A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders

While there are several accepted screening measures for identifying those with a bipolar disorder, variations in overall classification rates argue for the pursuit of a more discriminating measure. Extant measures, as well as the DSM-5, rate each diagnostic criterion as having equivalent weighting values; an approach which may compromise diagnostic assignment if symptoms vary considerably in their diagnostic sensitivity. We therefore sought to develop a new measure and examine whether a weighted rating scale was superior to one assigning equivalent weightings to each item.

An international sample of 165 bipolar patients and a comparison sample of 29 unipolar patients completed a measure assessing 96 putative manic/hypomanic symptoms. A previous machine learning analysis had identified the twenty most discriminating items. In this study, analysis was undertaken involving only the ten most discriminating items.

Whether items were scored as each having equivalent value or as weighted by their machine learning-generated values, classificatory accuracy was extremely high (in the order of 96%). Analyses also identified optimal cut-off scores. High classificatory accuracy was also obtained when scores for separate bipolar I and bipolar II groups were compared with scores from the unipolar group.

The sample consisted of comparatively few unipolar patients.

The ten-item set allows a new measure for researchers to evaluate, while the items should assist clinician assessment as to whether a patient has a bipolar or unipolar mood disorder.