Using Machine Learning To Understand Suicide: A New Approach To Classifying Australian Coroner’s Court Decisions

Abstract We aimed to demonstrate how a large collection of publicly accessible Australian Coroner’s Court case files (n=4459) (2009-2019) can be automatically classified for determination of death by suicide, presence of mental health disorder and sex of deceased via Natural Language Processing (NLP) methods - supervised machine learning and unsupervised dictionary-based and string search based approaches. We achieved superior levels of accuracy in the machine learning classification (Gradient Boosting vs. Random Forest baseline) of deaths by suicide of 83.3% (sensitivity = 85.1%, Specificity = 79.1%) and an accuracy of 98.3% for the dictionary-based classification of mental health disorder, as defined by the OCD-10 (sensitivity = 99.0%, specificity = 97.9%). Our machine learning approach automatically classified 24.2% (1078/4459) of the case files as referring to deaths by suicide while 63.7% (2940/4459) where classified as exhibiting a mental health disorder1. We employed a two-stage machine learning approach involving feature engineering, followed by predictive modelling in the second. Feature engineering involved several steps including removal of low value text, parts of speech analysis, term document weighting and topic clustering. Predictive classification involved extensive hyperparameter tuning to yield the most accurate model. We validated our models against a manually pre-coded subsample of case files, and also via binary logistic regression to test the contribution of each classified mental health disorder against determinations of deaths by suicide according to extant literature. This validation step confirmed elevated odds of suicide attributed to diagnoses of Depression, Schizophrenia and Obsessive Compulsive Disorder. Finally, we offer a short case study to demonstrate the efficacy of our approach in investigating a subset of case findings referring to suicides resulting from family violence. We offer a proof of concept model that demonstrates an objective and scalable approach to the analysis of legal texts. The use of NLP methods in analysing Coroner's Court case findings has important implications for the ongoing development of a real-time surveillance of suicide system in Australia.

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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

10.26434/chemrxiv.5513581.v1 ◽

2017 ◽

Author(s):

Sabrina Jaeger ◽

Simone Fulle ◽

Samo Turk

Keyword(s):

Machine Learning ◽

Language Processing ◽

Supervised Machine Learning ◽

Learning Approach ◽

Learning Approaches ◽

Unsupervised Machine Learning ◽

Feature Representations ◽

Machine Learning Approach ◽

The Individual ◽

Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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