AIMC Topic: Aged, 80 and over
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Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images.
Communications biology
Jan 8, 2020
Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachmen...
Assessing stroke severity using electronic health record data: a machine learning approach.
BMC medical informatics and decision making
Jan 8, 2020
BACKGROUND: Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structur...
Machine learning methods for optimal prediction of motor outcome in Parkinson's disease.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jan 7, 2020
PURPOSE: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus, it is critical to improve prediction of outcome in PD patients.
Perioperative adverse events in women over age 65 undergoing robot-assisted sacrocolpopexy.
International urogynecology journal
Jan 3, 2020
INTRODUCTION AND HYPOTHESIS: Pelvic floor disorders are common among and disproportionately affect older women. There are limited data regarding perioperative adverse events in older women undergoing robot-assisted sacrocolpopexy (RASC) specifically....
Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.
European radiology
Jan 3, 2020
OBJECTIVES: We aimed to establish and validate an artificial intelligence-based radiomics strategy for predicting personalized responses of hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) session by quantitatively analy...
Automated CT registration tool improves sensitivity to change in ventricular volume in patients with shunts and drains.
The British journal of radiology
Jan 3, 2020
OBJECTIVE: CT is the mainstay imaging modality for assessing change in ventricular volume in patients with ventricular shunts or external ventricular drains (EVDs). We evaluated the performance of a novel fully automated CT registration and subtracti...
Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes.
JAMA network open
Jan 3, 2020
IMPORTANCE: Accurate risk stratification of patients with heart failure (HF) is critical to deploy targeted interventions aimed at improving patients' quality of life and outcomes.
Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study.
Internal and emergency medicine
Jan 2, 2020
Length of stay (LOS) and discharge destination predictions are key parts of the discharge planning process for general medical hospital inpatients. It is possible that machine learning, using natural language processing, may be able to assist with ac...
Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation.
Artificial intelligence in medicine
Dec 31, 2019
BACKGROUND AND OBJECTIVE: The measurement of carotid intima media thickness (CIMT) in ultrasound images can be used to detect the presence of atherosclerotic plaques. Usually, the CIMT estimation strategy is semi-automatic, since it requires: (1) a m...
Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.
Arthritis research & therapy
Dec 30, 2019
BACKGROUND: Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We devel...
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