Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Metabolic profile of patients with diabetes in Barbalha, Brazil

Introduction: Modern life has imposed to people sedentary habits and excessive food consumption, what results into an increase of the incidence of metabolic diseases, which promote the development of atherosclerosis. Objectives: We aimed to evaluate the metabolic profile of diabetic patients assisted by the endocrinology service from Ceara Federal University, located in Barbalha, Brazil. Methods: This is a transversal and retrospective study, based on the analysis of patient records. 119 records were reviewed and 35 of them were selected, according to their registers about anthropometric and laboratorial measuring. Results and discussion: Among the selected records, 65.71% were female patients. It was observed a positive relationship between age and the level of triglycerides, between LDL-cholesterol and the use of tobacco and between blood glucose and glycated hemoglobin. Conclusion: The superposition of risk factor in this group shows the necessity of an integrated assistance and a follow-up about their metabolic profile, aiming to mitigate or retard serious circulatory pathologies.

Impact of sunlight on the age of onset of bipolar disorder

Bauer M, Glenn T, Alda M, Andreassen OA, Ardau R, Bellivier F, Berk M, Bjella TD, Bossini L, Del Zompo M, Dodd S, Fagiolini A, Frye MA, Gonzalez-Pinto A, Henry C, Kapczinski F, Kliwicki S, Konig B, Kunz M, Lafer B, Lopez-Jaramillo C, Manchia M, Marsh W, Martinez-Cengotitabengoa M, Melle I, Morken G, Munoz R, Nery FG, ODonovan C, Pfennig A, Quiroz D, Rasgon N, Reif A, Rybakowski J, Sagduyu K, Simhandl C, Torrent C, Vieta E, Zetin M, Whybrow PC. Impact of sunlight on the age of onset of bipolar disorder. Bipolar Disord 2012: 14: 654663. (c) 2012 The Authors. Journal compilation (c) 2012 John Wiley & Sons A/S. Objective: Although bipolar disorder has high heritability, the onset occurs during several decades of life, suggesting that social and environmental factors may have considerable influence on disease onset. This study examined the association between the age of onset and sunlight at the location of onset. Method: Data were obtained from 2414 patients with a diagnosis of bipolar I disorder, according to DSM-IV criteria. Data were collected at 24 sites in 13 countries spanning latitudes 6.3 to 63.4 degrees from the equator, including data from both hemispheres. The age of onset and location of onset were obtained retrospectively, from patient records and/or direct interviews. Solar insolation data, or the amount of electromagnetic energy striking the surface of the earth, were obtained from the NASA Surface Meteorology and Solar Energy (SSE) database for each location of onset. Results: The larger the maximum monthly increase in solar insolation at the location of onset, the younger the age of onset (coefficient= -4.724, 95% CI: -8.124 to -1.323, p = 0.006), controlling for each countrys median age. The maximum monthly increase in solar insolation occurred in springtime. No relationships were found between the age of onset and latitude, yearly total solar insolation, and the maximum monthly decrease in solar insolation. The largest maximum monthly increases in solar insolation occurred in diverse environments, including Norway, arid areas in California, and Chile. Conclusion: The large maximum monthly increase in sunlight in springtime may have an important influence on the onset of bipolar disorder.