Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges.

Mobile health as one of the new technologies can be a proper solution to support care provision for the elderly and provide personalized care for them. This study is aimed at reviewing the benefits and challenges of personalized mobile health (PMH) for elderly home care. With a systematic review methodology, 1895 records were retrieved by searching four databases.

MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques.

Background: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and classify brain tumors.

Prediction models for SIRS, sepsis and associated organ dysfunctions in paediatric intensive care: study protocol for a diagnostic test accuracy study.

Introduction: Systemic inflammatory response syndrome (SIRS), sepsis and associated organ dysfunctions are life-threating conditions occurring at paediatric intensive care units (PICUs). Early recognition and treatment within the first hours of onset are critical. However, time pressure, lack of personnel resources, and the need for complex age-dependent diagnoses impede an accurate and timely diagnosis by PICU physicians.

Targeted Data Quality Analysis for a Clinical Decision Support System for SIRS Detection in Critically Ill Pediatric Patients.

Background: Data quality issues can cause false decisions of clinical decision support systems (CDSSs). Analyzing local data quality has the potential to prevent data quality-related failure of CDSS adoption.

Objectives: To define a shareable set of applicable measurement methods (MMs) for a targeted data quality assessment determining the suitability of local data for our CDSS.

Federated electronic data capture (fEDC): Architecture and prototype.

In clinical research as well as patient care, structured documentation of findings is an important task. In many cases, this is achieved by means of electronic case report forms (eCRF) using corresponding information technology systems. To avoid double data entry, eCRF systems can be integrated with electronic health records (EHR).

Prediction of COVID-19 Patients' Survival by Deep Learning Approaches.

Despite many studies done to predict severe coronavirus 2019 (COVID-19) patients, there is no applicable clinical prediction model to predict and distinguish severe patients early. Based on laboratory and demographic data, we have developed and validated a deep learning model to predict survival and assist in the triage of COVID-19 patients in the early stages. This retrospective study developed a survival prediction model based on the deep learning method using demographic and laboratory data.

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging.

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both "traditional" machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods.

Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets.

Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression.

DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks.

The increasing quantity of multi-omic data, such as methylomic and transcriptomic profiles collected on the same specimen or even on the same cell, provides a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations. We propose a network approach based on Gaussian Graphical Models (GGMs) that facilitates the joint analysis of paired omics data. This method, called DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network's complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics 'layers.

Hypothesis of a potential BrainBiota and its relation to CNS autoimmune inflammation.

Infectious agents have been long considered to play a role in the pathogenesis of neurological diseases as part of the interaction between genetic susceptibility and the environment. The role of bacteria in CNS autoimmunity has also been highlighted by changes in the diversity of gut microbiota in patients with neurological diseases such as Parkinson's disease, Alzheimer disease and multiple sclerosis, emphasizing the role of the gut-brain axis. We discuss the hypothesis of a brain microbiota, the BrainBiota: bacteria living in symbiosis with brain cells.

Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics: Second Revision.

Background: The purpose of educational recommendations is to assist in establishing courses and programs in a discipline, to further develop existing educational activities in the various nations, and to support international initiatives for collaboration and sharing of courseware. The International Medical Informatics Association (IMIA) has published two versions of its international recommendations in biomedical and health informatics (BMHI) education, initially in 2000 and revised in 2010. Given the recent changes to the science, technology, the needs of the healthcare systems, and the workforce of BMHI, a revision of the recommendations is necessary.

Best Research Papers in the Field of Sensors, Signals, and Imaging Informatics 2021.

Objectives: In this synopsis, we identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2021.

Methods: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and imaging informatics.

Targeted proteomics identifies circulating biomarkers associated with active COVID-19 and post-COVID-19.

The ongoing Coronavirus Disease 2019 (COVID-19) pandemic is caused by the highly infectious Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). There is an urgent need for biomarkers that will help in better stratification of patients and contribute to personalized treatments. We performed targeted proteomics using the Olink platform and systematically investigated protein concentrations in 350 hospitalized COVID-19 patients, 186 post-COVID-19 individuals, and 61 healthy individuals from 3 independent cohorts.

Optimal Treatment Duration of Bevacizumab as Front-Line Therapy for Advanced Ovarian Cancer: AGO-OVAR 17 BOOST/GINECO OV118/ENGOT Ov-15 Open-Label Randomized Phase III Trial.

Purpose: To compare standard versus extended duration of bevacizumab treatment in combination with front-line chemotherapy in women with newly diagnosed stage IIB-IV ovarian cancer.

Methods: In this multicenter, open-label, randomized phase III trial (ClinicalTrials.gov identifier: NCT01462890), patients with newly diagnosed International Federation of Gynecology and Obstetrics stage IIB-IV epithelial ovarian, fallopian tube, or peritoneal cancer underwent primary cytoreductive surgery followed by six cycles of chemotherapy (paclitaxel 175 mg/m plus carboplatin area under the curve 5 once every 3 weeks) and bevacizumab (15 mg/kg once every 3 weeks).

One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case.

Smart cities and digital public health are closely related. Managing digital transformation in urbanization and living spaces is challenging. It is critical to prioritize the emotional and physical health and well-being of humans and their animals in the dynamic and ever-changing environment they share.

Health Information Systems: Past, Present, Future - Revisited.

Background: Health information systems (HIS) represent an essential part of the infrastructure for the delivery of good health care.

Objectives: To present the author's personal views on HIS developments over the last decades and on the opportunities and priorities for future HIS developments.

Methods: Reflecting on his views, the author identified relevant semantic dimensions, which are denoted as development paths, and searched for appropriate periods to characterize HIS development leaps.

The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping.

Molecular signatures have been suggested as biomarkers to classify pancreatic ductal adenocarcinoma (PDAC) into two, three, four or five subtypes. Since the robustness of existing signatures is controversial, we performed a systematic evaluation of four established signatures for PDAC stratification across nine publicly available datasets. Clustering revealed inconsistency of subtypes across independent datasets and in some cases a different number of PDAC subgroups than in the original study, casting doubt on the actual number of existing subtypes.

Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction.

Background: One of the major challenges in pediatric intensive care is the detection of life-threatening health conditions under acute time constraints and performance pressure. This includes the assessment of pediatric organ dysfunction (OD) that demands extraordinary clinical expertise and the clinician's ability to derive a decision based on multiple information and data sources. Clinical decision support systems (CDSS) offer a solution to support medical staff in stressful routine work.

Reverse inflammaging: Long-term effects of HCV cure on biological age.

Background & Aims: Chronic hepatitis C virus (HCV) infection can be cured with direct-acting antivirals (DAAs). However, not all sequelae of chronic hepatitis C appear to be completely reversible after sustained virologic response (SVR). Recently, chronic viral infections have been shown to be associated with biological age acceleration defined by the epigenetic clock.

Low anti-HBc levels are associated with lower risk of virological relapse after nucleos(t)ide analogue cessation in HBe antigen-negative patients.

Background And Aims: Low anti-HBc serum levels at the time of therapy cessation were linked to a higher relapse risk in predominantly HBeAg-positive cohorts. We investigated the association of anti-HBc levels with relapse in HBeAg-negative patients.

Methods: Serum levels of anti-HBc, HBsAg and HBcrAg were determined in 136 HBeAg-negative patients, participating in a vaccination trial (ABX-203, NCT02249988), before treatment cessation or vaccination.

Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data.

The COVID-19 pandemic has strong effects on most health care systems. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models.