A method for assessing a neuropsychiatric condition of a human subject, the method comprising determining one or more neuropsychiatric condition associated biological markers in a biological sample from the subject to provide biomarker data for the subject, the markers determined by a method comprising one or more of a polymerase chain reaction, a reverse transcription PCR reaction (RT-PCR), mass spectroscopy, high pressure liquid chromatography (HPLC), LC-MS, DNA sequencing, and an enzyme-linked, bead based, or sandwich immunoassay, generating, using one or more computer processors, a biomarker score based on the strength of the association of the biomarker data with the neuropsychiatric condition, the method further characterized by obtaining thought-marker data from the subject, the thought marker data including one or more of the subject's recorded thoughts, spoken words, transcribed speech, and writings, generating, using one or more computer processors, a thought-marker score based on the strength of the association of the thought marker data with the neuropsychiatric condition by a method comprising the steps of determining a correlation between (i) the thought marker data of the subject and (ii) a corpus of thought data pertaining to the neuropsychiatric condition, 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 generating a neuropsychiatric condition score based on the biomarker score and the thought-marker score, thereby providing a quantitative assessment of the neuropsychiatric condition of the subject. determining one or more neuropsychiatric condition associated biological markers in a biological sample from the subject to provide biomarker data for the subject, the markers determined by a method comprising one or more of a polymerase chain reaction, a reverse transcription PCR reaction (RT-PCR), mass spectroscopy, high pressure liquid chromatography (HPLC), LC-MS, DNA sequencing, and an enzyme-linked, bead based, or sandwich immunoassay, generating, using one or more computer processors, a biomarker score based on the strength of the association of the biomarker data with the neuropsychiatric condition, the method further characterized by obtaining thought-marker data from the subject, the thought marker data including one or more of the subject's recorded thoughts, spoken words, transcribed speech, and writings, generating, using one or more computer processors, a thought-marker score based on the strength of the association of the thought marker data with the neuropsychiatric condition by a method comprising the steps of determining a correlation between (i) the thought marker data of the subject and (ii) a corpus of thought data pertaining to the neuropsychiatric condition, 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 (i) the thought marker data of the subject and (ii) a corpus of thought data pertaining to the neuropsychiatric condition, 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 generating a neuropsychiatric condition score based on the biomarker score and the thought-marker score, thereby providing a quantitative assessment of the neuropsychiatric condition of the subject.