AIMC Topic: Cohort Studies

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A chemotherapy response classifier based on support vector machines for high-grade serous ovarian carcinoma.

Oncotarget
Long-term outcome of high-grade serous epithelial ovarian carcinoma (HGSOC) remains poor as a result of recurrence and the emergence of drug resistance. Almost all the patients were given the same platinum-based chemotherapy after debulking surgery e...

Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury.

Brain injury
BACKGROUND: White matter hyperintensities (WMHs) are foci of abnormal signal intensity in white matter regions seen with magnetic resonance imaging (MRI). WMHs are associated with normal ageing and have shown prognostic value in neurological conditio...

Defining a Patient Population With Cirrhosis: An Automated Algorithm With Natural Language Processing.

Journal of clinical gastroenterology
OBJECTIVES: The objective of this study was to use natural language processing (NLP) as a supplement to International Classification of Diseases, Ninth Revision (ICD-9) and laboratory values in an automated algorithm to better define and risk-stratif...

Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study.

Journal of psychiatry & neuroscience : JPN
BACKGROUND: Brain imaging is of limited diagnostic use in psychiatry owing to clinical heterogeneity and low sensitivity/specificity of between-group neuroimaging differences. Machine learning (ML) may better translate neuroimaging to the level of in...

Simultaneous Laparoscopic Resection of Primary Tumor and Liver Metastases for Colorectal Cancer: Surgical Technique and Short-Term Outcome.

Hepato-gastroenterology
BACKGROUND/AIMS: Laparoscopic approaches are increasingly used in selected patients with either colorectal or liver disease. The aim of this study was to evaluate the safety and feasibility of laparoscopy-assisted combined colorectal and liver resect...

Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring.

Journal of acquired immune deficiency syndromes (1999)
OBJECTIVE: Regular HIV RNA testing for all HIV-positive patients on antiretroviral therapy (ART) is expensive and has low yield since most tests are undetectable. Selective testing of those at higher risk of failure may improve efficiency. We investi...

A clinical perspective on the relevance of research domain criteria in electronic health records.

The American journal of psychiatry
OBJECTIVE: The limitations of the DSM nosology for capturing dimensionality and overlap in psychiatric syndromes, and its poor correspondence to underlying neurobiology, have been well established. The Research Domain Criteria (RDoC), a proposed dime...

Evaluation of a hospital admission prediction model adding coded chief complaint data using neural network methodology.

European journal of emergency medicine : official journal of the European Society for Emergency Medicine
OBJECTIVE: Our objective was to apply neural network methodology to determine whether adding coded chief complaint (CCC) data to triage information would result in an improved hospital admission prediction model than one without CCC data.