DIAGONSTIC PREDICTION AFTER COVID
Abstract : To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia. Design Rapid systematic review and critical appraisal. Data sources PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).
? The hope is that AI can accelerate both the processes of discovering new drugs as well as for repurposing existing drugs.
? Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones.
? There has been promising progress with a number of notable activities recognizing the importance of building and sharing existing datasets and information about the epidemic.
? One of the first has been the World Health Organization’s (WHO) Global Research on Coronavirus disease database, which also provides links to other similar initiatives.
? This is a well known problem when building prediction models and increases the risk of overfitting the model.
? Covid-19 prediction problems will often not present as a simple binary classification task.
? By pointing to the most important methodological challenges and issues in design and reporting of the currently available models, we hope to have provided a useful starting point for further studies aiming to develop new models, or to validate and update existing ones.
? It is not only the lack of historical data but also the problems with using “big data”, e.g., harvested from social media, that have shown to be problematic.
• The fear is that once the outbreak is over, that erosion of data privacy would not be rolled back and that governments would continue to use their improved ability to survey their populationsand use the data obtained in the fight against COVID-19 for other purposes.
• Models ranging from rule based scoring systems to advanced machine learning models (deep learning) have been proposed and published in response to a call to share relevant covid-19 research findings rapidly and openly to inform the public health response and help save lives.
• AI can, for present purposes, be defined as Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction.
? We focus on performance statistics as estimated from the strongest available form of validation.
? All models reported good to excellent predictive performance, but all were appraised to have high risk of bias owing to a combination of poor reporting and poor methodological conduct for participant selection, predictor description, and statistical methods used.
? A high risk of bias implies that these models will probably perform worse in practice than the performance reported by the researchers.
? This target population must also be carefully described so that the performance of the developed or validated model can be appraised in context, and users know which people the model applies to when making predictions.
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