AIMC Topic: Electronic Health Records
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Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology.
Clinical pharmacology and therapeutics
Aug 1, 2020
The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive a...
Explainable artificial intelligence model to predict acute critical illness from electronic health records.
Nature communications
Jul 31, 2020
Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these pa...
Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing.
PloS one
Jul 30, 2020
BACKGROUND: Heart failure (HF) is a major cause of morbidity and mortality. However, much of the clinical data is unstructured in the form of radiology reports, while the process of data collection and curation is arduous and time-consuming.
Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data.
Annals of emergency medicine
Jul 24, 2020
STUDY OBJECTIVE: Acute kidney injury occurs commonly and is a leading cause of prolonged hospitalization, development and progression of chronic kidney disease, and death. Early acute kidney injury treatment can improve outcomes. However, current dec...
Prediction on critically ill patients: The role of "big data".
Journal of critical care
Jul 23, 2020
Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simpl...
Natural language processing and machine learning to enable automatic extraction and classification of patients' smoking status from electronic medical records.
Upsala journal of medical sciences
Jul 22, 2020
BACKGROUND: The electronic medical record (EMR) offers unique possibilities for clinical research, but some important patient attributes are not readily available due to its unstructured properties. We applied text mining using machine learning to en...
ROC Curve
Support Vector Machine
Natural Language Processing
Bayes Theorem
Electronic Health Records
Reproducibility of Results
Sweden
Automation
Pattern Recognition, Automated
Smoking
False Positive Reactions
Data Mining
Observer Variation
Medical Informatics
Humans
Machine Learning
Software
Algorithms
Research Design
Tobacco Use Disorder
RAHM: Relation augmented hierarchical multi-task learning framework for reasonable medication stocking.
Journal of biomedical informatics
Jul 14, 2020
As an important task in digital preventive healthcare management, especially in the secondary prevention stage, active medication stocking refers to the process of preparing necessary medications in advance according to the predicted disease progress...
Extraction of temporal relations from clinical free text: A systematic review of current approaches.
Journal of biomedical informatics
Jul 13, 2020
BACKGROUND: Temporal relations between clinical events play an important role in clinical assessment and decision making. Extracting such relations from free text data is a challenging task because it lies on between medical natural language processi...
Identification of Patients with Heart Failure in Large Datasets.
Heart failure clinics
Jul 9, 2020
Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of case...
Interpretable clinical prediction via attention-based neural network.
BMC medical informatics and decision making
Jul 9, 2020
BACKGROUND: The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in th...
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