Recognition of Schizophrenia with Regularized Support Vector Machine and Sequential Region of Interest Selection using Structural Magnetic Resonance Imaging.

Structural brain abnormalities in schizophrenia have been well characterized with the application of univariate methods to magnetic resonance imaging (MRI) data. However, these traditional techniques lack sensitivity and predictive value at the individual level. Machine-learning approaches have emerged as potential diagnostic and prognostic tools. We used an anatomically and spatially regularized support vector machine (SVM) framework to categorize schizophrenia and healthy individuals based on whole-brain gray matter densities estimated using voxel-based morphometry from structural MRI scans. The regularized SVM model yielded recognition accuracy of 86.6% in the training set of 127 individuals and validation accuracy of 83.5% in an independent set of 85 individuals. A sequential region-of-interest (ROI) selection step was adopted for feature selection, improving recognition accuracy to 92.0% in the training set and 89.4% in the validation set. The combined model achieved 96.6% sensitivity and 74.1% specificity. Seven ROIs were identified as the optimal discriminatory subset: the occipital fusiform gyrus, middle frontal gyrus, pars opercularis of the inferior frontal gyrus, anterior superior temporal gyrus, superior frontal gyrus, left thalamus and left lateral ventricle. These findings demonstrate the utility of spatial and anatomical priors in SVM for neuroimaging analyses in conjunction with sequential ROI selection in the recognition of schizophrenia.

Effect of multimorbidity on survival of patients diagnosed with heart failure: a retrospective cohort study in Singapore.

OBJECTIVE: Multimorbidity in patients with heart failure (HF) results in poor prognosis and is an increasing public health concern. We aim to examine the effect of multimorbidity focusing on type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) on all-cause and cardiovascular disease (CVD)-specific mortality among patients diagnosed with HF in Singapore. DESIGN: Retrospective cohort study. SETTING: Primary and tertiary care in three (out of six) Regional Health Systems in Singapore. PARTICIPANTS: Patients diagnosed with HF between 2003 and 2016 from three restructured hospitals and nine primary care polyclinics were included in this retrospective cohort study. PRIMARY OUTCOMES: All-cause mortality and CVD-specific mortality. RESULTS: A total of 34 460 patients diagnosed with HF from 2003 to 2016 were included in this study and were followed up until 31 December 2016. The median follow-up time was 2.1 years. Comorbidities prior to HF diagnosis were considered. Patients were categorised as (1) HF only, (2) T2DM+HF, (3) CKD+HF and (4) T2DM+CKD+HF. Cox regression model was used to determine the effect of multimorbidity on (1) all-cause mortality and (2) CVD-specific mortality. Adjusting for demographics, other comorbidities, baseline treatment and duration of T2DM prior to HF diagnosis, 'T2DM+CKD+HF' patients had a 56% higher risk of all-cause mortality (HR: 1.56, 95% CI 1.48 to 1.63) and a 44% higher risk of CVD-specific mortality (HR: 1.44, 95% CI 1.32 to 1.56) compared with patients diagnosed with HF only. CONCLUSION: All-cause and CVD-specific mortality risks increased with increasing multimorbidity. This study highlights the need for a new model of care that focuses on holistic patient management rather than disease management alone to improve survival among patients with HF with multimorbidity.

Singapore's regional health systems-a data-driven perspective on frequent admitters and cross utilization of healthcare services in three systems.

INTRODUCTION: With population health management being a priority in the Singapore, this paper aims to provide a data-driven perspective of the population health management initiatives to aid program planning and serves as a baseline for evaluation of future implemented programs. METHODS: A database with information on patient demographics, health services utilization, cost, diagnoses and chronic disease information from 2008 to 2013 for three regional health systems in Singapore was used for analysis. Patients with three or more inpatient admissions were considered as "Frequent Admitters." Health service utilization was quantified, and cross utilization of services was studied. One-year readmission rate for inpatients was studied, and a predictive model for readmission or death was developed. RESULTS: There were a total of 2.8 M patients in the database. Frequent admitters accounted for 0.9% of all patients with an average cost per patient of S$29 547. Of these, 89% had chronic diseases. Cross utilization of health services showed that 8.2% of the patients utilized services from more than one hospital with 19.6% utilizing hospital and polyclinic services in 2013. The highest risk of readmission or death was for those patients who had five or more inpatient episodes in each of the preceding 2 years. CONCLUSION: By understanding the profile of the patients and their utilization patterns in the three regional health systems, our study will help clinicians and decision makers design appropriate integrated care programs for patients with the aim of covering the healthcare needs for the enitre population across the healthcare spectrum in Singapore. Copyright © 2015 John Wiley & Sons, Ltd.