AI Medical Compendium Topic:
Electronic Health Records

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Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer.

JAMA network open
IMPORTANCE: Electronic health records (EHRs) provide a low-cost means of accessing detailed longitudinal clinical data for large populations. A lung cancer cohort assembled from EHR data would be a powerful platform for clinical outcome studies.

Augmented intelligence to predict 30-day mortality in patients with cancer.

Future oncology (London, England)
An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. An algorithm to predict 30-day mortality risk was developed us...

Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China.

BMC medical informatics and decision making
BACKGROUND: With the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can...

Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID).

Scientific reports
In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individu...

Treatment initiation prediction by EHR mapped PPD tensor based convolutional neural networks boosting algorithm.

Journal of biomedical informatics
Electronic health records contain patient's information that can be used for health analytics tasks such as disease detection, disease progression prediction, patient profiling, etc. Traditional machine learning or deep learning methods treat EHR ent...

A machine learning approach to predict healthcare cost of breast cancer patients.

Scientific reports
This paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients ...

Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population.

BMJ open
PURPOSE: The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challeng...

Gaining Insights Into Patient Satisfaction Through Interpretable Machine Learning.

IEEE journal of biomedical and health informatics
Patient satisfaction is a key performance indicator of patient-centered care and hospital reimbursement. To discover the major factors that affect patient experiences is considered as an effective way to formulate corrective actions. A patient during...

A Deep Learning-Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients.

IEEE journal of biomedical and health informatics
Physicians increasingly depend on electronic health records (EHRs) to manage their patients. However, many patient records have substantial missing values that pose a fundamental challenge to their clinical use. To address this prevailing challenge, ...

CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support.

IEEE/ACM transactions on computational biology and bioinformatics
The rapidly developed Health 2.0 technology has provided people with more opportunities to conduct online medical consultation than ever before. Understanding contexts within different online medical communications and activities becomes a significan...