24,135 research outputs found
Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation
Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries.
Rehabilitation after such a musculoskeletal injury remains a prolonged process
with a very variable outcome. Accurately predicting rehabilitation outcome is
crucial for treatment decision support. However, it is challenging to train an
automatic method for predicting the ATR rehabilitation outcome from treatment
data, due to a massive amount of missing entries in the data recorded from ATR
patients, as well as complex nonlinear relations between measurements and
outcomes. In this work, we design an end-to-end probabilistic framework to
impute missing data entries and predict rehabilitation outcomes simultaneously.
We evaluate our model on a real-life ATR clinical cohort, comparing with
various baselines. The proposed method demonstrates its clear superiority over
traditional methods which typically perform imputation and prediction in two
separate stages
Fuzzy rule-based system applied to risk estimation of cardiovascular patients
Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc
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