Predicting post-discharge self-harm incidents using disease comorbidity networks: A retrospective machine learning study

Abstract

Background: Self-harm is preventable if the risk can be identified early. The co-occurrence of multiple diseases is related to self-harm risk. This study develops a comorbidity network-based deep learning framework to improve the prediction of individual self-harm.

Methods: Between 01/01/2007-12/31/2010, we obtained 2,323 patients with self-harm records and 46,460 randomly sampled controls from 1,764,094 inpatients across 44 public hospitals in Hong Kong. 80% of the samples were randomly selected for model training, and the remaining 20% were set aside for model testing. We propose a novel patient embedding method, namely Dx2Vec (Diagnoses to Vector), based on the comorbidity network constructed by all historical diagnoses. Dx2Vec represents the comorbidity patterns among diseases and temporal patterns of historical admissions for each patient.

Results: Experiments demonstrate that the Dx2Vec-based model outperforms the baseline deep learning model in identifying patients who would self-harm within 12 months (C-statistic: 0.89). The precision is 0.54 for positive cases and 0.98 for negative cases, whilst the recall is 0.72 for positive cases and 0.96 for negative cases. The model extracted the most predictive diagnoses, and pairwise comorbid diagnoses to help medical professionals identify patients with risk.

Limitations: The inpatient data does not contain lab test information.

Conclusions: Incorporation of a disease comorbidity network can significantly improve self-harm prediction performance, indicating that it is critical to consider comorbidity patterns in self-harm screening and prevention programs. The findings have the potential to be translated into effective self-harm screening systems.