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Electronic Health Records

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Electronic Health Records

NLP on Clinical Psychiatric Notes

Clinical notes of psychiatric patients are a rich resource for gaining a better understanding of mental disorders and developing better phenotypes. In a master project at the Hasso-Plattner-Institute of winter term 19/20, we used natural language processing on clinical notes of electronic health records (EHR) from Mount Sinai hospital system to develop meaningful language-based representations of patients with depression. With unsupervised machine learning, we aimed to find categories that are closer to underlying mechanisms as well as subcategories that could inform treatment decisions. In a follow-up project we are now working on Using Natural Language Processing (NLP) to explore gender differences in clinical descriptions of mental health conditions. It is well known that psychiatric symptoms either differ or are perceived differently by doctors, depending on gender or ethnical background of patients. In hospitals, doctors notify these symptoms in clinical notes. Using Natural Language Processing on these notes allows to compare, on a big scale, descriptions of mental conditions between gender and ethnical groups. This information could inform and improve the diagnostic process of mental health conditions in the future.


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