Query Results

Number of results 0
Abstracts of input examples inputs.html
Complete abstracts of the Medline records given as relevant training examples. They have been ranked by classifier scores.
PubMed IDs of results results.txt
PubMed IDs and scores of classifier predictions, ranked by decreasing score.
PubMed IDs of inputs inputs.txt
PubMed IDs and scores of the relevant training examples.
Started at 2009/07/01 09:23:09 GMT
Time at which query was started
Finished at 2009/07/01 09:26:12 GMT
Time at which this file was written.
Feature score method scores_laplace_split
Name of the method used to calculate feature scores. Docstring for the method: For feature probabilities we use a Laplace prior, of 1 success and 1 failure in total, split between the classes according to size. This avoids problems with class skew.
Min Information Gain 2e-05
We exclude features with less than this value of Information Gain.
Base score -150.036343015
The log likelihood ratio of an empty article (one in which every feature failed to occur).
Prior score -15.9574558959
The log of the prior probability ratio for an article being relevant versus irrelevant (added to log likelihood ratio to obtain the final score). Equals the logit of the estimated prevalence of relevant articles in Medline (which may be estimated from the input size or specified separately).
Limit 1000
The maximum number of results to include.
Threshold -30.0
Default Naive Bayes classification threshold is zero. This threshold is the minimum log probability ratio for predicting an article to be relevant.

Feature Statistics

Quantity Relevant Docs Irrelevant Docs
Number of documents 1 17031975
Number of selected, occurring features 13 16403
Total occurrences of selected features 13 806822708
Selected features per Medline record 13.000 47.371
Of the considered feature types, 16403 features are selected out of 3703762 occurring at least once in training data. The aggressivity of selection is 225.798. The complete database lists 3703762 potential features.

Features with high TF.IDF

Features with TF.IDF above 0.2 or 0.3 could make good keywords. TF.IDF is term frequency times inverse document frequency, where we treat the set of input citations as a single document

TF-IDF Type Term Term ID Score Pos Neg
1.06 a w offenkrantz 1876377 28.97 1 17
0.70 mesh National Health Insurance, United States 46490 24.42 1 1798
0.70 mesh Psychotherapy, Brief 42633 24.30 1 2018
0.55 mesh Psychoanalytic Therapy 42632 22.46 1 12777
0.52 w Psychotherapy 24361 22.09 1 18505
0.51 issn 0002-953X 54469 21.95 1 21252
0.48 mesh Psychotherapy 24367 21.45 1 34888
0.46 mesh Cost-Benefit Analysis 2651 21.23 1 43619
0.36 w National 344 19.94 1 156656
0.27 w Care 19 18.71 1 527836
0.26 mesh United States 195 18.60 1 587144
0.25 w Health 185 18.54 1 620551
0.04 mesh Humans 124 14.82 1 10368493