  Drug-induced liver injury (DILI) describes the adverse effects of drugs that
damage liver. Life-threatening results including liver failure or death were
also reported in severe DILI cases. Therefore, DILI-related events are strictly
monitored for all approved drugs and the liver toxicity became important
assessments for new drug candidates. These DILI-related reports are documented
in hospital records, in clinical trial results, and also in research papers
that contain preliminary in vitro and in vivo experiments. Conventionally, data
extraction from previous publications relies heavily on resource-demanding
manual labelling, which considerably decreased the efficiency of the
information extraction process. The recent development of artificial
intelligence, particularly, the rise of natural language processing (NLP)
techniques, enabled the automatic processing of biomedical texts. In this
study, based on around 28,000 papers (titles and abstracts) provided by the
Critical Assessment of Massive Data Analysis (CAMDA) challenge, we benchmarked
model performances on filtering out DILI literature. Among four word
vectorization techniques, the model using term frequency-inverse document
frequency (TF-IDF) and logistic regression outperformed others with an accuracy
of 0.957 with our in-house test set. Furthermore, an ensemble model with
similar overall performances was implemented and was fine-tuned to lower the
false-negative cases to avoid neglecting potential DILI reports. The ensemble
model achieved a high accuracy of 0.954 and an F1 score of 0.955 in the
hold-out validation data provided by the CAMDA committee. Moreover, important
words in positive/negative predictions were identified via model
interpretation. Overall, the ensemble model reached satisfactory classification
results, which can be further used by researchers to rapidly filter
DILI-related literature.
