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Electronic Health Records

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Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease.

BMJ health & care informatics
BACKGROUND: Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hos...

Evaluating the effectiveness of a sliding window technique in machine learning models for mortality prediction in ICU cardiac arrest patients.

International journal of medical informatics
Extensive research has been devoted to predicting ICU mortality, to assist clinical teams managing critical patients. Electronic health records (EHR) contain both static and dynamic medical data, with the latter accumulating during ICU stays. Existin...

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.

Psychiatry research
Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic Topic Modeling, a natural language processing method that evaluates topic changes over time, to anal...

Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques.

Computers in biology and medicine
Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increased. Artificial Intelligence has proved to be instrumental in development of various such techniques...

Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language, enabling computers to understand, generate, and derive meaning from human language. NLP's potential appli...

Machine learning computational model to predict lung cancer using electronic medical records.

Cancer epidemiology
BACKGROUND: Lung cancer (LC) screening using low-dose computed tomography (CT) is recommended according to standard risk criteria or personalized risk calculators. Machine learning (ML) models that can predict disease risk are an emerging method in m...

Deep representation learning from electronic medical records identifies distinct symptom based subtypes and progression patterns for COVID-19 prognosis.

International journal of medical informatics
OBJECTIVE: Symptoms are significant kind of phenotypes for managing and controlling of the burst of acute infectious diseases, such as COVID-19. Although patterns of symptom clusters and time series have been considered the high potential prediction ...

MoCab: A framework for the deployment of machine learning models across health information systems.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and divers...

An Approach to Potentially Increasing Adoption of an Artificial Intelligence-Enabled Electronic Medical Record Encounter in Canadian Primary Care: Protocol for a User-Centered Design.

JMIR research protocols
BACKGROUND: Primary care physicians are at the forefront of the clinical process that can lead to diagnosis, referral, and treatment. With electronic medical records (EMRs) being introduced and, over time, gaining acceptance by primary care users, th...

ARDSFlag: an NLP/machine learning algorithm to visualize and detect high-probability ARDS admissions independent of provider recognition and billing codes.

BMC medical informatics and decision making
BACKGROUND: Despite the significance and prevalence of acute respiratory distress syndrome (ARDS), its detection remains highly variable and inconsistent. In this work, we aim to develop an algorithm (ARDSFlag) to automate the diagnosis of ARDS based...