FOLLOW-UP OF TREATED OSTEOSARCOMA PATIENT

Objective: To evaluate the value of post-treatment follow-up in osteosarcoma patients. Methods: Data were collected through a clinical record, with socio-demographic and clinical data, and information relating to the medical appointment. Descriptive analysis of the data was carried out. The Chi-squared test was used to associate the independent variables with attendance at scheduled follow-up appointments. Results: We found a recurrence in 59.6% of cases, of which 58% were lung related; 44% presented clinical complaints and arrived on the scheduled date of the appointment. There was no statistically significant association between the demographic characteristics and early attendance of follow-up visits. 81.3% of the cases who came for the appointment earlier than originally scheduled presented complaints compared to those who did not (p=0.005). Of the cases who presented recurrence, 12.9% attended an appointment late and those who did not present recurrence, 47.6% were late for the appointment (p=0.006). Conclusion: It is seen that the patients who came for an earlier appointment presented more complaints and were associated with the positive result of the exams carried out. The patients who had recurrence and came for an earlier appointment did not present a statistically significant difference in recurrence-free survival. It was observed that distance was not a predominant factor in late attendance at appointments. Level of Evidence II, Retrospective Study.

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.