3 research outputs found
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk
ICU mortality risk prediction is a tough yet important task. On one hand, due
to the complex temporal data collected, it is difficult to identify the
effective features and interpret them easily; on the other hand, good
prediction can help clinicians take timely actions to prevent the mortality.
These correspond to the interpretability and accuracy problems. Most existing
methods lack of the interpretability, but recently Subgraph Augmented
Nonnegative Matrix Factorization (SANMF) has been successfully applied to time
series data to provide a path to interpret the features well. Therefore, we
adopted this approach as the backbone to analyze the patient data. One
limitation of the raw SANMF method is its poor prediction ability due to its
unsupervised nature. To deal with this problem, we proposed a supervised SANMF
algorithm by integrating the logistic regression loss function into the NMF
framework and solved it with an alternating optimization procedure. We used the
simulation data to verify the effectiveness of this method, and then we applied
it to ICU mortality risk prediction and demonstrated its superiority over other
conventional supervised NMF methods.Comment: 7 Pages, 2 figure
Low-Rank Reorganization via Proportional Hazards Non-negative Matrix Factorization Unveils Survival Associated Gene Clusters
One of the central goals in precision health is the understanding and
interpretation of high-dimensional biological data to identify genes and
markers associated with disease initiation, development, and outcomes. Though
significant effort has been committed to harness gene expression data for
multiple analyses while accounting for time-to-event modeling by including
survival times, many traditional analyses have focused separately on
non-negative matrix factorization (NMF) of the gene expression data matrix and
survival regression with Cox proportional hazards model. In this work, Cox
proportional hazards regression is integrated with NMF by imposing survival
constraints. This is accomplished by jointly optimizing the Frobenius norm and
partial log likelihood for events such as death or relapse. Simulation results
on synthetic data demonstrated the superiority of the proposed method, when
compared to other algorithms, in finding survival associated gene clusters. In
addition, using human cancer gene expression data, the proposed technique can
unravel critical clusters of cancer genes. The discovered gene clusters reflect
rich biological implications and can help identify survival-related biomarkers.
Towards the goal of precision health and cancer treatments, the proposed
algorithm can help understand and interpret high-dimensional heterogeneous
genomics data with accurate identification of survival-associated gene
clusters
Patient Similarity Analysis with Longitudinal Health Data
Healthcare professionals have long envisioned using the enormous processing
powers of computers to discover new facts and medical knowledge locked inside
electronic health records. These vast medical archives contain time-resolved
information about medical visits, tests and procedures, as well as outcomes,
which together form individual patient journeys. By assessing the similarities
among these journeys, it is possible to uncover clusters of common disease
trajectories with shared health outcomes. The assignment of patient journeys to
specific clusters may in turn serve as the basis for personalized outcome
prediction and treatment selection. This procedure is a non-trivial
computational problem, as it requires the comparison of patient data with
multi-dimensional and multi-modal features that are captured at different times
and resolutions. In this review, we provide a comprehensive overview of the
tools and methods that are used in patient similarity analysis with
longitudinal data and discuss its potential for improving clinical decision
making