Suicide prediction with natural language processing of electronic health records

Suicide attempts are one of the most challenging psychiatric outcomes and have great importance in clinical practice. However, they remain difficult to detect in a standardised way to assist prevention because assessment is mostly qualitative and often subjective. As digital documentation is increasingly used in the medical field, Electronic Health Records (EHRs) have become a source of information that can be used for prevention purposes, containing codified data, structured data, and unstructured free text. This study aims to provide a quantitative approach to suicidality detection using EHRs, employing natural language processing techniques in combination with deep learning artificial intelligence methods to create an algorithm intended for use with medical documentation in German. Using psychiatric medical files from in-patient psychiatric hospitalisations between 2013 and 2021, free text reports will be transformed into structured embeddings using a German trained adaptation of Word2Vec, followed by a Long-Short Term Memory (LSTM) - Convolutional Neural Network (CNN) approach on sentences of interest. Text outside the sentences of interest will be analysed as context using a fixed size ordinally-forgetting encoding (FOFE) before combining these findings with the LSTM-CNN results in order to label suicide related content. This study will offer promising ways for automated early detection of suicide attempts and therefore holds opportunities for mental health care.