Invariant prediction rules and an adequate statistic

1981 ◽  
Vol 33 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Yoshikazu Takada
Keyword(s):  
Prediction Rules ◽  
Adequate Statistic

Kardiovaskuläre «Prediction rules»: Was sind Probleme bei der Anwendung? – Resultate der Diskussion in einem Workshop

Praxis ◽  
2006 ◽  
Vol 95 (45) ◽  
pp. 1751-1756
Author(s):  
Eichler ◽  
Biller-Andorno ◽  
Traindl ◽  
Tschudi ◽  
Bachmann ◽  
...  
Keyword(s):  
Prediction Rules

«Guidelines» zur Primärprävention kardiovaskulärer Erkrankungen empfehlen die Bestimmung des kardiovaskulären Gesamtrisikos mittels Risikokalkulatoren (sog. «Prediction rules»). Während die «Prediction rules» als Messinstrumente eingesetzt werden, geben die «Guidelines» Handlungsanleitungen, welche Konsequenzen aus der Berechnung gezogen werden sollten. An einer Tagung wurden Fragen zur Anwendung von «Prediction rules» in der Grundversorgung diskutiert. Die Anwendung scheint in der Praxis einfach zu sein, doch sind einige Barrieren vorhanden, diese Instrumente einzusetzen. Teilweise ist nicht bekannt, wie zuverlässig das Risiko überhaupt berechnet werden kann, teilweise fehlen in den «Prediction rules» Risikofaktoren (z.B. Adipositas), die intuitiv doch wichtig sind und drittens muss zwischen «Prediction rules» und «Guidelines» differenziert werden. In diesem Artikel werden, sofern vorhanden, Antworten auf Fragen, die an dieser Tagung aufgekommen sind, beschrieben.

scholarly journals 61. Using Machine Learning for Prediction of Poor Clinical Outcomes in Adult Patients Hospitalized with COVID-19

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S162-S163
Author(s):  
Guillermo Rodriguez-Nava ◽  
Daniela Patricia Trelles-Garcia ◽  
Maria Adriana Yanez-Bello ◽  
Chul Won Chung ◽  
Sana Chaudry ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Operating Characteristic ◽  
Decision Rules ◽  
Training Model ◽  
Inpatient Mortality ◽  
Prediction Rules ◽  
Testing Model ◽  
Measures Of Performance

Abstract Background As the ongoing COVID-19 pandemic develops, there is a need for prediction rules to guide clinical decisions. Previous reports have identified risk factors using statistical inference model. The primary goal of these models is to characterize the relationship between variables and outcomes, not to make predictions. In contrast, the primary purpose of machine learning is obtaining a model that can make repeatable predictions. The objective of this study is to develop decision rules tailored to our patient population to predict ICU admissions and death in patients with COVID-19. Methods We used a de-identified dataset of hospitalized adults with COVID-19 admitted to our community hospital between March 2020 and June 2020. We used a Random Forest algorithm to build the prediction models for ICU admissions and death. Random Forest is one of the most powerful machine learning algorithms; it leverages the power of multiple decision trees, randomly created, for making decisions. Results 313 patients were included; 237 patients were used to train each model, 26 were used for testing, and 50 for validation. A total of 16 variables, selected according to their availability in the Emergency Department, were fit into the models. For the survival model, the combination of age >57 years, the presence of altered mental status, procalcitonin ≥3.0 ng/mL, a respiratory rate >22, and a blood urea nitrogen >32 mg/dL resulted in a decision rule with an accuracy of 98.7% in the training model, 73.1% in the testing model, and 70% in the validation model (Table 1, Figure 1). For the ICU admission model, the combination of age < 82 years, a systolic blood pressure of ≤94 mm Hg, oxygen saturation of ≤93%, a lactate dehydrogenase >591 IU/L, and a lactic acid >1.5 mmol/L resulted in a decision rule with an accuracy of 99.6% in the training model, 80.8% in the testing model, and 82% in the validation model (Table 2, Figure 2). Table 1. Measures of Performance in Predicting Inpatient Mortality Conclusion We created decision rules using machine learning to predict ICU admission or death in patients with COVID-19. Although there are variables previously described with statistical inference, these decision rules are customized to our patient population; furthermore, we can continue to train the models fitting more data with new patients to create even more accurate prediction rules. Figure 1. Receiver Operating Characteristic (ROC) Curve for Inpatient Mortality Table 2. Measures of Performance in Predicting Intensive Care Unit Admission Figure 2. Receiver Operating Characteristic (ROC) Curve for Intensive Care Unit Admission Disclosures All Authors: No reported disclosures

Should a Head-Injured Child Receive a Head CT Scan? A Systematic Review of Clinical Prediction Rules

PEDIATRICS ◽  
2009 ◽  
Vol 124 (1) ◽  
pp. e145-e154 ◽  
Author(s):  
J. L. Maguire ◽  
K. Boutis ◽  
E. M. Uleryk ◽  
A. Laupacis ◽  
P. C. Parkin
Keyword(s):  
Systematic Review ◽  
Ct Scan ◽  
Clinical Prediction ◽  
Clinical Prediction Rules ◽  
Injured Child ◽  
Prediction Rules ◽  
Head Ct ◽  
Head Injured
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