Hospital-Based Medicine

RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection.

Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behaviour rely on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection me...

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

IMPORTANCE: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models.

Fully end-to-end deep-learning-based diagnosis of pancreatic tumors.

Artificial intelligence can facilitate clinical decision making by considering massive amounts of medical imaging data. Various algorithms have been i...

Comparison of Robot-Assisted and Laparoscopic Partial Nephrectomy for Renal Hilar Tumors: Results from a Tertiary Referral Center.

To compare perioperative, functional, and oncologic outcomes between robot-assisted partial nephrectomy (RAPN) and laparoscopic partial nephrectomy (...

Artificial intelligence in radiology: relevance of collaborative work between radiologists and engineers for building a multidisciplinary team.

The use of artificial intelligence (AI) algorithms in the field of radiology is becoming more common. Several studies have demonstrated the potential ...

Response to: Williams, Kotamarti, and Schulman re: "Outpatient Robot-Assisted Radical Prostatectomy: Are Patients Ready for Same-Day Discharge?" by Dobbs et al.

Image registration: Maximum likelihood, minimum entropy and deep learning.

In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysi...

Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning.

The impact of sex and gender in the incidence and severity of coronavirus disease 2019 (COVID-19) remains controversial. Here, we aim to describe the...

Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification.

To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinica...

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, vira...

The role of artificial intelligence in surgical patient perioperative management.

Perioperative medicine is a patient-centered, multidisciplinary and integrated clinical practice that starts from the moment of contemplation of surge...

Artificial intelligence (AI) in breast cancer care - Leveraging multidisciplinary skills to improve care.

A plea to merge clinical and public health practices: reasons and consequences.

BACKGROUND: Revisiting professionalism, both as a medical ideal and educational topic, this paper asks whether, in the rise of artificial intelligence...

A Data-Driven Approach to Predict and Classify Epileptic Seizures from Brain-Wide Calcium Imaging Video Data.

The prediction of epileptic seizures has been an essential problem of epilepsy study. The calcium imaging video data images the whole brain-wide neuro...

Machine learning approach for predicting Fusarium culmorum and F. proliferatum growth and mycotoxin production in treatments with ethylene-vinyl alcohol copolymer films containing pure components of essential oils.

Fusarium culmorum and F. proliferatum can grow and produce, respectively, zearalenone (ZEA) and fumonisins (FUM) in different points of the food chain...

Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches.

Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding high risk late ...

Using machine learning tools to predict outcomes for emergency department intensive care unit patients.

The number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vi...

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective.

BACKGROUND: Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. E...

Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis.

BACKGROUND: Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). However, this operation is complex, and the peri-oper...

A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals.

Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a lar...