A method comprising: generating a feature vector representing a speech occurrence wherein: the feature vector comprises a plurality of lingual features of the speech occurrence, and the lingual features are chosen based on accuracy in determining a disease state, and the lingual features include both word based features derived from analysis of individual words within the speech occurrence and complex features derived from analysis of the speech occurrence as a whole, wherein the lingual features are selected as features being predictive of disease state across two or more languages and wherein the complex features include at least one of mean sentence length, number of grammatical errors, variety of words, acoustic features extracted from an audio recording of the speech occurrence, inflection extracted from the audio recording, pronoun to noun ratio, and pronoun/noun to word ratio; generating a prediction of disease state by passing the feature vector as input to a decision module, the decision module comprising a cross-lingual machine learning model trained using data regarding disease state including labeled observations in each of the two or more languages; and returning the prediction to one or more user devices. generating a feature vector representing a speech occurrence wherein: the feature vector comprises a plurality of lingual features of the speech occurrence, and the lingual features are chosen based on accuracy in determining a disease state, and the lingual features include both word based features derived from analysis of individual words within the speech occurrence and complex features derived from analysis of the speech occurrence as a whole, wherein the lingual features are selected as features being predictive of disease state across two or more languages and wherein the complex features include at least one of mean sentence length, number of grammatical errors, variety of words, acoustic features extracted from an audio recording of the speech occurrence, inflection extracted from the audio recording, pronoun to noun ratio, and pronoun/noun to word ratio; the feature vector comprises a plurality of lingual features of the speech occurrence, and the lingual features are chosen based on accuracy in determining a disease state, and the lingual features include both word based features derived from analysis of individual words within the speech occurrence and complex features derived from analysis of the speech occurrence as a whole, wherein the lingual features are selected as features being predictive of disease state across two or more languages and wherein the complex features include at least one of mean sentence length, number of grammatical errors, variety of words, acoustic features extracted from an audio recording of the speech occurrence, inflection extracted from the audio recording, pronoun to noun ratio, and pronoun/noun to word ratio; generating a prediction of disease state by passing the feature vector as input to a decision module, the decision module comprising a cross-lingual machine learning model trained using data regarding disease state including labeled observations in each of the two or more languages; and returning the prediction to one or more user devices. <EOS>