Publications by authors named "A Keijzer"
Objective: This study aimed to develop a machine-learning (ML) model to predict the risk for Periodontal Disease (PD) based on nonimage electronic dental records (EDRs).
Methods: By using EDRs collected in the BigMouth repository, dental patients from the US were included. Patients were labeled as cases or controls, based on PD diagnosis, treatment and pocketing.
Article Synopsis
- A retrospective cohort study in the Netherlands examined differences between male and female patients with ascending aortic aneurysm (AscAA) regarding their aneurysm growth and clinical outcomes.
- The study included 1,858 adult patients, finding that women were typically older at diagnosis and had different patterns of aneurysm involvement compared to men, with women more commonly affected in the tubular area and men in the sinuses.
- Results showed that women experienced faster growth in the tubular ascending segment, leading to lower 10-year survival rates and a higher incidence of type A dissections compared to men.
Background: Electronic health records (EHRs) contain patients' health information over time, including possible early indicators of disease. However, the increasing amount of data hinders clinicians from using them. There is accumulating evidence suggesting that machine learning (ML) and deep learning (DL) can assist clinicians in analyzing these large-scale EHRs, as algorithms thrive on high volumes of data.
View Article and Find Full Text PDFBackground: Health Related Quality of Life (HRQoL) is an important factor regarding treatment for localized Muscle Invasive Bladder Carcinoma (MIBC), as it may affect choice of treatment. The impact of chemoradiotherapy (CRT) for MIBC on HRQoL has not yet been well-established.
Objective: To systematically evaluate evidence regarding HRQoL as assessed by validated questionnaires after definitive treatment with CRT for localized MIBC.
Background: Dynamic indices, including pulse pressure, systolic pressure, and stroke volume variation (PPV, SPV, and SVV), are accurate predictors of fluid responsiveness under strict conditions, for example, controlled mechanical ventilation using conventional tidal volumes (TVs) in the absence of cardiac arrhythmias. However, in routine clinical practice, these prerequisites are not always met. We evaluated the effect of regularly used ventilator settings, different calculation methods, and the presence of cardiac arrhythmias on the ability of dynamic indices to predict fluid responsiveness in sedated, mechanically ventilated patients.
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