AIMC Topic: Prognosis

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Subclinical Hypothyroidism Is Associated With Adverse Prognosis in Heart Failure Patients.

The Canadian journal of cardiology
BACKGROUND: It is widely recognized that overt hyper- as well as hypothyroidism are potential causes of heart failure (HF). Additionally it has been recently reported that subclinical hypothyroidism (sub-hypo) is associated with atherosclerosis, deve...

Interactive phenotyping of large-scale histology imaging data with HistomicsML.

Scientific reports
Whole-slide imaging of histologic sections captures tissue microenvironments and cytologic details in expansive high-resolution images. These images can be mined to extract quantitative features that describe tissues, yielding measurements for hundre...

Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases).

Schizophrenia research
Psychiatric diseases are very heterogeneous both in clinical manifestation and etiology. With the recent rise of using machine learning techniques to attempt to diagnose and prognose these disorders, the issue of heterogeneity becomes increasingly im...

A new machine-learning method to prognosticate paraquat poisoned patients by combining coagulation, liver, and kidney indices.

PloS one
The prognosis of paraquat (PQ) poisoning is highly correlated to plasma PQ concentration, which has been identified as the most important index in PQ poisoning. This study investigated the predictive value of coagulation, liver, and kidney indices in...

Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning.

JACC. Cardiovascular imaging
OBJECTIVES: This study evaluated the added predictive value of combining clinical information and myocardial perfusion single-photon emission computed tomography (SPECT) imaging (MPI) data using machine learning (ML) to predict major adverse cardiac ...

High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision.

Radiology
Purpose To develop a machine learning model that allows high-risk breast lesions (HRLs) diagnosed with image-guided needle biopsy that require surgical excision to be distinguished from HRLs that are at low risk for upgrade to cancer at surgery and t...

A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome.

Scientific reports
Gene expression signatures are commonly used as predictive biomarkers, but do not capture structural features within the tissue architecture. Here we apply a 2-step machine learning framework for quantitative imaging of tumor vasculature to derive a ...

A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Early death after a treatment can be seen as a therapeutic failure. Accurate prediction of patients at risk for early mortality is crucial to avoid unnecessary harm and reducing costs. The goal of our work is two-fold: first, to evaluate ...

Learning ensemble classifiers for diabetic retinopathy assessment.

Artificial intelligence in medicine
Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doct...