A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Breast cancer patients' visual attention to information in hospital report cards: An eye-tracking study on differences between younger and older female patients

To (1) explore how women visually attend to a hospital report card (HRC), (2) explore whether visual attention of younger and older women (patients and non-patients) differs. Eye-tracking study with a short survey. Participants (N = 37) were provided with a hypothetical realistic HRC. Total dwell times and fixation counts were measured while participants viewed the information. Overall, no differences existed between younger and older women. Visual attention to the hospital of choice (vs not of choice) and to indicators perceived as most important (vs least important) did not differ. However, women with higher health literacy looked longer at the HRC than women with lower health literacy. Also, per fixation, older patients (vs younger patients) looked longer at the hospital of choice and at indicators perceived most important. Pre-existing conceptions of what information is relevant might result in more in-depth information processing among older patients than younger patients. In general, differences in level of health literacy, rather than (chronological) age, seem to be relevant to take into account when designing and/or updating HRCs

Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm

The Cardiotocography is the most broadly utilized technique in obstetrics practice to monitor fetal health condition. The foremost motive of monitoring is to detect the fetal hypoxia at early stage. This modality is also widely used to record fetal heart rate and uterine activity. The exact analysis of cardiotocograms is critical for further treatment. In this manner, fetal state evaluation utilizing machine learning technique using cardiotocogram data has achieved significant attention. In this paper, we implement a model based CTG data classification system utilizing a supervised Random Forest (RF) which can classify the CTG data based on its training data. As per the showed up results, the overall performance of the supervised machine learning based classification approach provided significant performance. In this study, Precision, Recall, F-Score and Rand Index has been employed as the metric to evaluate the performance. It was found that, the RF based classifier could identify normal, suspicious and pathologic condition, from the nature of CTG data with 94.8% accuracy