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- Get e-book Warum tötest du, Zaid? (German Edition)
- Jürgen Todenhöfer - Wikipedia
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Example MI 3 thresholds identifying low- and high-risk patients in the training set were 1. Using machine learning, MI 3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low- and high-risk patients who may benefit from earlier clinical decisions. The myocardial-ischemic-injury-index MI 3 algorithm generates a value that takes into consideration age, sex, paired cardiac troponin I concentrations, and rate of change in troponin concentration, to estimate the negative and positive predictive value for each patient value associated with these measures.
This represents one of the first effective demonstrations of how machine learning could be used to guide clinical decision making in patients with suspected acute coronary syndrome. The MI 3 algorithm is more versatile than existing algorithms because the former is not dependent on fixed cardiac troponin thresholds, does not require serial testing to be performed at specific time points, and recognizes that different healthcare systems have different priorities and tolerances of risk.
Prospective studies are now required to evaluate patient outcomes and resource use after implementation of the MI 3 algorithm into clinical practice. The use of cardiac troponin testing in clinical practice is evolving rapidly. These strategies have been incorporated into accelerated diagnostic pathways that advocate earlier troponin measurement at presentation and 1 to 3 hours later to facilitate prompt diagnosis and treatment in those with myocardial infarction or to expedite discharge in those without.
The performance of these pathways varies across different populations, reflecting variation in cardiac troponin concentrations with age and sex. The analysis code for this study is available on request. The algorithm is proprietary and subject to a patent application, but we can share it with researchers who agree to use it only for research purposes with a data sharing agreement.
This study was an analysis of prospectively collected data from multiple centers to train and test the MI 3 algorithm to predict the diagnosis of type 1 myocardial infarction. The training set comprised data from 2 cohorts 14 , 24 and the test set comprised data from 7 cohorts of patients attending the emergency department with suspected myocardial infarction. Training and testing are the nomenclature of machine learning and are analogous to derivation and validation in studies of new diagnostic biomarkers.
MI 3 incorporates age, sex, paired cardiac troponin I concentrations at presentation and at another early, yet flexible, time point, and rate of change of cardiac troponin I concentration. These variables features were selected a priori because they are 1 objective and automatically captured from electronic hospital records, 2 include serial measurements as recommended by guidelines, and 3 associated with the diagnosis of myocardial infarction.
MI 3 computes a value from 0 to the MI 3 value , which reflects the likelihood of a diagnosis of type 1 myocardial infarction for each patient during hospitalization higher values indicate greater likelihood.
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The algorithm uses an embedded reference table to report for each individual patient estimates of sensitivity, negative predictive value NPV , specificity, and positive predictive value PPV of the diagnosis for a given MI 3 value. MI 3 was developed on the training data set by Abbott Diagnostics using a machine learning technique called gradient boosting.
This technique iteratively trains a set of sequential weak learners here decision trees using the provided features to map onto the outcome whether the patient was or was not diagnosed with myocardial infarction. For further details regarding the gradient boosting method, see also the online-only Data Supplement. The algorithm was provided to an independent statistician, J. Patients presenting with symptoms suggestive of myocardial infarction in whom serial cardiac troponin measurements were obtained were included.
Cohorts were identified for inclusion if they were prospective, had cardiac troponin I concentrations measured with the Abbott ARCHITECT STAT high-sensitivity assay Abbott Diagnostics, Chicago, IL at presentation and at a second time point approximately 1 to 3 hours later details in the online-only Data Supplement , the final diagnosis was adjudicated according to the Universal Definition of Myocardial Infarction, 5 and ethical approval permitted sharing of individual patient-level data Table I in the online-only Data Supplement.
All cohort studies were conducted in accordance with the Declaration of Helsinki and approved by the local research ethics committee or institutional review board. Written informed consent was obtained where this was required. All adjudication was completed before developing the MI 3 algorithm.
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The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction during the index admission. Although high-sensitivity cardiac troponin I was measured in all patients, other cardiac troponin assays were used for adjudication in some cohorts Table I in the online-only Data Supplement. A gradient boosting model was developed using predefined features age, sex, paired cardiac troponin I concentrations at presentation and at another early, yet flexible, time point, and rate of change of cardiac troponin I concentration to estimate the likelihood of a diagnosis of type 1 myocardial infarction.
Once the model was trained, it was used to generate MI 3 values for each patient in the test sets. We describe algorithm performance in the test set by 1 visual inspection of a calibration curve to show how accurately MI 3 values estimate the likelihood of myocardial infarction and 2 by the area under the receiver operating characteristic curve AUC to quantify how well the MI 3 values discriminated between those with and without myocardial infarction.
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MI 3 is designed to be used as a continuous measure. However, we recognize that in this field most tools rely on thresholds to guide clinical decisions. Therefore, as illustrative examples of how an individual hospital may choose to use MI 3 , we demonstrate its diagnostic performance at 2 exemplar MI 3 value thresholds.
These diagnostic criteria were prespecified and based on an international survey of acceptable risk by emergency department physicians, 32 and prior prospective studies defining risk stratification thresholds for high-sensitivity cardiac troponin. All analyses were performed in R version 3. Prespecified subgroup analyses were performed by age, sex, comorbidities coronary artery disease, diabetes mellitus, hypertension, current smoking , time from symptom onset to first sample draw, time between tests, and the presence or absence of myocardial ischemia on the electrocardiogram.
Performance of the algorithm was also evaluated for the outcomes of type 1 myocardial infarction within the next 30 days and for type 1 or 2 myocardial infarction on index admission. The training set comprised patients of whom Of these patients, There were no missing data for any of the variables used in the training and testing sets.
Patients in the testing set were younger, less likely to have known coronary artery disease, but more likely to smoke cigarettes, have diabetes mellitus, dyslipidemia, or a family history of coronary artery disease than those in the training set. Table 1. Baseline Characteristics of Training and Testing Sets. MI 3 indicates myocardial-ischemic-injury-index. The MI 3 algorithm was well calibrated and discriminated between those with and without type 1 myocardial infarction AUC, 0.
Figure 1. Calibration and discrimination of the myocardial-ischemic-injury-index MI 3 algorithm. Calibration of the MI 3 algorithm with the observed proportion of patients with type 1 myocardial infarction in the test data set A. Each point represents patients. The dashed lines represent perfect calibration. Receiver operating characteristic curve showing discrimination of the MI 3 algorithm in the test data set B.click
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Some MI 3 values shown for illustrative purposes only. There was no difference in AUCs for those presenting within 3 hours 0. The AUCs were higher in patients with no prior history of coronary artery disease, diabetes mellitus, or hypertension compared with patients with these comorbidities. The AUC was higher in younger compared with older patients and in those with no myocardial ischemia on the ECG compared with those with ischemia. The MI 3 threshold values from the training set that corresponded to our prespecified diagnostic performance metrics were 1.
In the test set, MI 3 values of 1. MI 3 values of Table 2. Sensitivity and NPV thresholds divide the population into low-risk and not-low-risk groups ie, they do not determine a high-risk group. Similarly, specificity and PPV thresholds divide the population into high-risk and not-high-risk groups ie, they do not determine a low-risk group. These 2 exemplar thresholds were used for all subsequent analysis.
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The MI 3 threshold value of 1. The MI 3 threshold value of In addition to the The MI 3 value discriminated between those patients with and without type 1 myocardial infarction within the next 30 days with an AUC of 0. Threshold values of 1. The MI 3 value discriminated between those with and without type 1 or type 2 myocardial infarction with an AUC of 0.
The example low-risk MI 3 threshold, 1. The example high-risk threshold, In all patients in the test set, the 99th percentile upper reference limit at any time-point identified This pathway identified Use of these thresholds identified Figure 2. URL indicates upper reference limit.
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Figure 3. The algorithm was well calibrated, and the overall diagnostic performance was identical in both training and test data sets. This study has several unique and important characteristics. First, this technique provides an individualised and precise assessment of risk by using age, sex, and paired high-sensitivity cardiac troponin I concentrations and allows for the complex and nonlinear ways in which these variables may interact.
This contrasts with contemporary algorithms in clinical use which are based on fixed time-points for sampling, fixed troponin thresholds, and do not account for any interaction between input variables.