Hospital-Based Medicine

Latest AI and machine learning research in hospital-based medicine for healthcare professionals.

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$\mathtt {Deepr}$: A Convolutional Net for Medical Records.

Feature engineering remains a major bottleneck when creating predictive systems from electronic medi...

Single-pass albumin dialysis in a child aged six months with phenobarbital poisoning.

A girl aged six months was hospitalized because of resistant seizures and was discharged with phenob...

Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

BACKGROUND: Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about ...

Ileostomy creation in colorectal cancer surgery: risk of acute kidney injury and chronic kidney disease.

BACKGROUND: Ileostomy creation is associated with postoperative dehydration and readmission; however...

The impact of post-stroke complications on in-hospital mortality depends on stroke severity.

INTRODUCTION: Controversies remain on whether post-stroke complications represent an independent pre...

Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks.

BACKGROUND: Outcome prediction is important in the clinical decision-making process. Artificial neur...

Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes.

OBJECTIVE: The continuous uninterrupted feedback system is the essential part of any well-organized ...

Early surgery after angiography in patients scheduled for valve replacement.

Background There are limited data regarding the risks of cardiac surgery early after coronary angiog...

Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries.

Clinical narrative text includes information related to a patient's medical history such as chronolo...

Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.

BACKGROUND: Although risk adjustment remains a cornerstone for comparing outcomes across hospitals, ...

Robotic Pancreaticoduodenectomy: Single-Surgeon Initial Experience.

Minimally invasive surgery has gained increasing acceptance over the last few years, which has expan...

Predicting early psychiatric readmission with natural language processing of narrative discharge summaries.

The ability to predict psychiatric readmission would facilitate the development of interventions to ...

Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources.

OBJECTIVE: Text and data mining play an important role in obtaining insights from Health and Hospita...

Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

Muscle spindle discharge during active movement is a function of mechanical and neural parameters. M...

Baseline characteristics of patients with heart failure and preserved ejection fraction at admission with acute heart failure in Saudi Arabia.

UNLABELLED: Heart failure and preserved ejection fraction (HFpEF) is defined as heart failure sympto...

A mixed-ensemble model for hospital readmission.

OBJECTIVE: A hospital readmission is defined as an admission to a hospital within a certain time fra...

Technical notes on pure laparoscopic isolated caudate lobectomy for patient with liver cancer.

BACKGROUND: The advantages of laparoscopic liver resection become more obvious as evidence on its lo...

Intrathecal injection of tigecycline in treatment of multidrug-resistant meningitis: a case report.

The ubiquitous is an important and troublesome pathogen of nosocomial infection. Multidrug-resistan...

Dense Annotation of Free-Text Critical Care Discharge Summaries from an Indian Hospital and Associated Performance of a Clinical NLP Annotator.

Electronic Health Record (EHR) use in India is generally poor, and structured clinical information i...

Energy landscapes for a machine-learning prediction of patient discharge.

The energy landscapes framework is applied to a configuration space generated by training the parame...

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