Information Technology

Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data.

BACKGROUND: With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only 'known' risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data.

DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms.

Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This has led to wide-spread adoption of AI-powered tools, in pursuit of improving accuracy and efficiency of this process. While the unique challeng...

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.

Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk factors (, body mass index (BMI), body fat distributi...

[Big Data, AI and Machine Learning for Precision Psychiatry: How are they changing the clinical practice?].

Currently, we are witnessing an increasing interest in predictive models and personalized diagnosis and treatment choice in psychiatric research. Agai...

Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients.

OBJECTIVE: Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral isc...

Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach.

BACKGROUND: Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many st...

De-identifying free text of Japanese electronic health records.

BACKGROUND: Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast ...

Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily a...

Artificial intelligence in medicine creates real risk management and litigation issues.

The next step in the evolution of electronic medical record (EMR) use is the integration of artificial intelligence (AI) into health care. With the be...

ALICE: a hybrid AI paradigm with enhanced connectivity and cybersecurity for a serendipitous encounter with circulating hybrid cells.

A fully automated and accurate assay of rare cell phenotypes in densely-packed fluorescently-labeled liquid biopsy images remains elusive. Employing ...

Identifying Goals of Care Conversations in the Electronic Health Record Using Natural Language Processing and Machine Learning.

CONTEXT: Goals-of-care discussions are an important quality metric in palliative care. However, goals-of-care discussions are often documented as free...

Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations.

Electronic health records (EHRs) contain rich documentation regarding disease symptoms and progression, but EHR data is challenging to use for diagnos...

Toward automated severe pharyngitis detection with smartphone camera using deep learning networks.

PURPOSE: Severe pharyngitis is frequently associated with inflammations caused by streptococcal pharyngitis, which can cause immune-mediated and post-...

Structuring, reuse and analysis of electronic dental data using the Oral Health and Disease Ontology.

BACKGROUND: A key challenge for improving the quality of health care is to be able to use a common framework to work with patient information acquired...

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review.

The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in many countries. This poses a serious threat to the...

Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension.

Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this stu...

Graph Neural Network-Based Diagnosis Prediction.

Diagnosis prediction is an important predictive task in health care that aims to predict the patient future diagnosis based on their historical medica...

Medical speciality classification system based on binary particle swarms and ensemble of one vs. rest support vector machines.

Nowadays, artificial intelligence plays an integral role in medical and healthcare informatics. Developing an automatic question classification and an...

Tele-robotics and artificial-intelligence in stroke care.

In the last forty years, the field of medicine has experienced dramatic shifts in technology-enhanced surgical procedures - from its initial use in 19...

Explainable artificial intelligence model to predict acute critical illness from electronic health records.

Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinic...