Hospital-Based Medicine

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission.

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and re...

Propensity matched analysis of short term oncological and perioperative outcomes following robotic and thoracolaparoscopic esophagectomy for carcinoma esophagus- the first Indian experience.

Thoracolaparoscopic esophagectomy (TLE) for carcinoma esophagus has better short-term outcomes compared to open esophagectomy. The precise role of robot-assisted laparoscopic esophagectomy (RALE) is still evolving. Single center retrospective analysis of TLE and RALE performed for carcinoma esophagus between January 2015 and September 2018. Propensity score matching was done between the groups for...

Early risk assessment for COVID-19 patients from emergency department data using machine learning.

Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million rep...

Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables.

This study aimed to develop a machine learning algorithm to identify key clinical measures to triage patients more effectively to general admission v...

HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study.

Early detection of sepsis can be life-saving. Machine learning models have shown great promise in early sepsis prediction when applied to patient phys...

Patient journey through cases of depression from claims database using machine learning algorithms.

Health insurance and acute hospital-based claims have recently become available as real-world data after marketing in Japan and, thus, classification ...

From predictions to prescriptions: A data-driven response to COVID-19.

The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatm...

Multimodal temporal-clinical note network for mortality prediction.

BACKGROUND: Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provid...

Development of a Smart Hospital Assistant: Integrating Artificial Intelligence and a Voice-User Interface for Improved Surgical Outcomes.

Patient safety and efficiency are top priorities in any surgical procedure. One effective way to achieve these objectives is to automate the logistica...

Ultraviolet disinfection robots to improve hospital cleaning: Real promise or just a gimmick?

The global COVID-19 pandemic due to the novel coronavirus SARS-CoV-2 has challenged the availability of traditional surface disinfectants. It has also...

Full robotic multivisceral resections: the Modena experience and literature review.

The robotic platform is becoming a multidisciplinary tool, versatile, and suitable for multiple procedures. Combined multivisceral resections may repr...

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and red...

Immediate post-operative PDE5i therapy improves early erectile function outcomes after robot assisted radical prostatectomy (RARP).

To assess whether the timing of post-operative Phosphodiesterase Inhibitor (PDE5i) therapy after Robot-Assisted Radical Prostatectomy (RARP) is associ...

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate p...

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.

In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has sho...

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage.

We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage ...

Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks.

Chronic obstructive pulmonary disease (COPD) is a global burden, which is estimated to be the third leading cause of death worldwide by 2030. The econ...

Effect of assist-as-needed robotic gait training on the gait pattern post stroke: a randomized controlled trial.

BACKGROUND: Regaining gait capacity is an important rehabilitation goal post stroke. Compared to clinically available robotic gait trainers, robots wi...

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predic...

Machine learning-based prediction of in-hospital mortality using admission laboratory data: A retrospective, single-site study using electronic health record data.

Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resourc...