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.

Long-term risk of recurrent cerebrovascular events after patent foramen ovale closure: Results from a real-world stroke cohort.

Introduction: Patent foramen ovale (PFO)-closure is recommended for stroke prevention in selected patients with suspected PFO-associated stroke. However, studies on cerebrovascular event recurrence after PFO-closure are limited by relatively short follow-up periods and information on the underlying aetiology of recurrent events is scarce.

Patients And Methods: All consecutive patients with a cerebral ischaemic event and PFO-closure at the University Hospital Graz were prospectively identified from 2004 to 2021.

Introduction and Comparison of Novel Decentral Learning Schemes with Multiple Data Pools for Privacy-Preserving ECG Classification.

Artificial intelligence and machine learning have led to prominent and spectacular innovations in various scenarios. Application in medicine, however, can be challenging due to privacy concerns and strict legal regulations. Methods that centralize knowledge instead of data could address this issue.

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression.

In situations like the COVID-19 pandemic, healthcare systems are under enormous pressure as they can rapidly collapse under the burden of the crisis. Machine learning (ML) based risk models could lift the burden by identifying patients with a high risk of severe disease progression. Electronic Health Records (EHRs) provide crucial sources of information to develop these models because they rely on routinely collected healthcare data.

Frail People in LABLand: Development of an Easy-to-Use Machine Learning Model to Identify Frail People in Hospitals Based on Laboratory Data.

Background: Frail individuals are very vulnerable to stressors, which often lead to adverse outcomes. To ensure an adequate therapy, a holistic diagnostic approach is needed which is provided in geriatric wards. It is important to identify frail individuals outside the geriatric ward as well to ensure that they also benefit from the holistic approach.

The interplay of inflammation, exosomes and Ca dynamics in diabetic cardiomyopathy.

Diabetes mellitus is one of the prime risk factors for cardiovascular complications and is linked with high morbidity and mortality. Diabetic cardiomyopathy (DCM) often manifests as reduced cardiac contractility, myocardial fibrosis, diastolic dysfunction, and chronic heart failure. Inflammation, changes in calcium (Ca) handling and cardiomyocyte loss are often implicated in the development and progression of DCM.

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.

Real-world clinical outcomes with avelumab in patients with Merkel cell carcinoma treated in the USA: a multicenter chart review study.

Background: Merkel cell carcinoma (MCC) is a rare, aggressive, cutaneous neuroendocrine neoplasm with annual incidence rates of 0.13-1.6 cases/100,000/year worldwide as of 2018.

Towards Automated Screening of Literature on Artificial Intelligence in Nursing.

We evaluate the performance of multiple text classification methods used to automate the screening of article abstracts in terms of their relevance to a topic of interest. The aim is to develop a system that can be first trained on a set of manually screened article abstracts before using it to identify additional articles on the same topic. Here the focus is on articles related to the topic "artificial intelligence in nursing".

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.

A Real-World Comparative Effectiveness Analysis of Thromboprophylactic Use of Enoxaparin Versus Unfractionated Heparin in Abdominal Surgery Patients in a Large U.S. Hospital Database.

Little is known about outcomes associated with enoxaparin versus unfractionated heparin (UFH) for venous thromboembolism (VTE) prophylaxis in abdominal surgery patients in U.S. clinical practice.

Are caregivers ready for digital? Caregiver preferences for health technology tools to monitor medication adherence among patients with serious mental illness.

Background: Adherence to antipsychotic medication is critical for bipolar disorder (BPD), major depression (MDD) and schizophrenia (SCZ) patients. Digital tools have emerged to monitor medication adherence along with tracking general health. Evidence on physician or patient preferences for such tools exists but is limited among caregivers.

A Delphi Consensus Approach for Difficult-to-Treat Patients with Severe Hemophilia A without Inhibitors.

Introduction: Over the past decade, there has been an increase in novel therapeutic options to treat hemophilia A. It is still unclear how these novel treatments are used in the management of patients with hemophilia A, particularly those with challenging clinical scenarios who are typically excluded in clinical trials.

Purpose: This study aimed to understand the areas of consensus and disagreement among hematologists regarding the preferences toward therapeutic approaches for difficult-to-treat patients with severe hemophilia A without inhibitors.

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.

How to Improve Information Technology to Support Healthcare to Address the COVID-19 Pandemic: an International Survey with Health Informatics Experts.

Objectives: To identify the ways in which healthcare information and communication technologies can be improved to address the challenges raised by the COVID-19 pandemic.

Methods: The study population included health informatics experts who had been involved with the planning, development and deployment of healthcare information and communication technologies in healthcare settings in response to the challenges presented by the COVID-19 pandemic. Data were collected via an online survey.

Real-World Comparative Effectiveness and Cost Comparison of Thromboprophylactic Use of Enoxaparin versus Unfractionated Heparin in 376,858 Medically Ill Hospitalized US Patients.

Background: Venous thromboembolism (VTE) is a serious complication in medically ill inpatients. Enoxaparin or unfractionated heparin (UFH) thromboprophylaxis has been shown to reduce VTE in clinical trials; however, comparative effectiveness and differences in hospital costs are unknown in US hospital practice.

Objective: This study compared clinical and economic outcomes between enoxaparin and UFH thromboprophylaxis in medically ill inpatients.

The role of epidemiology in informing United States childhood immunization policy and practice.

One of the ten greatest public health achievements is childhood vaccination because of its impact on controlling and eliminating vaccine-preventable diseases (VPDs). Evidence-based immunization policies and practices are responsible for this success and are supported by epidemiology that has generated scientific evidence for informing policy and practice. The purpose of this report is to highlight the role of epidemiology in the development of immunization policy and successful intervention in public health practice that has resulted in a measurable public health impact: the control and elimination of VPDs in the United States.

Recommended physical activity and all cause and cause specific mortality in US adults: prospective cohort study.

Objective: To determine the association between recommended physical activity according to the 2018 physical activity guidelines for Americans and all cause and cause specific mortality using a nationally representative sample of US adults.

Design: Population based cohort study.

Setting: National Health Interview Survey (1997-2014) with linkage to the National Death Index records to 31 December 2015.

Psychological distress and mortality among US adults: prospective cohort study of 330 367 individuals.

Background: Previous studies have shown inconsistent findings on the association between psychological distress and risk of mortality. This study aimed to address this inconsistent association using a large US population-based cohort.

Methods: This study used data from 1997 to 2009 US National Health Interview Survey, which were linked with National Death Index through 31 December 2011.

Improved workflow for mass spectrometry-based metabolomics analysis of the heart.

MS-based metabolomics methods are powerful techniques to map the complex and interconnected metabolic pathways of the heart; however, normalization of metabolite abundance to sample input in heart tissues remains a technical challenge. Herein, we describe an improved GC-MS-based metabolomics workflow that uses insoluble protein-derived glutamate for the normalization of metabolites within each sample and includes normalization to protein-derived amino acids to reduce biological variation and detect small metabolic changes. Moreover, glycogen is measured within the metabolomics workflow.

Chronic Respiratory Disease and Cognitive Impairment in Older Mexican Adults.

Background: Cognitive impairment has emerged as an important concern in clinical practice in aging population. Several comorbid factors contribute to etiopathogenesis; one disease of interest is chronic respiratory disease.

Aim: The aim of this study is to investigate the association of chronic respiratory disease with risk of cognitive impairment in older Mexicans.

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.