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Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow‐up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ‐12). Predictor variables included structured items (e.g., relating to sleep and well‐being) and responses to one unstructured question, “how do you feel today?” We compared NLP‐based models using the unstructured question with logistic regression prediction models using structured data. The PPV, sensitivity, and specificity for NLP‐based models of suicidal ideation were 0.61, 0.56, and 0.57, respectively, compared to 0.73, 0.76, and 0.62 of structured data‐based models. The PPV …