[BOOK][B] Healthcare data analytics
CK Reddy, CC Aggarwal - 2015 - books.google.com
Supplying a comprehensive overview of healthcare analytics research, Healthcare Data
Analytics provides an understanding of the analytical techniques currently available to solve …
Analytics provides an understanding of the analytical techniques currently available to solve …
Efficient implementation of penalized regression for genetic risk prediction
Polygenic risk scores (PRS) combine many single-nucleotide polymorphisms into a score
reflecting the genetic risk of develo** a disease. Privé, Aschard, and Blum present an …
reflecting the genetic risk of develo** a disease. Privé, Aschard, and Blum present an …
Integrating somatic mutations for breast cancer survival prediction using machine learning methods
Z He, J Zhang, X Yuan, Y Zhang - Frontiers in genetics, 2021 - frontiersin.org
Breast cancer is the most common malignancy in women, and because it has a high
mortality rate, it is urgent to develop computational methods to increase the accuracy of …
mortality rate, it is urgent to develop computational methods to increase the accuracy of …
Stratification of breast cancer by integrating gene expression data and clinical variables
Z He, J Zhang, X Yuan, J **, Z Liu, Y Zhang - Molecules, 2019 - mdpi.com
Breast cancer is a heterogeneous disease. Although gene expression profiling has led to
the definition of several subtypes of breast cancer, the precise discovery of the subtypes …
the definition of several subtypes of breast cancer, the precise discovery of the subtypes …
Classification based on extensions of LS-PLS using logistic regression: application to clinical and multiple genomic data
C Bazzoli, S Lambert-Lacroix - BMC bioinformatics, 2018 - Springer
Background To address high-dimensional genomic data, most of the proposed prediction
methods make use of genomic data alone without considering clinical data, which are often …
methods make use of genomic data alone without considering clinical data, which are often …
Diagnosis using clinical/pathological and molecular information
I Irigoien, C Arenas - Statistical methods in medical research, 2016 - journals.sagepub.com
In diagnosis and classification diseases multiple outcomes, both molecular and
clinical/pathological are routinely gathered on patients. In recent years, many approaches …
clinical/pathological are routinely gathered on patients. In recent years, many approaches …
Clinico-genomic data analytics for precision diagnosis and disease management
Patient data can be present in clinical notes, lab results, genomic data sources,
environmental and geospatial data sources and tissue banks to name a few. A holistic view …
environmental and geospatial data sources and tissue banks to name a few. A holistic view …
Characterizing physicians practice phenotype from unstructured electronic health records
Clinical practice varies among physicians in ways that could lead to variation in what is
documented in a patient's electronic health records (EHR) and act as a source of bias to …
documented in a patient's electronic health records (EHR) and act as a source of bias to …
Predictive Models for Integrating Clinical and Genomic Data.
Until the last decade, traditional clinical care and management of complex diseases mainly
relied on different clinico-pathological data, such as signs and symptoms, demographic …
relied on different clinico-pathological data, such as signs and symptoms, demographic …
An architecture for integrating genetic and clinical data
Personalized medicine is the new horizon of the medical science. Its main goal is to improve
the quality of patient care, both in prevention and in therapeutic stage, and to improve the …
the quality of patient care, both in prevention and in therapeutic stage, and to improve the …