On-time clinical phenotype prediction based on narrative reports

Abstract

In this paper we describe a natural language processing system which is able to predict whether or not a patient exhibits a specific phenotype using the information extracted from the narrative reports associated with the patient. Furthermore, the phenotypic annotations from our report dataset were performed at the report level which allows us to perform the prediction of the clinical phenotype at any point in time during the patient hospitalization period. Our experiments indicate that an important factor in achieving better results for this problem is to determine how much information to extract from the patient reports in the time interval between the patient admission time and the current prediction time.

Figure 1.

(a) The distribution of patients with respect to the ICU day when they were first identified as positive for pneumonia. (b) The distribution of reports with timestamps in a specific time interval.

Figure 2.

Graphical representation of a data instance for the problem of on-time clinical phenotype prediction.

Figure 3.

System performance results under different configurations. (a), (b) and (c) show the baseline results of the system when considering various combinations of word and UMLS concept n-grams. (d) shows the experiments performed when using the assertion feature as well as combinations of this feature with word and concept unigrams. Finally, (e), (f), (g), and (h) illustrate the steps of a greedy algorithm for searching the most optimal threshold values which indicate what word and concept n-gram features to be selected in the learning framework.