Machine learning-based delirium prediction in surgical in-patients: a prospective validation study.

Objective: Delirium is a syndrome that leads to severe complications in hospitalized patients, but is considered preventable in many cases. One of the biggest challenges is to identify patients at risk in a hectic clinical routine, as most screening tools cause additional workload. The aim of this study was to validate a machine learning (ML)-based delirium prediction tool on surgical in-patients undergoing a systematic assessment of delirium.

Machine Learning-Based Prediction of Malnutrition in Surgical In-Patients: A Validation Pilot Study.

Background: Malnutrition in hospitalised patients can lead to serious complications, worse patient outcomes and longer hospital stays. State-of-the-art screening methods rely on scores, which need additional manual assessments causing higher workload.

Objectives: The aim of this prospective study was to validate a machine learning (ML)-based approach for an automated prediction of malnutrition in hospitalised patients.

Evaluation of a Machine Learning-Based Dysphagia Prediction Tool in Clinical Routine: A Prospective Observational Cohort Study.

Based on a large number of pre-existing documented electronic health records (EHR), we developed a machine learning (ML) algorithm for detection of dysphagia and aspiration pneumonia. The aim of our study was to prospectively apply this algorithm in two large patient cohorts. The tool was integrated in the hospital information system of a secondary care hospital in Austria.

Development of an Architecture to Implement Machine Learning Based Risk Prediction in Clinical Routine: A Service-Oriented Approach.

Background: Patients at risk of developing a disease have to be identified at an early stage to enable prevention. One way of early detection is the use of machine learning based prediction models trained on electronic health records.

Objectives: The aim of this project was to develop a software solution to predict cardiovascular and nephrological events using machine learning models.

External Validation of a Machine Learning Based Delirium Prediction Software in Clinical Routine.

Background: Various machine learning (ML) models have been developed for the prediction of clinical outcomes, but there is missing evidence on their performance in clinical routine and external validation.

Objectives: Our aim was to deploy and prospectively evaluate an already developed delirium prediction software in clinical routine of an external hospital.

Methods: We compared updated ML models of the software and models re-trained with the external hospital's data.

Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events.

Background: Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented with the right interventions.

Objectives: The aim of this study was to develop machine learning-based models for the 5-year risk prediction of MACE.

Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods Study.

Early identification of patients with life-threatening risks such as delirium is crucial in order to initiate preventive actions as quickly as possible. Despite intense research on machine learning for the prediction of clinical outcomes, the acceptance of the integration of such complex models in clinical routine remains unclear. The aim of this study was to evaluate user acceptance of an already implemented machine learning-based application predicting the risk of delirium for in-patients.

Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study.

Objective: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting.

Materials And Methods: Delirium was predicted at admission and recalculated on the evening of admission.

An Improvised Classification Model for Predicting Delirium.

With the vast increase of digital healthcare data, there is an opportunity to mine the data for understanding inherent health patterns. Although machine-learning techniques demonstrated their applications in healthcare to answer several questions, there is still room for improvement in every aspect. In this paper, we are demonstrating a method that improves the performance of a delirium prediction model using random forest in combination with logistic regression.

Development of a Machine Learning Model Predicting an ICU Admission for Patients with Elective Surgery and Its Prospective Validation in Clinical Practice.

Frequent utilization of the Intensive Care Unit (ICU) is associated with higher costs and decreased availability for patients who urgently need it. Common risk assessment tool, like the ASA score, lack objectivity and do account only for some influencing parameters. The aim of our study was (1) to develop a reliable machine learning model predicting ICU admission risk after elective surgery, and (2) to implement it in a clinical workflow.

Is Regular Re-Training of a Predictive Delirium Model Necessary After Deployment in Routine Care?

Adoption of electronic medical records in hospitals generates a large amount of data. Health care professionals can easily lose their sight on the important insights of the patients' clinical and medical history. Although machine learning algorithms have already proved their significance in healthcare research, remains a challenge translation and dissemination of fully automated prediction algorithms from research to decision support at the point of care.

Information Adapted Machine Learning Models for Prediction in Clinical Workflow.

Background: In a database of electronic health records, the amount of available information varies widely between patients. In a real-time prediction scenario, a machine learning model may receive limited information for some patients.

Objectives: Our aim was to evaluate the influence of missing data on real-time prediction of delirium, and detect changes in prediction performance when training separate models for patients with missing data.

Risk assessment for preterm preeclampsia in first trimester: Comparison of three calculation algorithms.

Objective: To better adjust the risk for preeclampsia, multifactorial models in first trimester of pregnancy have found the way in clinical practice. This study compares the available test algorithms.

Study Design: In a cross-sectional study between November 2013 and April 2016 we compared the tests results of three first trimester testing algorithms for preeclampsia in 413 women.

Evaluating the Impact of Incorrect Diabetes Coding on the Performance of Multivariable Prediction Models.

The use of electronic health records for risk prediction models requires a sufficient quality of input data to ensure patient safety. The aim of our study was to evaluate the influence of incorrect administrative diabetes coding on the performance of a risk prediction model for delirium, as diabetes is known to be one of the most relevant variables for delirium prediction. We used four data sets varying in their correctness and completeness of diabetes coding as input for different machine learning algorithms.

On the Representation of Machine Learning Results for Delirium Prediction in a Hospital Information System in Routine Care.

Digitalisation of health care for the purpose of medical documentation lead to huge amounts of data, hence having an opportunity to derive knowledge and associations of different attributes recorded. Many health care events can be prevented when identified. Machine learning algorithms could identify such events but there is ambiguity in understanding the suggestions especially in clinical setup.

Effect of Nursing Assessment on Predictive Delirium Models in Hospitalised Patients.

Delirium is an acute neuropsychiatric syndrome which is common in elderly patients during their hospitalisation and is associated with an increased mortality and morbidity. Since delirium is a) often underdiagnosed and b) preventable if early signs are detected,igh expectations are set in delirium risk assessment during hospital admission. In our latest studies, we showed that delirium prediction using machine learning algorithms is possible based on the patients' health history.

Cleansing and Imputation of Body Mass Index Data and Its Impact on a Machine Learning Based Prediction Model.

Background: A challenge of using electronic health records for secondary analyses is data quality. Body mass index (BMI) is an important predictor for various diseases but often not documented properly.

Objectives: The aim of our study is to perform data cleansing on BMI values and to find the best method for an imputation of missing values in order to increase data quality.

Small airway function before and after cold dry air challenge in pediatric asthma patients during remission.

Background: We wanted to compare cold dry air challenge (CACh) induced changes in spirometric parameters with changes in nitrogen multiple breath washout (N MBW) parameters in pediatric asthma patients during clinical remission over the past year (ie, with "inactive asthma"). As N MBW assesses ventilation heterogeneity we expected to gain detailed information about peripheral airways contribution.

Methods: In subjects with normal spirometry N MBW, spirometry and body plethysmography were performed at baseline, after CACh, and after salbutamol inhalation.

A retrospective study of isolated fractures of the alveolar process in the permanent dentition.

Background/aim: There is a lack of studies of fractures of the alveolar process (FAP). Only five were published in the last 50 years. The aim of this study was to analyze the risk of pulp necrosis and infection (PN), pulp canal obliteration (PCO), infection-related root resorption (IRR), ankylosis-related resorption (ARR), marginal bone loss (MBL), and tooth loss (TL) as well as to identify the possible risk factors for teeth involved in an isolated alveolar process fracture.