We are currently building a medical prediction platform (Evidencio) for researchers and physicians to facilitate translation of scientific articles to medical practice (www.evidencio.com). Evidencio is a platform created for and by scientists/physicians that allows for easy external validation of prediction models as well as conversion to easy-to-use online calculators for everyday practice.
Which (type of) prediction models would be most usefull for you to add to the platform? And, as a researcher/physician, would you be interested in adding your own prediction model to the platform?
Please let me know if this reference/site is useful to you:
16 October 2001 Annals of Internal Medicine
Disease Prediction Models Aim To Guide Medical Decision Making
Tom Reynolds
When performing a history and physical examination on a patient with a disorder such as chest pain, physicians try to gauge the likelihood that each differential diagnosis explains the chief complaint. Determining the likelihood of disease as an estimate of probability has important advantages, and this will become increasingly evident as medical information systems, such as those developed for hand-held personal digital assistants (PDAs), help physicians to estimate probability at the moment of decision making.
Clinical prediction rules provide a structured approach to calculating a probability estimate by incorporating the key variables that predict whether a disease is present or may develop in the future.
Dennis
Dennis Mazur
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CitationsCitation
Reynolds T. Disease Prediction Models Aim To Guide Medical Decision Making. Ann Intern Med. 2001;135:637–640. doi: 10.7326/0003-4819-135-8_Part_1-200110160-00023
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Published: Ann Intern Med. 2001;135(8_Part_1):637-640.
DOI: 10.7326/0003-4819-135-8_Part_1-200110160-00023
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2. Childhood asthma prediction models: a systematic review.Smit HA et al. Lancet Respir Med. (2015)
3. Diagnostic prediction models for suspected pulmonary embolism: systematic review and independent external validation in primary care.Hendriksen JM et al. BMJ. (2015)
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5. Oncotarget. 2016 Jul 5;8(33):53959-53967. doi: 10.18632/oncotarget.10403. eCollection 2017 Aug 15.
Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations
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Cheng Y#1, Jiang T#1, Zhu M1, Li Z1, Zhang J1, Wang Y1, Geng L1, Liu J1, Shen W1, Wang C1, Hu Z1,2, Jin G1,2, Ma H1,2, Shen H1,2, Dai J1,2.
Author information
1
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
2
Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China.
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Abstract
In the past ten years, great successes have been accumulated by taking advantage of both candidate-gene studies and genome-wide association studies. However, limited studies were available to systematically evaluate the genetic effects for lung cancer risk with large-scale and different ethnic populations. We systematically reviewed relevant literatures and filtered out 241 important genetic variants identified in 124 articles. A two-stage case-control study within specific subgroups was performed to assess the effects [Training set: 2,331 cases vs. 3,077 controls (Chinese population); testing set: 1,937 cases vs. 1,984 controls (European population)]. Variable selection and model development were used LASSO penalized regression and genetic risk score (GRS) system. Further change in area under the receiver operator characteristic curves (AUC) made by the epidemiologic model with and without GRS was used to compare predictions. It kept 38 genetic variants in our study and the ratios of lung cancer risk for subjects in the upper quartile GRS was three times higher compared to that in the low quartile (odds ratio: 4.64, 95% CI: 3.87-5.56). In addition, we found that adding genetic predictors to smoking risk factor-only model improved lung cancer predictive value greatly: AUC, 0.610 versus 0.697 (P < 0.001). Similar performance was derived in European population and the combined two data sets. Our findings suggested that genetic predictors could improve the predictive ability of risk model for lung cancer and highlighted the application among different populations, indicating that the lung cancer risk assessment model will be a promising tool for high risk population screening and prediction.
Dennis
Dennis Mazur
The IOTA simple and advance rules for predicting ovarian cancer. Although there already is an app for that from the IOTA collaboration study group, so you might have a copyright issue. Still, having everything in one place increases visiability and the more people who use the, the better for patients
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