sell, increase, and decrease production.

Mbah Tawum Juvert, Ye Haiwang, Zhang Jianhua, Long Mei


Autoregressive integrated moving average, Limestone, Long short-term memory, Predict, Price, Recurrent neural network

General General

Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic.

In International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management

The pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading all over the world. Medical health care systems are in urgent need to diagnose this pandemic with the support of new emerging technologies like artificial intelligence (AI), internet of things (IoT) and Big Data System. In this dichotomy study, we divide our research in two ways-firstly, the review of literature is carried out on databases of Elsevier, Google Scholar, Scopus, PubMed and Wiley Online using keywords Coronavirus, Covid-19, artificial intelligence on Covid-19, Coronavirus 2019 and collected the latest information about Covid-19. Possible applications are identified from the same to enhance the future research. We have found various databases, websites and dashboards working on real time extraction of Covid-19 data. This will be conducive for future research to easily locate the available information. Secondly, we designed a nested ensemble model using deep learning methods based on long short term memory (LSTM). Proposed Deep-LSTM ensemble model is evaluated on intensive care Covid-19 confirmed and death cases of India with different classification metrics such as accuracy, precision, recall, f-measure and mean absolute percentage error. Medical healthcare facilities are boosted with the intervention of AI as it can mimic human intelligence. Contactless treatment is possible only with the help of AI assisted automated health care systems. Furthermore, remote location self treatment is one of the key benefits provided by AI based systems.

Shastri Sourabh, Singh Kuljeet, Kumar Sachin, Kour Paramjit, Mansotra Vibhakar


Artificial intelligence, Covid-19, Deep learning, LSTM, Nested ensemble

General General

Determinants of efficiency in state-chartered financial institutions: Why financial education and freedom matter.

In Heliyon

In this paper, we verify which qualitative banking attributes can determine the level of American state-chartered Financial Institutions (FIs) and evaluate its underlying variables. The methodology followed three procedures of analysis. First, we measured banking efficiency using a two-stage SBM network data envelopment analysis (NDEA). Subsequently, we used machine learning methods to predict efficient FIs from qualitative attributes. Finally, we tested the variables related to the attributes, using a fractionated logistic regression controlled by economic-financial variables. As main results, we found that attributes linked to political-administrative localization criteria were the more important attribute in predicting if the FI was in the efficient group; we confirmed the recent findings of the literature that state that less governmental influence (freedom) is related to more efficient institutions. Besides that, we found that a population with a higher financial education have FIs with higher levels of efficiency.

de Abreu Emmanuel Sousa, Kimura Herbert


Banking efficiency, Fractional logistic regression, Machine learning, SBM DEA network, State-chartered financial institutions

General General

High-precision multiclass cell classification by supervised machine learning on lectin microarray data.

In Regenerative therapy

Introduction : Establishment of a cell classification platform for evaluation and selection of human pluripotent stem cells (hPSCs) is of great importance to assure the efficacy and safety of cell-based therapy. In our previous work, we introduced a discriminant function that evaluates pluripotency from the cells' glycome. However, it is not yet suitable for general use.

Methods : The current study aims to establish a high-precision cell classification platform introducing supervised machine learning and test the platform on glycome analysis as a proof-of-concept study. We employed linear classification and neural network to the lectin microarray data from 1577 human cells and categorized them into five classes including hPSCs.

Results : The linear-classification-based model and the neural-network-based model successfully predicted the sample type with accuracies of 89% and 97%, respectively.

Conclusions : Because of the high recognition accuracies and the small amount of computing resources required for these analyses, our platform can be a high precision conventional cell classification system for hPSCs.

Shibata Mayu, Okamura Kohji, Yura Kei, Umezawa Akihiro


Artificial intelligence, Lectin microarray, Linear classification, Neural network, Pluripotent stem cells