Journal of biomedical informatics
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Bone sawing or cutting is widely used for bone removal processes in bone surgery. It is an essential skill that surgeons should execute with a high level of experience and sensitive force perception. Surgical training simulators, with virtual and haptic feedback functions, can offer a safe, repeatable and cost-effective alternative to traditional surgeries. In this research, we developed a surgical training simulator with virtual and haptic force feedback for maxillofacial surgery, and we validated the effects on the learning of bone-sawing skills through empirical evaluation. ⋯ The evaluation results proved the construct validity, face validity and the transfer validity of the simulator. These results indicated that this simulator was able to produce the effect of learning bone-sawing skill, and it could provide a training alternative for novices.
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Rapid, automated determination of the mapping of free text phrases to pre-defined concepts could assist in the annotation of clinical notes and increase the speed of natural language processing systems. The aim of this study was to design and evaluate a token-order-specific naïve Bayes-based machine learning system (RapTAT) to predict associations between phrases and concepts. Performance was assessed using a reference standard generated from 2860 VA discharge summaries containing 567,520 phrases that had been mapped to 12,056 distinct Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) concepts by the MCVS natural language processing system. ⋯ RapTAT required just 7.2ms to map all phrases within a single discharge summary, and mapping rate did not decrease as the number of processed documents increased. The high performance attained by the tool in terms of both accuracy and speed was encouraging, and the mapping rate should be sufficient to support near-real-time, interactive annotation of medical narratives. These results demonstrate the feasibility of rapidly and accurately mapping phrases to a wide range of medical concepts based on a token-order-specific naïve Bayes model and machine learning.
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Multi Organ Dysfunction Syndrome (MODS) represents a continuum of physiologic derangements and is the major cause of death in the Intensive Care Unit (ICU). Scoring systems for organ failure have become an integral part of critical care practice and play an important role in ICU-based research by tracking disease progression and facilitating patient stratification based on evaluation of illness severity during ICU stay. In this study a Dynamic Bayesian Network (DBN) was applied to model SOFA severity score changes in 79 adult critically ill patients consecutively admitted to the general ICU of the Sant'Andrea University hospital (Rome, Italy) from September 2010 to March 2011, with the aim to identify the most probable sequences of organs failures in the first week after the ICU admission. ⋯ Sequences involving heart, lung, hematologic system and liver turned out to be the more likely to occur, with slightly different probabilities depending on the day of the week they occur. DBNs could be successfully applied for modeling temporal systems in critical care domain. Capability to predict sequences of likely organ failures makes DBNs a promising prognostic tool, intended to help physicians in undertaking therapeutic decisions in a patient-tailored approach.