AIMC Topic: Electronic Health Records

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A cross-lingual approach to automatic ICD-10 coding of death certificates by exploring machine translation.

Journal of biomedical informatics
Automatic ICD-10 coding is an unresolved challenge in terms of Machine Learning tasks. Despite hospitals generating an enormous amount of clinical documents, data is considerably sparse, associated with a very skewed and unbalanced code distribution,...

Classification of Patients with Coronary Microvascular Dysfunction.

IEEE/ACM transactions on computational biology and bioinformatics
While coronary microvascular dysfunction (CMD) is a major cause of ischemia, it is very challenging to diagnose due to lack of CMD-specific screening measures. CMD has been identified as one of the five priority areas of investigation in a 2014 Natio...

Recurrent neural networks with segment attention and entity description for relation extraction from clinical texts.

Artificial intelligence in medicine
At present, great progress has been achieved on the relation extraction for clinical texts, but we have noticed that the current models have great drawbacks when dealing with long sentences and multiple entities in a sentence. In this paper, we propo...

Efficient learning from big data for cancer risk modeling: A case study with melanoma.

Computers in biology and medicine
BACKGROUND: Building cancer risk models from real-world data requires overcoming challenges in data preprocessing, efficient representation, and computational performance. We present a case study of a cloud-based approach to learning from de-identifi...

Predicting coronary artery disease: a comparison between two data mining algorithms.

BMC public health
BACKGROUND: Cardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data minin...

Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study.

Annals of medicine
Investigation of the clinical potential of extensive phenotype data and machine learning (ML) in the prediction of mortality in acute coronary syndrome (ACS). The value of ML and extensive clinical data was analyzed in a retrospective registry stud...

The Price of Artificial Intelligence.

Yearbook of medical informatics
INTRODUCTION: Whilst general artificial intelligence (AI) is yet to appear, today's narrow AI is already good enough to transform much of healthcare over the next two decades.

Machine learning for phenotyping opioid overdose events.

Journal of biomedical informatics
OBJECTIVE: To develop machine learning models for classifying the severity of opioid overdose events from clinical data.

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

Computers in biology and medicine
OBJECTIVE: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for...

Predicting childhood obesity using electronic health records and publicly available data.

PloS one
BACKGROUND: Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. ...