Infection Control

Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers.

Skin cancer is among the 10 most common cancers. Recent research revealed the superiority of artificial intelligence (AI) over dermatologists to diagnose skin cancer from predesignated and cropped images. However, there remain several uncertainties for AI in diagnosing skin cancers, including lack of testing for consistency, lack of pathological proof or ambiguous comparisons. Hence, to develop a ...

Closing the Digital Health Evidence Gap: Development of a Predictive Score to Maximize Patient Outcomes.

Clinical studies of telemedicine (TM) programs for chronic illness have demonstrated mixed results across settings and populations. With recent uptake in use of digital health modalities, more precise patient classification may improve outcomes, efficiency, and effectiveness. The purpose of the research was to develop a predictive score that measures the influence of patient characteristics on T...

Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease.

Reducing preventable hospital re-admissions in Sickle Cell Disease (SCD) could potentially improve outcomes and decrease healthcare costs. In a retros...

Comparing machine learning with case-control models to identify confirmed dengue cases.

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to dete...

Safety and efficacy of robot-assisted versus open pancreaticoduodenectomy: a meta-analysis of multiple worldwide centers.

The objective of the study is to compare the safety and efficacy of robot-assisted pancreaticoduodenectomy (PD) with open PD. The PubMed, EMBASE and C...

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.

Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system bas...

Assessment of User Needs for Telemedicine Robots in a Developing Nation Hospital Setting.

This study aimed to investigate the needs of medical users of telemedicine robots to encourage international cooperation and development. As the use...

Effect of Obesity on Clinical Outcomes of Patients Treated With Cefepime.

As the prevalence of obesity climbs, dosing of antimicrobials, particularly cephalosporins, is becoming a greater challenge for clinicians. Data are ...

The implementation of a rapid sample preparation method for the detection of SARS-CoV-2 in a diagnostic laboratory in South Africa.

The SARS-CoV-2 pandemic has resulted in shortages of both critical reagents for nucleic acid purification and highly trained staff as supply chains ar...

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.

BACKGROUND: Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs...

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital.

BACKGROUND: Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct res...

Artificial Intelligence-Driven Oncology Clinical Decision Support System for Multidisciplinary Teams.

Watson for Oncology (WfO) is a clinical decision support system driven by artificial intelligence. In Korea, WfO is used by multidisciplinary teams (M...

Stroke prognostication for discharge planning with machine learning: A derivation study.

Post-stroke discharge planning may be aided by accurate early prognostication. Machine learning may be able to assist with such prognostication. The s...

Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections.

Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patients are difficult to diagnosis. Metabolomic changes ...

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea.

Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other c...

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study.

The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwi...

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population.

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other comm...

In-Hospital Prognostic Value of Electrocardiographic Parameters Other Than ST-Segment Changes in Acute Myocardial Infarction: Literature Review and Future Perspectives.

Electrocardiography (ECG) remains an irreplaceable tool in the management of the patients with myocardial infarction, with evaluation of the QRS and S...

Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China.

The efficiency of disease prevention and medical care service necessitated the prediction of incidence. However, predictive accuracy and power were la...

Predicting hospital admission for older emergency department patients: Insights from machine learning.

BACKGROUND: Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories o...