A disease classification based on histopathology or clinical follow-up 2 years after imaging may false classify some patients as false negative, although they could be true negative at the time of the PET/CT scan.
The number of false positives, genes falsely classified as "differentially expressed", can be estimated based on Bonferroni correction [ 43]: at 1E-6 p value, for example, the predicted false positives were 0.07 for human (72865 × 1E-6) and 0.03 for mouse (30172 × 1E-6).
Upon inspection of the images falsely classified in the test sets, either as false positives (i.e. classified as gene expression in an anatomical component while the manual curation states that no expression is present) or false negatives, we found that several images were erroneously annotated.
This package utilizes balanced accuracy (BA) as the evaluation measure for comparing different combinations of variables, defined as B A = 1 2 T P T P + F N + T N T N + F P where (TP, TN, FP, FN) represent the number of true positives, true negatives, false positives, and false negatives classified by a particular combination of loci, respectively.
Repeated measures ANOVAs, with type of music as within-subject factor with three levels (no music, happy music, sad music), were conducted on the three scales of the SAM (valence, dominance, and arousal), the detection and identification rates for happy and sad faces, the proportion of false alarms, and the proportion of false alarms classified as happy faces.
Investigation of the false positive classified patients indicated that in several cases the classification "diabetic renal damage" may in fact be correct, but albuminuria may be under the respective criteria (see method section: 'Methods').
Different types of false signals, classified by us as noise, have also been named compositional signals, heterotachous signals, or rate signals [ 51].
Example plots showing the distribution of normal data with ranges, and the follow-up patients are given in Figs. 1 and 2 with the number of incorrectly classified false positive and false negative data shown in Table 3.
Then, we define a general loss function L (θ, d ) = f 1 dI (θ < β thresh ) + f 2 1- d )I(θ > β thresh ) Here dI(θ < β thresh ) denotes a false positive: classifying a hospital as having higher than acceptable mortality when in reality it has acceptable mortality.
Agreement between the psychiatric assessment and the Silberfeld questionnaire was poor (kappa 0.249), with a sensitivity of 35.7% and a specificity of 91.6% (see Figure 2); using the Silberfeld score, 12 patients were classified false positive and 9 false negative.
We classified false positive findings by type, and false negative findings by the first sequential data source (i.e., PubMed, SemRep output, dynamic summarization output) that did not include them.
