A computerized system for assessing a suicide attempt risk of a human subject, the system comprising: computerized means for receiving biomarker data of one or more suicide risk associated biological markers obtained from an analysis of the subject's biological sample; computerized means for receiving thought-marker data obtained pertaining to one or more of the subject's recorded thoughts, spoken words, transcribed speech, and writings; computerized means for generating a biomarker score associated with the suicide attempt risk from the biomarker data; characterized in that the system further comprises: computerized means for generating a thought-marker score associated with the suicide attempt risk from the thought-marker data by a method comprising the steps of: determining a correlation between (a) the thought marker data of the subject and (b) a corpus of thought data comprising a set of suicide notes language associated with prior completions of suicides, the correlation determined using a machine learning method implementing a classification algorithm selected from the group consisting of decision trees, classification rules, function models, and instance-based learner methods, the machine learning method comprising extracting and quantifying relevant content features of the thought marker data and creating a heterogeneous, multidimensional feature space, normalizing the feature values, and generating the thought-marker score based upon the strength of the correlation; and computerized means for calculating a suicide attempt risk score based, at least in part, upon the biomarker score and the thought-marker score. computerized means for receiving biomarker data of one or more suicide risk associated biological markers obtained from an analysis of the subject's biological sample; computerized means for receiving thought-marker data obtained pertaining to one or more of the subject's recorded thoughts, spoken words, transcribed speech, and writings; computerized means for generating a biomarker score associated with the suicide attempt risk from the biomarker data; characterized in that the system further comprises: computerized means for generating a thought-marker score associated with the suicide attempt risk from the thought-marker data by a method comprising the steps of: determining a correlation between (a) the thought marker data of the subject and (b) a corpus of thought data comprising a set of suicide notes language associated with prior completions of suicides, the correlation determined using a machine learning method implementing a classification algorithm selected from the group consisting of decision trees, classification rules, function models, and instance-based learner methods, the machine learning method comprising extracting and quantifying relevant content features of the thought marker data and creating a heterogeneous, multidimensional feature space, normalizing the feature values, and generating the thought-marker score based upon the strength of the correlation; and determining a correlation between (a) the thought marker data of the subject and (b) a corpus of thought data comprising a set of suicide notes language associated with prior completions of suicides, the correlation determined using a machine learning method implementing a classification algorithm selected from the group consisting of decision trees, classification rules, function models, and instance-based learner methods, the machine learning method comprising extracting and quantifying relevant content features of the thought marker data and creating a heterogeneous, multidimensional feature space, normalizing the feature values, and generating the thought-marker score based upon the strength of the correlation; and computerized means for calculating a suicide attempt risk score based, at least in part, upon the biomarker score and the thought-marker score.