Using cluster ensembles to identify psychiatric patient subgroups

Identification of patient subgroups is an important process for supporting clinical care in many medical specialties. In psychiatry, patient stratification is mainly done using a psychiatric diagnosis following the Diagnostic and Statistical Manual of Mental Disorders (DSM). Diagnostic categories in the DSM are however heterogeneous, and many symptoms cut across several diagnoses, leading to criticism of this approach. Data-driven approaches using clustering algorithms have recently been proposed, but have suffered from subjectivity in choosing a number of clusters and a clustering algorithm. We therefore propose to apply cluster ensemble techniques to the problem of identifying subgroups of psychiatric patients, which have previously been shown to overcome drawbacks of individual clustering algorithms. We first introduce a process guide for modelling and evaluating cluster ensembles in the form of a Meta Algorithmic Model. Then, we apply cluster ensembles to a novel cross-diagnostic dataset from the Psychiatry Department of the University Medical Center Utrecht in the Netherlands. We finally describe the clusters that are identified, and their relations to several clinically relevant variables

Using cluster ensembles to identify psychiatric patient subgroups

Identification of patient subgroups is an important process for supporting clinical care in many medical specialties. In psychiatry, patient stratification is mainly done using a psychiatric diagnosis following the Diagnostic and Statistical Manual of Mental Disorders (DSM). Diagnostic categories in the DSM are however heterogeneous, and many symptoms cut across several diagnoses, leading to criticism of this approach. Data-driven approaches using clustering algorithms have recently been proposed, but have suffered from subjectivity in choosing a number of clusters and a clustering algorithm. We therefore propose to apply cluster ensemble techniques to the problem of identifying subgroups of psychiatric patients, which have previously been shown to overcome drawbacks of individual clustering algorithms. We first introduce a process guide for modelling and evaluating cluster ensembles in the form of a Meta Algorithmic Model. Then, we apply cluster ensembles to a novel cross-diagnostic dataset from the Psychiatry Department of the University Medical Center Utrecht in the Netherlands. We finally describe the clusters that are identified, and their relations to several clinically relevant variables

Clinical and morphological practices in the diagnosis of transplant-associated microangiopathy: a study on behalf of Transplant Complications Working Party of the EBMT.

Transplant-associated thrombotic microangiopathy (TA-TMA) is a life-threatening complication of allogeneic hematopoietic stem cell transplantation (HSCT). This study evaluated clinical and morphological practices of TA-TMA diagnosis in EBMT centers. Two questionnaires, one for transplant physician and one for morphologist, and also a set of electronic blood slides from 10 patients with TA-TMA and 10 control patients with various erythrocyte abnormalities, were implemented for evaluation. Seventeen EBMT centers participated in the study. Regarding criteria used for TA-TMA diagnosis, centers reported as follows: 41% of centers used the International Working Group (IWG) criteria, 41% used "overall TA-TMA" criteria and 18% used physician's decision. The threshold of schistocytes to establish TA-TMA diagnosis in the participating centers was significantly associated with morphological results of test cases evaluations (p = 0.002). The mean number of schistocytes reported from blood slide analyses were 4.3 ± 4.5% for TA-TMA cases (range 0-19.6%, coefficient of variation (CV) 0.7) and 1.3 ± 1.6% for control cases (range 0-8.3%, CV 0.8). Half of the centers reported schistocyte levels below 4% for 7/10 TA-TMA cases. The intracenter variability was low, indicating differences in the institutional practices of morphological evaluation. In conclusion, the survey identified the need for the standardization of TA-TMA morphological diagnosis