Hospitalists

Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth.

Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in the case of a vaginal birth. We studied machine learning models to predict postpartum hemorrhage. Women who underwent vaginal birth at the Tokyo Women Medical University East Center between 1995 and 2020 were included. We used 11 clinical variables to ...

An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model.

Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water quality. Artificial intelligence (AI) methods have previously proved to be accurate tools for DO concentration prediction. This study presents the implementation of a deep learning approach applied to a recurrent neural network (RNN) algorithm. The proposed deep recurrent neural network (DRNN) model...

Beneficial Effects of Robot-Assisted Gait Training on Functional Recovery in Women after Stroke: A Cohort Study.

Robot-assisted gait training (RAGT) could be a rehabilitation option for patients after experiencing a stroke. This study aims to determine the sex-r...

Interpretable time-aware and co-occurrence-aware network for medical prediction.

BACKGROUND: Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it's hard due to the special da...

Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments.

Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance co...

Single-port Mini-Pfannenstiel Robotic Pyeloplasty: Establishing a Non-narcotic Pathway Along With a Same-day Discharge Protocol.

OBJECTIVE: To analyze the feasibility of a same day discharge protocol following single-port (SP) robotic pyeloplasty.

The Route of Motor Recovery in Stroke Patients Driven by Exoskeleton-Robot-Assisted Therapy: A Path-Analysis.

: Exoskeleton-robot-assisted therapy is known to positively affect the recovery of arm functions in stroke patients. However, there is a lack of evide...

A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis.

A massive amount of multimodal data are continuously collected in the intensive care unit (ICU) along each patient stay, offering a great opportunity ...

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge.

OBJECTIVES: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making p...

Investigating the impact of sewer overflow on the environment: A comprehensive literature review paper.

Sewer networks play a pivotal role in our everyday lives by transporting the stormwater and urban sewage away from the urban areas. In this regard, Se...

Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques.

Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to ef...

Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm.

BACKGROUND: The ability to reliably predict outcomes after trauma in older adults (age ≥ 65 y) is critical for clinical decision making. Using novel m...

Transhiatal robot-assisted minimally invasive esophagectomy: unclear benefits compared to traditional transhiatal esophagectomy.

Esophagectomy is a high-risk operation, regardless of technique. Minimally invasive transthoracic esophagectomy could reduce length of stay and pulmon...

Using different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing.

COVID-19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system. Using 38 blood t...

Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques.

Biomedical natural language processing (NLP) has an important role in extracting consequential information in medical discharge notes. Detecting meani...

Limitations of Transformers on Clinical Text Classification.

Bidirectional Encoder Representations from Transformers (BERT) and BERT-based approaches are the current state-of-the-art in many natural language pro...

Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM.

This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff...

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 pati...

Electric signal synchronization as a behavioural strategy to generate social attention in small groups of mormyrid weakly electric fish and a mobile fish robot.

African weakly electric fish communicate at night by constantly emitting and perceiving brief electrical signals (electric organ discharges, EOD) at v...

A Machine Learning Approach to Predict Acute Ischemic Stroke Thrombectomy Reperfusion using Discriminative MR Image Features.

Mechanical thrombectomy (MTB) is one of the two standard treatment options for Acute Ischemic Stroke (AIS) patients. Current clinical guidelines instr...