A Survey of the Job Profiles of Biomedical Informatics Graduates.

BACKGROUND: In 2003, the University of São Paulo established the first Biomedical Informatics (BMI) undergraduate course in Brazil. Our mission is to provide undergraduate students with formal education on the fundamentals of BMI and its applied methods. This undergraduate course offers theoretical aspects, practical knowledge and scientifically oriented skills in the area of BMI, enab- ling students to contribute to research and methodical development in BMI. Course coordinators, professors and students frequently evaluate the BMI course and the curriculum to ensure that alumni receive quality higher education. OBJECTIVES: This study investigates (i) the main job activities undertake by USP BMI graduates, (ii) subjects that are fundamental important for graduates to pursue a career in BMI, and (iii) the course quality perceived by the alumni. METHODS: Use of a structured questionnaire to conduct a survey involving all the BMI graduates who received their Bachelor degree before July, 2015 (attempted n = 205). RESULTS: One hundred and forty-five graduates (71 %) answered the questionnaire. Nine out of ten of our former students currently work as informaticians. Seventy-six graduates (52 %) work within the biomedical informatics field. Fifty-five graduates (38 %) work outside the biomedical informatics field, but they work in other IT areas. Ten graduates (7 %) do not work with BMI or any other informatics activities, and four (3 %) are presently unemployed. Among the 145 surveyed BMI graduates, 46 (32 %) and seven (5 %) hold a Master's degree and a PhD degree, respectively. Database Systems, Software Engineering, Introduction to Computer Science, Object-Oriented Programming, and Data Structures are regarded as the most important subjects during the higher education course. The majority of the graduates (105 or 72 %) are satisfied with the BMI education and training they received during the undergraduate course. CONCLUSIONS: More than half of the graduates from our BMI course work in their primary education area. Besides technical adequacy, the diverse job profiles, and the high level of satisfaction of our graduates indicate the importance of undergraduate courses specialized in the BMI domain are of utmost importance.

Prediction of antimicrobial activity of synthetic peptides by a decision tree model.

Antimicrobial resistance is a persistent problem in the public health sphere. However, recent attempts to find effective substitutes to combat infections have been directed at identifying natural antimicrobial peptides in order to circumvent resistance to commercial antibiotics. This study describes the development of synthetic peptides with antimicrobial activity, created in silico by site-directed mutation modeling using wild-type peptides as scaffolds for these mutations. Fragments of antimicrobial peptides were used for modeling with molecular modeling computational tools. To analyze these peptides, a decision tree model, which indicated the action range of peptides on the types of microorganisms on which they can exercise biological activity, was created. The decision tree model was processed using physicochemistry properties from known antimicrobial peptides available at the Antimicrobial Peptide Database (APD). The two most promising peptides were synthesized, and antimicrobial assays showed inhibitory activity against Gram-positive and Gram-negative bacteria. Colossomin C and colossomin D were the most inhibitory peptides at 5 µg/ml against Staphylococcus aureus and Escherichia coli. The methods described in this work and the results obtained are useful for the identification and development of new compounds with antimicrobial activity through the use of computational tools.

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.