Interpretability on clinical analysis from Pattern Disentanglement Insight

Abstract

Diagnosis of a clinical condition can help medical professionals save time in making a clinical diagnosis and prevent overlooking risks. Therefore we explore the problem using free-text medical notes recorded in electronic health records (EHR). MIMIC III is a de-identified EHR database containing observations from over 40,000 patients in critical care units. Since the text corpus is unstructured in the non-database table format, existing machine learning models may be ineffective at interpreting the results, which is often desirable for clinical diagnosis. Hence, in this paper, we proposed a text mining and pattern discovery solution to discover strong association patterns from patient discharge summaries and the code of international classification of diseases (ICD9-code). The proposed approach offers a straightforward interpretation of the underlying relation of patient characteristics in an unsupervised machine learning setting. The clustering results outperform the baseline clustering algorithm and are comparable to baseline supervised methods.

Tasks

Reproductions