Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Diagnosis Prevalence vs. Efficacy in Machine-learning Based Diagnostic
  Decision Support   Many recent studies use machine learning to predict a small number of
ICD-9-CM codes. In practice, on the other hand, physicians have to consider a
broader range of diagnoses. This study aims to put these previously incongruent
evaluation settings on a more equal footing by predicting ICD-9-CM codes based
on electronic health record properties and demonstrating the relationship
between diagnosis prevalence and system performance. We extracted patient
features from the MIMIC-III dataset for each admission. We trained and
evaluated 43 different machine learning classifiers. Among this pool, the most
successful classifier was a Multi-Layer Perceptron. In accordance with general
machine learning expectation, we observed all classifiers' F1 scores to drop as
disease prevalence decreased. Scores fell from 0.28 for the 50 most prevalent
ICD-9-CM codes to 0.03 for the 1000 most prevalent ICD-9-CM codes. Statistical
analyses showed a moderate positive correlation between disease prevalence and
efficacy (0.5866).

19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
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The categories of this paper are: adap-org