Predicting patients' sentiments about medications using artificial intelligence techniques.

The increasing development of technology has led to the increase of digital data in various fields, such as medication-related texts. Sentiment Analysis (SA) in medication is essential to give clinicians insights into patients' feedback about the treatment procedure. Therefore, this study intends to develop Artificial Intelligence (AI) models to predict patients' sentiments.

Pulmonologists-level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach.

Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases.

Predicting stroke severity of patients using interpretable machine learning algorithms.

Background: Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke severity is crucial for predicting clinical outcomes, with standard assessment tools being the Rapid Arterial Occlusion Evaluation (RACE) and the National Institutes of Health Stroke Scale (NIHSS). This study aims to utilize Machine Learning (ML) algorithms to predict stroke severity using these two distinct scales.

Impact of motivational feedback on levels of physical activity and quality of life by activity monitoring following knee arthroplasty surgery-protocol for a randomized controlled trial nested in a prospective cohort (Knee-Activity).

Background: Evidence on how to improve daily physical activity (PA) levels following total knee arthroplasty (TKA) or medial uni-compartmental knee arthroplasty (mUKA) by motivational feedback is lacking. Moreover, it is unknown whether a focus on increased PA after discharge from the hospital improves rehabilitation, physical function, and quality of life. The aim of this randomized controlled trial (RCT) nested in a prospective cohort is (a) to investigate whether PA, physical function, and quality of life following knee replacement can be increased using an activity monitoring device including motivational feedback via a patient app in comparison with activity monitoring without feedback (care-as-usual), and (b) to investigate the potential predictive value of PA level prior to knee replacement for the length of stay, return to work, and quality of life.

Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.

Background: Smoking is a critical risk factor responsible for over eight million annual deaths worldwide. It is essential to obtain information on smoking habits to advance research and implement preventive measures such as screening of high-risk individuals. In most countries, including Denmark, smoking habits are not systematically recorded and at best documented within unstructured free-text segments of electronic health records (EHRs).

Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks.

The accurate estimation of energy expenditure from simple objective accelerometry measurements provides a valuable method for investigating the effect of physical activity (PA) interventions or population surveillance. Methods have been evaluated previously, but none utilize the temporal aspects of the accelerometry data. In this study, we investigated the energy expenditure prediction from acceleration measured at the subjects' hip, wrist, thigh, and back using recurrent neural networks utilizing temporal elements of the data.

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques.

In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coefficient test of skewness and the Ordinary Least Squares method, respectively. Using two sampling strategies, holdout and five-fold cross-validation, we developed five machine learning (ML) models alongside a feed-forward deep neural network (DNN) for the multiclass classification and regression prediction of glioblastoma patient survival.

A collection of multiregistry data on patients at high risk of lung cancer-a Danish retrospective cohort study of nearly 40,000 patients.

Background: Lung cancer (LC) is the leading cause of cancer related deaths, and several countries are implementing screening programs. Risk models have been introduced to refine the LC screening criteria, but the use of real-world data for this task demands a robust data infrastructure and quality. In this retrospective cohort study, we aim to address the different relevant risk factors in terms of data sources, descriptive statistics, completeness and quality.

Efficacy of a web-based healthcare innovation to advance the quality of life and care of patients with an implantable cardioverter defibrillator (ACQUIRE-ICD): a randomized controlled trial.

Aims: Modern clinical management of patients with an implantable cardioverter defibrillator (ICD) largely consists of remote device monitoring, although a subset is at risk of mental health issues post-implantation. We compared a 12-month web-based intervention consisting of goal setting, monitoring of patients' mental health-with a psychological intervention if needed-psychoeducational support from a nurse, and an online patient forum, with usual care on participants' device acceptance 12 months after implantation.

Methods And Results: This national, multi-site, two-arm, non-blinded, randomized, controlled, superiority trial enrolled 478 first-time ICD recipients from all 6 implantation centres in Denmark.

AUD-DSS: a decision support system for early detection of patients with alcohol use disorder.

Background: Alcohol use disorder (AUD) causes significant morbidity, mortality, and injuries. According to reports, approximately 5% of all registered deaths in Denmark could be due to AUD. The problem is compounded by the late identification of patients with AUD, a situation that can cause enormous problems, from psychological to physical to economic problems.

Monkeypox detection using deep neural networks.

Background: In May 2022, the World Health Organization (WHO) European Region announced an atypical Monkeypox epidemic in response to reports of numerous cases in some member countries unrelated to those where the illness is endemic. This issue has raised concerns about the widespread nature of this disease around the world. The experience with Coronavirus Disease 2019 (COVID-19) has increased awareness about pandemics among researchers and health authorities.

Structured decision support to prevent hospitalisations of community-dwelling older adults in Denmark (PATINA): an open-label, stepped-wedge, cluster-randomised controlled trial.

Background: Ageing populations and health-care staff shortages encourage efforts in primary care to recognise and prevent health deterioration and acute hospitalisation in community-dwelling older adults. The PATINA algorithm and decision-support tool alerts home-based-care nurses to older adults at risk of hospitalisation. The study aim was to test whether use of the PATINA tool was associated with changes in health-care use.

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders.

Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life.

Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods.

Background: High dimensionality in electronic health records (EHR) causes a significant computational problem for any systematic search for predictive, diagnostic, or prognostic patterns. Feature selection (FS) methods have been indicated to be effective in feature reduction as well as in identifying risk factors related to prediction of clinical disorders. This paper examines the prediction of patients with alcohol use disorder (AUD) using machine learning (ML) and attempts to identify risk factors related to the diagnosis of AUD.

Using Facial Landmark Detection on Thermal Images as a Novel Prognostic Tool for Emergency Departments.

Introduction: Emergency departments (ED) at hospitals sometimes experience unexpected deterioration in patients that were assessed to be in a stable condition upon arrival. Odense University Hospital (OUH) has conducted a retrospective study to investigate the possibilities of prognostic tools that can detect these unexpected deterioration cases at an earlier stage. The study suggests that the temperature difference (gradient) between the core and the peripheral body parts can be used to detect these cases.

Short-term atrial fibrillation detection using electrocardiograms: A comparison of machine learning approaches.

Background: Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias, which challenges the healthcare systems globally.Timely detection of AF can potentially reduce the mortality and morbidity rates as well as alleviate the economic burden caused by this.Digital solutions are shown to enhance the diagnosis of cardiac abnormalities.

Quantifying the impact of addressing data challenges in prediction of length of stay.

Background: Prediction of length of stay (LOS) at admission time can provide physicians and nurses insight into the illness severity of patients and aid them in avoiding adverse events and clinical deterioration. It also assists hospitals with more effectively managing their resources and manpower.

Methods: In this field of research, there are some important challenges, such as missing values and LOS data skewness.

Machine learning techniques for mortality prediction in emergency departments: a systematic review.

Objectives: This systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs).

Design: A systematic review was performed.

Setting: The databases including Medline (PubMed), Scopus and Embase (Ovid) were searched between 2010 and 2021, to extract published articles in English, describing ML-based models utilising vital sign variables to predict in-hospital mortality for patients admitted at EDs.

Attitudes of Patients and Health Professionals Regarding Screening Algorithms: Qualitative Study.

Background: As a preamble to an attempt to develop a tool that can aid health professionals at hospitals in identifying whether the patient may have an alcohol abuse problem, this study investigates opinions and attitudes among both health professionals and patients about using patient data from electronic health records (EHRs) in an algorithm screening for alcohol problems.

Objective: The aim of this study was to investigate the attitudes and opinions of patients and health professionals at hospitals regarding the use of previously collected data in developing and implementing an algorithmic helping tool in EHR for screening inexpedient alcohol habits; in addition, the study aims to analyze how patients would feel about asking and being asked about alcohol by staff, based on a notification in the EHR from such a tool.

Methods: Using semistructured interviews, we interviewed 9 health professionals and 5 patients to explore their opinions and attitudes about an algorithm-based helping tool and about asking and being asked about alcohol usage when being given a reminder from this type of tool.

Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study.

The volume of daily patient arrivals at Emergency Departments (EDs) is unpredictable and is a significant reason of ED crowding in hospitals worldwide. Timely forecast of patients arriving at ED can help the hospital management in early planning and avoiding of overcrowding. Many different ED patient arrivals forecasting models have previously been proposed by using time series analysis methods.

Cross-Cultural Bayesian Network Analysis of Factors Affecting Residents' Concerns About the Spread of an Infectious Disease Caused by Tourism.

COVID-19 has had a severe impact globally, and the recovery can be characterized as a tug of war between fast economic recovery and firm control of further virus-spread. To be prepared for future pandemics, public health policy makers should put effort into fully understanding any complex psychological tensions that inherently arise between opposing human factors such as free enjoyment versus self-restriction. As the COVID-19 crisis is an unusual and complex problem, combinations of diverse factors such as health risk perception, knowledge, norms and beliefs, attitudes and behaviors are closely associated with individuals' intention to enjoy the experience economy but also their concerns that the experience economy will trigger further spread of the infectious diseases.

Deep Neural Network to Identify Patients with Alcohol Use Disorder.

This paper presents an application of deep neural networks (DNN) to identify patients with Alcohol Use Disorder based on historical electronic health records. Our methodology consists of four stages including data collection, preprocessing, predictive model development, and validation. Data are collected from two sources and labeled into three classes including Normal, Hazardous, and Harmful drinkers.