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.

Novelty, but Not Operant Aversive Learning, Enhances Fos and Egr-1 Expression in the Medial Prefrontal Cortex and Hippocampal Areas of Rats

Immediate early genes (IEG) are presumed to be activated in response to stress, novelty, and learning. Evidence supports the involvement of prefrontal and hippocampal areas in stress and learning, but also in the detection of novel events. This study examined whether a previous experience with shocks changes the pattern of Fos and Egr-1 expression in the medial prefrontal cortex (mPFC), the hippocampal cornus ammonis 1 (CA1), and dentate gyrus (DG) of adult male Wistar rats that learned to escape in an operant aversive test. Subjects previously exposed to inescapable footshocks that learned to escape from Shocks were assigned to the treated group (EXP). Subjects from Group Novelty (NOV) rested undisturbed during treatment and also learned to escape in the test. The nonshock group (NSH) rested undisturbed in both sessions. Standard immunohistochemistry procedures were used to detect the proteins in brain sections. The results show that a previous experience with shocks changed the pattern of IEG expression, then demonstrating c-fos and egr-1 induction as experience-dependent events. Compared with NSH and EXP an enhanced Fos expression was detected in the mPFC and CA1 subfield of Group NOV, which also exhibited increased Egr-1 expression in the mPFC and DG in comparison to NSH. No differences were found in the DG for Fos, or in the CA1 for Egr-1. Novelty, and not the operant aversive escape learning, seems to have generated IEG induction. The results suggest novel stimuli as a possible confounding factor in studies on Fos and/or Egr-1 expression in aversive conditions.