School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review.

SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.

https://www.mdpi.com/1660-4601/17/9/3176/pdf

How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic.

a correct decision in 84% and a combined bad diagnostic and management error rate of 3-2%.' We used a different approach, which requires the surgeon to categorise patients into management pathways at the time ofadmission (definitely needs operation, definitely does not require operation, uncertain). Laparoscopy was done in the uncertain group. We consider that this management approach to acute abdominal pain is more appropriate than a system based on diagnostic accuracy. We emphasise the point made by the authors that improved results during the study period are due not to the use of a computer but to accurate collection ofinformation and feedback of results to the doctors concerned. Improvement in this important area stems from interest, analysis, and feedback. Computers are one way of achieving this, rigorous analysis of decision making is another. We prefer the latter.

https://www.bmj.com/content/bmj/293/6553/1026.1.full.pdf

Computer aided diagnosis of acute abdominal pain

Big Data for Health.

Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical, modeling can provide satisfactory models, it can also fail to comprehend the intricacies contained within the data. In this paper, authors use a publicly available dataset, containing information on infected, recovered, and deceased patients in 406 locations over 51 days (22nd January 2020 to 12th March 2020). This dataset, intended to be a time-series dataset, is transformed into a regression dataset and used in training a multilayer perceptron (MLP) artificial neural network (ANN). The aim of training is to achieve a worldwide model of the maximal number of patients across all locations in each time unit. Hyperparameters of the MLP are varied using a grid search algorithm, with a total of 5376 hyperparameter combinations. Using those combinations, a total of 48384 ANNs are trained (16128 for each patient group-deceased, recovered, and infected), and each model is evaluated using the coefficient of determination (R2). Cross-validation is performed using K-fold algorithm with 5-folds. Best models achieved consists of 4 hidden layers with 4 neurons in each of those layers, and use a ReLU activation function, with R2 scores of 0.98599 for confirmed, 0.99429 for deceased, and 0.97941 for recovered patient models. When cross-validation is performed, these scores drop to 0.94 for confirmed, 0.781 for recovered, and 0.986 for deceased patient models, showing high robustness of the deceased patient model, good robustness for confirmed, and low robustness for recovered patient model.

/pdf/modeling-the-spread-of-covid-19-infection-using-a-multilayer-uyd2vslyho.pdf

Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron.

/pdf/a-systematic-review-of-early-modelling-studies-of-ebola-2mqeg8806s.pdf

A systematic review of early modelling studies of Ebola virus disease in West Africa - CORRIGENDUM.

The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system based on the pre-trained language model.1 We describe steps for preparing supervised training data and executing python scripts for named entity recognition and text category classification. We then detail the use of machine evaluation and manual validation to illustrate the effectiveness of CCIE. For complete details on the use and execution of this protocol, please refer to Wang et al.2 

https://doi.org/10.1016/j.xpro.2023.102392

Protocol for the automatic extraction of epidemiological information via a pre-trained language model

At present no adequate annotation guidelines exists for incident report learning. This study aims at utilizing multiple quantitative and qualitative evidence to validate annotation guidelines for incident reporting of medication errors. Through multiple approaches via annotator training, annotation performance evaluation, exit surveys, and user and expert interviews, a mixed methods explanatory sequential design was utilized to collect 2-stage evidence for validation. We recruited two patient safety experts to participate in piloting, three annotators to receive annotation training and provide user feedback, and two incident report system designers to offer expert comments. Regarding the annotation performance evaluation, the overall accuracy reached 97% and 90% for named entity identification and attribute identification respectively. Participants provided invaluable comments and opinions towards improving the annotation methods. The mixed methods approach created a significant evidential basis for the use of annotation guidelines for incident report of medication errors. Further expansion of the guidelines and external validity present options for future research.

Annotation Guidelines for Medication Errors in Incident Reports: Validation Through a Mixed Methods Approach

Medical incident reports (MIRs) are documents that record what happened in a medical incident. A typical MIR consists of two sections: a structured categorical part and an unstructured text part. Most texts in MIRs describe what medication was intended to be given and what was actually given, because what happened in an incident is largely due to discrepancies between intended and actual medications. Recognizing the intention of clinicians and the factuality of medication is essential to understand the causes of medical incidents and avoid similar incidents in the future. Therefore, we are developing an MIR corpus with annotation of intention and factuality as well as of medication entities and their relations. In this paper, we present our annotation scheme with respect to the definition of medication entities that we take into account, the method to annotate the relations between entities, and the details of the intention and factuality annotation. We then report the annotated corpus consisting of 349 Japanese medical incident reports.

Zoie Shui-Yee Wong

Papers

A systematic review of early modelling studies of Ebola virus disease in West Africa - CORRIGENDUM.

Protocol for the automatic extraction of epidemiological information via a pre-trained language model

Annotation Guidelines for Medication Errors in Incident Reports: Validation Through a Mixed Methods Approach

Development of a Medical Incident Report Corpus with Intention and Factuality Annotation.