Hospitalists

Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission.

BACKGROUND: An aging population with a burden of chronic diseases puts increasing pressure on health care systems. Early prediction of the hospital length of stay (LOS) can be useful in optimizing the allocation of medical resources, and improving healthcare quality. However, the data available at the point of admission (PoA) are limited, making it difficult to forecast the LOS accurately.

Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images.

Information regarding water clarity at large spatiotemporal scales is critical for understanding comprehensive changes in the water quality and status of ecosystems. Previous studies have suggested that satellite observation is an effective means of obtaining such information. However, a reliable model for accurately mapping the water clarity of global lakes (reservoirs) is still lacking due to th...

Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction.

This paper proposes a representation learning framework HE-LSTM model for heterogeneous temporal events, which can automatically adapt to the multisca...

Automatic classification of nerve discharge rhythms based on sparse auto-encoder and time series feature.

BACKGROUND: Nerve discharge is the carrier of information transmission, which can reveal the basic rules of various nerve activities. Recognition of t...

Early heart rate variability evaluation enables to predict ICU patients' outcome.

Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has be...

Assessment of the da Vinci Single Port Robotic Platform on Cholecystectomy in Adolescents.

The new da Vinci single port (SP) robotic platform has great appeal for pediatric surgery. To assess its efficacy and identify potential challenges, ...

Incorporating uncertainty in learning to defer algorithms for safe computer-aided diagnosis.

Deep neural networks are increasingly being used for computer-aided diagnosis, but erroneous diagnoses can be extremely costly for patients. We propos...

Dietary Inhibitors of CYP3A4 Are Revealed Using Virtual Screening by Using a New Deep-Learning Classifier.

CYP3A4 is the main human enzyme responsible for phase I metabolism of dietary compounds, prescribed drugs and xenobiotics, steroid hormones, and bile ...

Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit.

The study aimed to establish a machine learning-based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (IC...

Analysis and prediction of water quality using deep learning and auto deep learning techniques.

Natural water sources like ponds, lakes and rivers are facing a great threat because of activities like discharge of untreated industrial effluents, s...

Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example.

BACKGROUND: There are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment ...

Evaluation of shape factor impact on discharge coefficient of side orifices using boost simulation model with extreme learning machine data-driven.

In this paper, for the first time, the impact of the shape factor on the discharge coefficient of side orifices is evaluated using the novel Extreme L...

Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients.

Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of...

Outpatient Inpatient Robot-Assisted Radical Prostatectomy: An Evidence-Based Analysis of Comparative Outcomes.

To provide a systematic analysis of outcomes comparing outpatient and inpatient robot-assisted radical prostatectomy (RARP) for prostate cancer based...

Rhabdomyolysis: An evidence-based approach.

A 76-year-old lady was found on the floor following a fall at home. She was uninjured, but unable to get up, and had been lying on the floor for rough...

Comparison of Higher-Than-Standard to D-Dimer Driven Thromboprophylaxis in Hospitalized Patients With COVID-19.

Coronavirus disease 2019 is a global health threat often accompanied with coagulopathy. Despite use of thromboprophylaxis in this population, thrombo...

Computational signatures for post-cardiac arrest trajectory prediction: Importance of early physiological time series.

BACKGROUND: There is an unmet need for timely and reliable prediction of post-cardiac arrest (CA) clinical trajectories. We hypothesized that physiolo...

Diagnostic Value of SonoVue Contrast-Enhanced Ultrasonography in Nipple Discharge Based on Artificial Intelligence.

This paper aims to explore the application value of SonoVue contrast-enhanced ultrasonography based on deep unsupervised learning (DNS) in the diagnos...

Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients.

Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant componen...

Intelligent Monitoring of Care Status for COPD Patients Based on Deep Learning.

To discuss the application method and effect of COPD patients in deep learning in intelligent monitoring, two groups were used under a reasonable sele...