Dual autoencoders modeling of electronic health records for adverse drug event preventability prediction

Background Elderly patients are at increased risk for Adverse Drug Events (ADEs). Proactively screening elderly people visiting the emergency department for the possibility of their hospital admission being drug-related helps to improve patient care as well as prevent potential unnecessary medical costs. Existing routine ADE assessment heavily relies on a rule-based checking process. Recently, machine learning methods have been shown to be effective in automating the detection of ADEs, however, most approaches used only either structured data or free texts for their feature engineering. How to better exploit all available EHRs data for better predictive modeling remains an important question. On the other hand, automated reasoning for the preventability of ADEs is still a nascent line of research. Methods Clinical information of 714 elderly ED-visit patients with ADE preventability labels was provided as ground truth data by Jeroen Bosch Ziekenhuis hospital, the Netherlands. Methods were developed to address the challenges of applying feature engineering to heterogeneous EHRs data. A Dual Autoencoders (2AE) model was proposed to solve the problem of imbalance embedded in the existing training data. Results Experimental results showed that 2AE can capture the patterns of the minority class without incorporating an extra process for class balancing. 2AE yields adequate performance and outperforms other more mainstream approaches, resulting in an AUPRC score of 0.481. Conclusions We have demonstrated how machine learning can be employed to analyze both structured and unstructured data from electronic health records for the purpose of preventable ADE prediction. The developed algorithm 2AE can be used to effectively learn minority group phenotype from imbalanced data

Onderzoek naar beïnvloedende factoren op samenredzaamheid en de toepassing hiervan in de interventie Community Support:Een systematische review [masterthesis]

De interventie Community Support richt zich op het vergroten van de samenredzaamheid. Samenredzaamheid wordt beschreven als het kunnen meedoen in de samenleving door middel van hulp vanuit het sociale netwerk of andere vormen van informele zorg, wanneer iemand (tijdelijk) verminderd zelfredzaam is. Zo kan de klant met hulp van het sociale netwerk, met emotionele en sociale uitdagingen in het dagelijks leven omgaan. Community Support richt zich op het vergroten van samenredzaamheid door te werken aan tien subdoelen. Deze subdoelen kunnen worden samengevat in drie onderdelen: zelfredzaamheid, netwerkversterking en zelfzeggenschap. Deze onderdelen zijn nog niet onderbouwd vanuit de wetenschappelijke literatuur. In dit onderzoek is middels een systematische review onderzocht welke factoren vanuit de wetenschappelijke literatuur van invloed zijn op samenredzaamheid. Door middel van twee zoekopdrachten in de zoekmachines ERIC en Psychinfo werden 290 artikelen gevonden. Vijftien artikelen voldeden aan de selectie- en inclusiecriteria van het onderzoek. Uit deze vijftien artikelen zijn zeven factoren gevonden: onveiligheid, armoede, sociale identiteit, zelfredzaamheid, etniciteit, familie en actieve participatie aan de interventie. Vervolgens is aan de hand van de interventiebeschrijving van Heijs (2020) onderzocht of deze factoren voorkomen in de interventie Community Support. Hieruit bleek dat Community Support zich richt op drie van de zeven factoren, namelijk sociale identiteit, zelfredzaamheid en familie. Nader onderzoek is nodig om te onderzoeken of Community Support zich in de praktijk daadwerkelijk richt op deze factoren, of dat mogelijk meer of andere factoren naar voren komen

Learning Vision-Based Bipedal Locomotion for Challenging Terrain

Reinforcement learning (RL) for bipedal locomotion has recently demonstrated robust gaits over moderate terrains using only proprioceptive sensing. However, such blind controllers will fail in environments where robots must anticipate and adapt to local terrain, which requires visual perception. In this paper, we propose a fully-learned system that allows bipedal robots to react to local terrain while maintaining commanded travel speed and direction. Our approach first trains a controller in simulation using a heightmap expressed in the robot's local frame. Next, data is collected in simulation to train a heightmap predictor, whose input is the history of depth images and robot states. We demonstrate that with appropriate domain randomization, this approach allows for successful sim-to-real transfer with no explicit pose estimation and no fine-tuning using real-world data. To the best of our knowledge, this is the first example of sim-to-real learning for vision-based bipedal locomotion over challenging terrains

Inhibition of CYP2D6 with low dose (5 mg) paroxetine in patients with high 10-hydroxynortriptyline serum levels-A prospective pharmacokinetic study

The antidepressant nortriptyline is metabolized by cytochrome P450 2D6 (CYP2D6) to the less active and more cardiotoxic drug metabolite, 10-hydroxynortriptyline. High serum levels of this metabolite (>200 μg/L) may lead to withdrawal of nortriptyline therapy. Adding CYP2D6 inhibitors reduce the metabolic activity of CYP2D6 (phenoconversion) and so decrease the forming of hydroxynortriptyline. In this study, 5 mg paroxetine is administered to patients with high hydroxynortriptyline concentrations (>200 μg/L). The shift in number of patients to therapeutic nortriptyline (50-150 μg/L) and safe hydroxynortriptyline (<200 μg/L) concentrations, and the degree of phenoconversion, expressed as the change in ratio nortriptyline/hydroxynortriptyline concentrations before and after paroxetine addition, are prospectively observed and described. After paroxetine addition, 12 patients (80%) had therapeutic nortriptyline and safe hydroxynortriptyline concentrations. Hydroxynortriptyline concentrations decreased in all patients. The average nortriptyline/hydroxynortriptyline concentrations ratio increased from 0.32 to 0.59. This study shows that 5 mg paroxetine addition is able to lower high hydroxynortriptyline serum levels to safe ranges

Body weight gain in clozapine-treated patients:Is norclozapine the culprit?

The antipsychotic drug clozapine is associated with weight gain. The proposed mechanisms include blocking of serotonin (5-HT2a/2c ), dopamine (D2 ) and histamine (H1 ) receptors. Clozapine is metabolized by cytochrome P450 1A2 (CYP1A2) to norclozapine, a metabolite with more 5-HT2c -receptor and less H1 blocking capacity. We hypothesized that norclozapine serum levels correlate with body mass index (BMI), waist circumference and other parameters of the metabolic syndrome. We performed a retrospective cross-sectional study in 39 patients (female n = 8 (20.5%), smokers n = 18 (46.2%), average age 45.8 ± 9.9 years) of a clozapine outpatient clinic in the Netherlands between 1 January 2017 and 1 July 2020. Norclozapine concentrations correlated with waist circumference (r = 0.354, P = .03) and hemoglobin A1c (HbA1c) (r = 0.34, P = .03). In smokers (smoking induces CYP1A2), norclozapine concentrations correlated with waist circumference (r = 0.723, P = .001), HbA1c (r = 0.49, P = .04) and BMI (r = 0.63, P = .004). Elucidating the relationship between norclozapine and adverse effects of clozapine use offers perspectives for interventions and treatment options