AIMC Topic: Machine Learning

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Korean clinical entity recognition from diagnosis text using BERT.

BMC medical informatics and decision making
BACKGROUND: While clinical entity recognition mostly aims at electronic health records (EHRs), there are also the demands of dealing with the other type of text data. Automatic medical diagnosis is an example of new applications using a different dat...

Leveraging TCGA gene expression data to build predictive models for cancer drug response.

BMC bioinformatics
BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression da...

Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation.

PloS one
BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) is a target for cardiovascular prevention. Contemporary equations for LDL-C estimation have limited accuracy in certain scenarios (high triglycerides [TG], very low LDL-C).

Prediction of Promiscuity Cliffs Using Machine Learning.

Molecular informatics
Compounds with the ability to interact with multiple targets, also called promiscuous compounds, provide the basis for polypharmacological drug discovery. In recent years, a plethora of structural analogs with different promiscuity has been identifie...

Prognostic machine learning models for COVID-19 to facilitate decision making.

International journal of clinical practice
An increasing number of COVID-19 cases worldwide has overwhelmed the healthcare system. Physicians are struggling to allocate resources and to focus their attention on high-risk patients, partly because early identification of high-risk individuals i...

Transfer Learning Based Automatic Human Identification using Dental Traits- An Aid to Forensic Odontology.

Journal of forensic and legal medicine
Forensic Odontology deals with identifying humans based on their dental traits because of their robust nature. Classical methods of human identification require more manual effort and are difficult to use for large number of Images. A Novel way of au...

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

Annals of clinical and translational neurology
OBJECTIVE: Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course ...

Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches.

Journal of the American Heart Association
Background Real-world healthcare data are an important resource for epidemiologic research. However, accurate identification of patient cohorts-a crucial first step underpinning the validity of research results-remains a challenge. We developed and e...

Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models.

Journal of ovarian research
BACKGROUND: The foundation of modern ovarian cancer care is cytoreductive surgery to remove all macroscopic disease (R0). Identification of R0 resection patients may help individualise treatment. Machine learning and AI have been shown to be effectiv...

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches.

Journal of neuroengineering and rehabilitation
BACKGROUND: Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process...