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The aim of this study was to determine which, and how many, data items are required to construct a decision support algorithm for early diagnosis of acute myocardial infarction using clinical and electrocardiographic data available at presentation. Logistic regression models were derived using data items from 600 consecutive patients at one centre(More)
Early and accurate diagnosis of myocardial infarction (MI) in patients who present to the Emergency Room (ER) complaining of chest pain is an important problem in emergency medicine. A number of decision aids have been developed to assist with this problem but have not achieved general use. Machine learning techniques, including classification tree and(More)
BACKGROUND Selective serotonin reuptake inhibitors (SSRIs) are widely used antidepressants and one of the most commonly used medications. There is growing concern that SSRIs, which sequester in bone marrow at higher concentrations than brain or blood, increase bone fragility and fracture risk. However, their mechanism of action on human osteoclasts (OC) and(More)
Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the(More)
The accurate determination of gastric emptying time requires correction or compensation for tissue attenuation. The gold standard for tissue attenuation correction for gastric emptying is the geometric mean of the gastric counts from the anterior and posterior views. For reasons of efficiency, many community hospitals acquire only the anterior projection.(More)
BACKGROUND To date, our ability to accurately identify patients at high risk from suicidal behaviour, and thus to target interventions, has been fairly limited. This study examined a large pool of factors that are potentially associated with suicide risk from the comprehensive electronic medical record (EMR) and to derive a predictive model for 1-6 month(More)
OBJECTIVES Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes. SETTING A regional cancer centre in Australia.(More)
This study examined the potential use of low-cost consumer-grade smartphone technology to perform and improve field data collection in support of small-scale forest management. This proof-of-concept exercise for day-to-day forester operations focused on the effectiveness of the smartphone platform (form factor and functionality) rather than any particular(More)