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 risk for delirium, and evaluated its performance setting.Delirium was predicted admission recalculated the evening of admission. The defined prediction outcome delirium coded recent hospital stay. During 7 months prospective...

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

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

Abstract Purpose The rise of digitization promotes the development screening and decision support tools. We sought to validate results from a machine learning based dysphagia risk prediction tool with clinical evaluation. Methods 149 inpatients in ENT department were evaluated real time by tool, as well clinically over 3-week period. Patients classified both patients at risk/no risk. Results AUROC, reflecting discrimination capability algorithm, was 0.97. accuracy achieved 92.6% given an...

Abstract Artificial Intelligence (AI) methods, which are often based on Machine Learning (ML) algorithms, also applied in the healthcare domain to provide predictions physicians and patients electronic health records (EHRs), such as history of laboratory values, procedures diagnoses. The question about these “Why Should I Trust You?” encapsulates issue with ML black boxes. Therefore, explaining reasons for is crucial allow them decide whether prediction applicable or not. In this paper, we...

Abstract Based on a large number of pre-existing documented electronic health records (EHR), we developed machine learning (ML) algorithm for detection dysphagia and aspiration pneumonia. The aim our study was to prospectively apply this in two patient cohorts. tool integrated the hospital information system secondary care Austria. existing data such as diagnoses, laboratory, medication, risk predicted automatically, patients were stratified into three groups. Patients’ groups factors...

We wanted to compare cold dry air challenge (CACh) induced changes in spirometric parameters with nitrogen multiple breath washout (N2 MBW) pediatric asthma patients during clinical remission over the past year (ie, "inactive asthma"). As N2 MBW assesses ventilation heterogeneity we expected gain detailed information about peripheral airways contribution.In subjects normal spirometry MBW, and body plethysmography were performed at baseline, after CACh, salbutamol inhalation. An initial...

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 validate a machine learning (ML)-based approach for an automated prediction malnutrition patients. Methods: For 159 surgical in-patients, assessment by dieticians compared the ML-based conducted...

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 ASA score, lack objectivity do account only some influencing parameters. The aim our study was (1) to develop a reliable machine learning model predicting ICU admission after elective surgery, (2) implement it in clinical workflow. We used electronic medical records from more than 61,000 modelling. A random...

There is a lack of studies fractures the alveolar process (FAP). Only five were published in last 50 years. The aim this study was to analyze risk pulp necrosis and infection (PN), canal obliteration (PCO), infection-related root resorption (IRR), ankylosis-related (ARR), marginal bone loss (MBL), tooth (TL) as well identify possible factors for teeth involved an isolated fracture. In second part, any late complications reported patients who responded follow-up examination.This retrospective...

Various machine learning (ML) models have been developed for the prediction of clinical outcomes, but there is missing evidence on their performance in routine and external validation.Our aim was to deploy prospectively evaluate an already delirium software hospital.We compared updated ML re-trained with hospital's data. The best were deployed one month, risk predictions all admitted patients ratings a senior physician. After using software, clinicians completed questionnaire assessing...

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

Background: Dysphagia is a dysfunction of the swallowing act and highly prevalent in acute post-stroke patients with chronic neurological diseases. associated several potentially life threatening complications. Thus, an early identification treatment could reduce morbidity mortality rates.

Digitalisation of health care for the purpose medical documentation lead to huge amounts data, hence having an opportunity derive knowledge and associations different attributes recorded. Many events can be prevented when identified. Machine learning algorithms could identify such but there is ambiguity in understanding suggestions especially clinical setup. In this paper we are presenting how explain decision based on random forest professionals course project predicting delirium during...

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

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

Background Studies of cardiovascular disease risk prediction by machine learning algorithms often do not assess their ability to generalize other populations and few them include an analysis the interpretability individual predictions. This manuscript addresses development validation, both internal external, predictive models for assessment risks major adverse events (MACE). Global local analyses predictions were conducted towards improving MACE’s model reliability tailoring preventive...

The use of electronic health records for risk prediction models requires a sufficient quality input data to ensure patient safety. aim our study was evaluate the influence incorrect administrative diabetes coding on performance model delirium, as is known be one most relevant variables delirium prediction. We used four sets varying in their correctness and completeness different machine learning algorithms. Although there higher prevalence patients, parameters did not vary between sets....

Patients at risk of developing a disease have to be identified an early stage enable prevention. One way detection is the use machine learning based prediction models trained on electronic health records.The aim this project was develop software solution predict cardiovascular and nephrological events using models. In addition, verification interface for care professionals established.In order meet requirements, different tools were analysed. Based this, architecture created, which designed...