AIMC Topic: Data Accuracy

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Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We...

Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning.

Journal of global health
BACKGROUND: Internet search engine data, such as Google Trends, was shown to be correlated with the incidence of COVID-19, but only in several countries. We aim to develop a model from a small number of countries to predict the epidemic alert level i...

Data for registry and quality review can be retrospectively collected using natural language processing from unstructured charts of arthroplasty patients.

The bone & joint journal
AIMS: Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clini...

H-Accuracy, an Alternative Metric to Assess Classification Models in Medicine.

Studies in health technology and informatics
As widely known, regular accuracy is a misleading and shallow indicator of the performance of a predictive model, especially in real-life domains like medicine, where decisions affect health or life. In this paper we present and discuss a new accurac...

Developing a Prototype Knowledge-Based System for Diagnosis and Treatment of Diabetes Using Data Mining Techniques.

Ethiopian journal of health sciences
BACKGROUND: Diabetes is a disease that affects the body's ability to produce or use insulin. A total of 425 million people are suffering from diabetes in the world. Of this, more than 16 million people live in the Africa Region, which is estimated to...

Imputing Missing Data In Large-Scale Multivariate Biomedical Wearable Recordings Using Bidirectional Recurrent Neural Networks With Temporal Activation Regularization.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Miniaturized and wearable sensor-based measurements offer unprecedented opportunities to study and assess human behavior in natural settings with wide ranging applications including in healthcare, wellness tracking and entertainment. However, wearabl...

Information Adapted Machine Learning Models for Prediction in Clinical Workflow.

Studies in health technology and informatics
BACKGROUND: In a database of electronic health records, the amount of available information varies widely between patients. In a real-time prediction scenario, a machine learning model may receive limited information for some patients.

SANA NetGO: a combinatorial approach to using Gene Ontology (GO) terms to score network alignments.

Bioinformatics (Oxford, England)
MOTIVATION: Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure GO similarity between proteins in isolation, but proteins in a network alignment are not isolate...

Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?

Epidemiology (Cambridge, Mass.)
The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for misme...