Rapid impact assessments of COVID-19 control measures against the Delta variant and short-term projections of new confirmed cases in Vietnam.

As of 2020, the cumulative number of COVID-19 cases recorded in Vietnam was less than 1500, proving the success of COVID-19 control in Vietnam [1]. Vietnam has been recognized as one of the few countries that successfully controlled COVID-19 in 2020 [2]. Several recent articles have summarised a set of lessons learned, the so-called “Zero-new-case-approach”. These included (i) a rapid and coordinated public health response with a decentralized health care system [3]; (ii) massive quarantine and targeted lockdown; (iii) third-degree contact tracing; (iv) centralized patient management; (v) early school closures and robust border controls; (vi) mask policies and 5K message (5K refers to use face masks in public places, disinfect regularly, keep distance, stop gathering, and make health declaration); and (vii) innovative mass testing strategies in the resource-constraint system (sample pooling strategy of PCR test with 2-7 swaps) [4], These “Zero-newcase-approach” strategies all focused on the non-pharmaceutical aspect of disease control. They aimed to maintain zero community transmission by establishing a comprehensive public surveillance system and enacted drastic measures with the support of the police and military forces. © 2021 THE AUTHOR(S) JoGH 2021 ISoG

Gold Standard/Manual Reviewed Annotated Datasets for Technical Validation

This page shares the technical validation datasets used to evaluate a Large Dataset of Annotated Incident Reports on Medication Errors and its machine annotator. The files contain in this repository include the IFMIR gold standard dataset (CrossValid_IFMIR_522.xlsx), randomly sampled labeled incident reports from 2010 – 2020  (InternalValid_JQ2010-20_40.xlsx), randomly sampled labeled incident reports from 2021 (ExternalValid_JQ2021_20.xlsx) and  Error-free reports  (Error_analysis.xlsx).    To use any of these datasets, one should also cite this original data source: Medical Adverse Event Information Collection Project [Iryō jiko jōhō shūshū-tō jigyō]　 Japan Council for Quality Health Care; 2022 [Available from: https://www.med-safe.jp/index.html.]</p

Machine annotator

This is a machine annotator for Japanese free text incident reports of medication errors. This pipeline has been tested to be stable and works best on Google Colab. The machine annotator utilizes CUDA, so please set the Runtime type to 'GPU'.SETUP:(1) Please mount the shared files (including "wiki-ja.model", "config.json", "model_entity_220309.bin", and "model_3_220310_2.bin") accordingly into "TOKENIZER_MODEL", "BERT_CONIFG_FILE", "BERT_PRETRAINED_MODEL", "MODEL_SAVE_PATH".(2) Next please set up input/output file paths:"in_dir": the input file path, it should be a xlsx file with two columns: "id" and "report", see the provided sample freetext.xlsx."out_dir": the output file path, it will generate the entity-level predicted output from the trained machine-annotator.RUN:Please run the code chunk by chunk and you will find the entity-level annotation in "out_dir".For any inquiries, please email to Dr Zoie SY Wong ([email protected])</p

Annotation guidelines for incident reports of medication errors

Annotation Guidelines for Incident Reports of Medication Errors (for English reports and Japanese reports)June 1, 2023 (V.1.0.3)Prepared by the ‘AI for Patient Safety’ team, led by Dr Zoie Wong (email: [email protected]) at the Graduate School of Public Health, St. Luke’s International University.</p

Epidemiologic information discovery from open-access COVID-19 case reports via pretrained language model.

Funder: Bill and Melinda Gates FoundationFunder: Minderoo FoundationAlthough open-access data are increasingly common and useful to epidemiological research, the curation of such datasets is resource-intensive and time-consuming. Despite the existence of a major source of COVID-19 data, the regularly disclosed case reports were often written in natural language with an unstructured format. Here, we propose a computational framework that can automatically extract epidemiological information from open-access COVID-19 case reports. We develop this framework by coupling a language model developed using deep neural networks with training samples compiled using an optimized data annotation strategy. When applied to the COVID-19 case reports collected from mainland China, our framework outperforms all other state-of-the-art deep learning models. The information extracted from our approach is highly consistent with that obtained from the gold-standard manual coding, with a matching rate of 80%. To disseminate our algorithm, we provide an open-access online platform that is able to estimate key epidemiological statistics in real time, with much less effort for data curation