Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career

Horizons and Perspectives eHealth

EHealth platform represents the combined use of IT technologies and electronic communications in the health field, using data (electronically transmitted, stored and accessed) with a clinical, educational and administrative purpose, both locally and distantly. eHealth has the significant capability to increase the movement in the direction of services centered towards citizens, improving the quality of the medical act, integrating the application of Medical Informatics (Medical IT), Telemedicine, Health Telematics, Telehealth, Biomedical engineering and Bioinformatics. Supporting the creation, development and recognition of a specific eHealth zone, the European Union policies develop through its programs FP6 and FP7, European-scale projects in the medical information technologies (the electronic health cards, online medical care, medical web portals, trans-European nets for medical information, biotechnology, generic instruments and medical technologies for health, ICT mobile systems for remote monitoring). The medical applications like electronic health cards ePrescription, eServices, medical eLearning, eSupervision, eAdministration are integral part of what is the new medical branch-eHealth, being in a continuous expansion due to the support from the global political, financial and medical organizations; the degree of implementation of the eHealth platform varying according to the development level of the communication infrastructure, allocated funds, intensive political priorities and governmental organizations opened to the new IT challenges.eHealth, telemedicine, telehealth, bioinformatics, telematics

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach