BMC Medical Informatics and Decision Making

http://bmcmedinformdecismak.biomedcentral.com

List of Papers (Total 4,552)

Extracting laboratory test information from paper-based reports

In the healthcare domain today, despite the substantial adoption of electronic health information systems, a significant proportion of medical reports still exist in paper-based formats. As a result, there is a significant demand for the digitization of information from these paper-based reports. However, the digitization of paper-based laboratory reports into a structured data...

Automatic literature screening using the PAJO deep-learning model for clinical practice guidelines

Clinical practice guidelines (CPGs) are designed to assist doctors in clinical decision making. High-quality research articles are important for the development of good CPGs. Commonly used manual screening processes are time-consuming and labor-intensive. Artificial intelligence (AI)-based techniques have been widely used to analyze unstructured data, including texts and images...

Smartwatches in healthcare medicine: assistance and monitoring; a scoping review

Smartwatches have become increasingly popular in recent times because of their capacity to track different health indicators, including heart rate, patterns of sleep, and physical movements. This scoping review aims to explore the utilisation of smartwatches within the healthcare sector. According to Arksey and O'Malley's methodology, an organised search was performed in PubMed...

In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study

Falls are one of the most common accidents in medical institutions, which can threaten the safety of inpatients and negatively affect their prognosis. Herein, we developed a machine learning (ML) model for fall prediction in patients with acute stroke and compared its accuracy with that of the existing fall risk prediction tool, the Morse Fall Scale (MFS). This is a retrospective...

The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients

Diabetic kidney disease (DKD) has become the largest cause of end-stage kidney disease. Early and accurate detection of DKD is beneficial for patients. The present detection depends on the measurement of albuminuria or the estimated glomerular filtration rate, which is invasive and not optimal; therefore, new detection tools are urgently needed. Meanwhile, a close relationship...

KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning

Predicting medications is a crucial task in intelligent healthcare systems, aiding doctors in making informed decisions based on electronic medical records (EMR). However, medication prediction faces challenges due to complex relations within heterogeneous medical data. Existing studies primarily focus on the supervised mining of hierarchical relations between homogeneous codes...

Predictive value of machine learning algorithm of coronary artery calcium score and clinical factors for obstructive coronary artery disease in hypertensive patients

The addition of coronary artery calcium score (CACS) to prediction models has been verified to improve performance. Machine learning (ML) algorithms become important medical tools in an era of precision medicine, However, combined utility by CACS and ML algorithms in hypertensive patients to forecast obstructive coronary artery disease (CAD) on coronary computed tomography...

A political economy analysis of strengthening health information system in Tanzania

Many countries’ health systems are implementing reforms to improve the functioning and performance of the Health Management Information System (HMIS) to facilitate evidence-based decisions for delivery of accessible and quality health services. However, in some countries such efforts and initiatives have led to a complex HMIS ecosystem characterized by multiple and fragmented sub...

Expectation of clinical decision support systems: a survey study among nephrologist end-users

Chronic kidney disease (CKD), a major public health problem with differing disease etiologies, leads to complications, comorbidities, polypharmacy, and mortality. Monitoring disease progression and personalized treatment efforts are crucial for long-term patient outcomes. Physicians need to integrate different data levels, e.g., clinical parameters, biomarkers, and drug...

Validation of the Swedish Quality Register for Ear Surgery – SwedEar

The Swedish Quality Register for Ear Surgery (SwedEar) is a national register monitoring surgical procedures and outcomes of ear surgery to facilitate quality improvement. The value of the register is dependent on the quality of its data. SwedEar has never been validated regarding data quality or missing entries. Therefor, the purpose of this study was to assess coverage...

Quest markup for developing FAIR questionnaire modules for epidemiologic studies

Online questionnaires are commonly used to collect information from participants in epidemiological studies. This requires building questionnaires using machine-readable formats that can be delivered to study participants using web-based technologies such as progressive web applications. However, the paucity of open-source markup standards with support for complex logic make...

Identifying factors that affect the use of health information technology in the treatment and management of hypertension

We conducted this study with the aim of identifying factors that affect the use of health information technology in the treatment and management of hypertension. This paper is a descriptive-analytic study conducted in 2022. To obtain relevant articles, databases including Scopus, Web of Science, IEEE, and PubMed were searched and the time period was between 2013 and 2022. Based...

Health care needs, eHealth literacy, use of mobile phone functionalities, and intention to use it for self-management purposes by informal caregivers of children with burns: a survey study

This study aimed to assess health care needs, electronic health literacy, mobile phone usage, and intention to use it for self-management purposes by informal caregivers of children with burn injuries. This cross-sectional research was carried out in 2021 with 112 informal caregivers of children with burns in a burn center in the north of Iran. The data collection tools were...

Individualized treatment decision model for inoperable elderly esophageal squamous cell carcinoma based on multi-modal data fusion

This research aimed to develop a model for individualized treatment decision-making in inoperable elderly patients with esophageal squamous cell carcinoma (ESCC) using machine learning methods and multi-modal data. A total of 189 inoperable elderly ESCC patients aged 65 or older who underwent concurrent chemoradiotherapy (CCRT) or radiotherapy (RT) were included. Multi-task...

Impact of clinical decision support on controlled substance prescribing

Prescription drug overdose and misuse has reached alarming numbers. A persistent problem in clinical care is lack of easy, immediate access to all relevant information at the actionable time. Prescribers must digest an overwhelming amount of information from each patient’s record as well as remain up-to-date with current evidence to provide optimal care. This study aimed to...

Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms

The global society is currently facing a rise in the elderly population. The concept of successful aging (SA) appeared in the gerontological literature to overcome the challenges and problems of population aging. SA is a subjective and multidimensional concept with many ambiguities regarding its meaning or measuring. This study aimed to propose an intelligent predictive model to...

Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults

Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires. This was a retrospective study comprising...

Escape to the future – a qualitative study of physicians’ views on the work environment, education, and support in a digital context

The use of remote services such as video consultations (VCs) has increased significantly in the wake of the COVID-19 pandemic. In Sweden, private healthcare providers offering VCs have grown substantially since 2016 and have been controversial. Few studies have focused on physicians’ experiences providing care in this context. Our aim was to study physicians’ experiences of VCs...

Cardiac arrhythmia detection using deep learning approach and time frequency representation of ECG signals

Cardiac arrhythmia is a cardiovascular disorder characterized by disturbances in the heartbeat caused by electrical conduction anomalies in cardiac muscle. Clinically, ECG machines are utilized to diagnose and monitor cardiac arrhythmia noninvasively. Since ECG signals are dynamic in nature and depict various complex information, visual assessment and analysis are time consuming...

Interpreting deep learning models for glioma survival classification using visualization and textual explanations

Saliency-based algorithms are able to explain the relationship between input image pixels and deep-learning model predictions. However, it may be difficult to assess the clinical value of the most important image features and the model predictions derived from the raw saliency map. This study proposes to enhance the interpretability of saliency-based deep learning model for...

Improving computerized decision support system interventions: a qualitative study combining the theoretical domains framework with the GUIDES Checklist

Computerized clinical decision support systems (CDSSs) can improve care by bridging knowledge to practice gaps. However, the real-world uptake of such systems in health care settings has been suboptimal. We sought to: (1) use the Theoretical Domains Framework (TDF) to identify determinants (barriers/enablers) of uptake of the Electronic Asthma Management System (eAMS) CDSS; (2...

Development, implementation, and evaluation of neonatal thermoregulation decision support web application

Thermoregulation is important for all age groups, and in neonates, it is considered a crucial event to adapt to extrauterine life. Therefore, using systems that provide frequent reminders in different ways in the field of thermoregulation can help thermal stability in neonates. The present study aimed to develop, implement, and evaluate a neonatal thermoregulation decision...

Resource use and cost associated with computerized decision support system and usual care in managing patients with atrial fibrillation: analysis of IMPACT-AF randomized trial data

IMPACT-AF is a prospective, randomized, cluster design trial comparing atrial fibrillation (AF) management with a computerized decision support system (CDS) to usual care (control) in the primary care setting of Nova Scotia, Canada. The objective of this analysis was to compare the resource use and costs between CDS and usual care groups. Case costing data, 12-month self...

Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness

For surveillance of episodic illness, the emergency department (ED) represents one of the largest interfaces for generalizable data about segments of the US public experiencing a need for unscheduled care. This protocol manuscript describes the development and operation of a national network linking symptom, clinical, laboratory and disposition data that provides a public...