Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity research is Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal, and sparse. Though existing studies on learning patient similarity from EHRs have shown being useful in solving real clinical problems, their applicability is limited due to the lack of medical interpretations. Moreover, most previous methods assume a vector-based representation for patients, which typically requires aggregation of medical events over a certain time period. As a consequence, temporal information will be lost. In this paper, we propose a patient similarity evaluation framework based on the temporal matching of longitudinal patient EHRs. Two efficient methods are presented, unsupervised and supervised, both of which preserve the temporal properties in EHRs. The supervised scheme takes a convolutional neural network architecture and learns an optimal representation of patient clinical records with medical concept embedding. The empirical results on real-world clinical data demonstrate substantial improvement over the baselines. We make our code and sample data available for further study.
Clustering of gene expression data simplifies subsequent data analyses and forms the basis of numerous approaches for biomarker identification, prediction of clinical outcome, and personalized therapeutic strategies. The most popular clustering methods such as K-means and hierarchical clustering are intuitive and easy to use, but they require arbitrary choices on their various parameters (number of clusters for K-means, and a threshold to cut the tree for hierarchical clustering). Human disease gene expression data are in general more difficult to cluster efficiently due to background (genotype) heterogeneity, disease stage and progression differences and disease subtyping; all of which cause gene expression datasets to be more heterogeneous. Spectral clustering has been recently introduced in many fields as a promising alternative to standard clustering methods. The idea is that pairwise comparisons can help reveal global features through the eigen techniques. In this paper, we developed a new recursive K-means spectral clustering method (ReKS) for disease gene expression data. We benchmarked ReKS on three large-scale cancer datasets and we compared it to different clustering methods with respect to execution time, background models and external biological knowledge. We found ReKS to be superior to the hierarchical methods and equally good to K-means, but much faster than them and without the requirement for a priori knowledge of K. Overall, ReKS offers an attractive alternative for efficient clustering of human disease data. We propose a mixture model for text data designed to capture underlying structure in the history of present illness section of electronic medical records data. Additionally, we propose a method to induce bias that leads to more homogeneous sets of diagnoses for patients in each cluster. We apply our model to a collection of electronic records from an emergency department and compare our results to three other relevant models in order to assess performance. Results using standard metrics demonstrate that patient clusters from our model are more homogeneous when compared to others, and qualitative analyses suggest that our approach leads to interpretable patient sub-populations when applied to real data. Finally, we demonstrate an example of our patient clustering model to identify adverse drug events. Abstract Background Little is known about how patients rate their health perceptions. Our objectives were to identify systematic multivariate patterns of perceptions using cluster analysis, and to investigate associations among the clusters, psychosocial characteristics and medication nonadherence. Methods Demographic, clinical and psychosocial data on 200 patients with inflammatory bowel disease (IBD) were collected prior to the index office visit and health perceptions were collected afterwards. Cluster analysis using a k -means method was used to identify subgroups of patients based on their responses to the Patient–Physician Discordance Scales (PPDS), an instrument that assesses perceptions of health status and of the clinical visit. Results We identified five different patient groups: a “healthy, not distressed, good communication, low expectation for medication testing” group; a “healthy, relatively distressed, good communication, high expectation for medication, low expectation for testing” group; a “symptomatic, distressed, good communication, high expectation for medication testing” group; a “healthy, not distressed, good communication, high expectation for medication testing” group; and a “relatively healthy, relatively distressed, poor communication, low expectation for medication testing” group. After adjustment for age, sex, language, form of IBD, and disease activity, statistically significant between-clusters differences were found in psychological distress, social support satisfaction and medication nonadherence. Conclusions Distinct patterns of patients' health perceptions correlated with psychological health and adherence to treatment. This categorization may be used to help identify patients at higher risks for ineffective communication and nonadherence to medication.