AIMC Topic: Comorbidity

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Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm.

International journal of medical informatics
OBJECTIVE: Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk...

Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions.

Clinical neurology and neurosurgery
OBJECTIVES: Machine Learning and Artificial Intelligence (AI) are rapidly growing in capability and increasingly applied to model outcomes and complications within medicine. In spinal surgery, post-operative surgical site infections (SSIs) are a rare...

Identifying epilepsy psychiatric comorbidities with machine learning.

Acta neurologica Scandinavica
OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect suicidality in adults. This study's purpose was to use spoken words to create machine-learning classif...

Representation learning for clinical time series prediction tasks in electronic health records.

BMC medical informatics and decision making
BACKGROUND: Electronic health records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high dimensionality, sparseness, n...

Analysis of disease comorbidity patterns in a large-scale China population.

BMC medical genomics
BACKGROUND: Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set.

Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study.

Journal of the American Academy of Dermatology
BACKGROUND: Psoriasis is associated with elevated risk of heart attack and increased accumulation of subclinical noncalcified coronary burden by coronary computed tomography angiography (CCTA). Machine learning algorithms have been shown to effective...

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

British journal of anaesthesia
BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores ...

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder.

Scientific reports
Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP)...