7 research outputs found
Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach
Cancer is one of the leading cause of death, worldwide. Many believe that
genomic data will enable us to better predict the survival time of these
patients, which will lead to better, more personalized treatment options and
patient care. As standard survival prediction models have a hard time coping
with the high-dimensionality of such gene expression (GE) data, many projects
use some dimensionality reduction techniques to overcome this hurdle. We
introduce a novel methodology, inspired by topic modeling from the natural
language domain, to derive expressive features from the high-dimensional GE
data. There, a document is represented as a mixture over a relatively small
number of topics, where each topic corresponds to a distribution over the
words; here, to accommodate the heterogeneity of a patient's cancer, we
represent each patient (~document) as a mixture over cancer-topics, where each
cancer-topic is a mixture over GE values (~words). This required some
extensions to the standard LDA model eg: to accommodate the "real-valued"
expression values - leading to our novel "discretized" Latent Dirichlet
Allocation (dLDA) procedure. We initially focus on the METABRIC dataset, which
describes breast cancer patients using the r=49,576 GE values, from
microarrays. Our results show that our approach provides survival estimates
that are more accurate than standard models, in terms of the standard
Concordance measure. We then validate this approach by running it on the
Pan-kidney (KIPAN) dataset, over r=15,529 GE values - here using the mRNAseq
modality - and find that it again achieves excellent results. In both cases, we
also show that the resulting model is calibrated, using the recent
"D-calibrated" measure. These successes, in two different cancer types and
expression modalities, demonstrates the generality, and the effectiveness, of
this approach
Predicting adverse outcomes following catheter ablation treatment for atrial fibrillation
Objective: To develop prognostic survival models for predicting adverse
outcomes after catheter ablation treatment for non-valvular atrial fibrillation
(AF).
Methods: We used a linked dataset including hospital administrative data,
prescription medicine claims, emergency department presentations, and death
registrations of patients in New South Wales, Australia. The cohort included
patients who received catheter ablation for AF. Traditional and deep survival
models were trained to predict major bleeding events and a composite of heart
failure, stroke, cardiac arrest, and death.
Results: Out of a total of 3285 patients in the cohort, 177 (5.3%)
experienced the composite outcomeheart failure, stroke, cardiac arrest,
deathand 167 (5.1%) experienced major bleeding events after catheter ablation
treatment. Models predicting the composite outcome had high risk discrimination
accuracy, with the best model having a concordance index > 0.79 at the
evaluated time horizons. Models for predicting major bleeding events had poor
risk discrimination performance, with all models having a concordance index <
0.66. The most impactful features for the models predicting higher risk were
comorbidities indicative of poor health, older age, and therapies commonly used
in sicker patients to treat heart failure and AF.
Conclusions: Diagnosis and medication history did not contain sufficient
information for precise risk prediction of experiencing major bleeding events.
The models for predicting the composite outcome have the potential to enable
clinicians to identify and manage high-risk patients following catheter
ablation proactively. Future research is needed to validate the usefulness of
these models in clinical practice.Comment: Under journal revie
No Excess Mortality up to 10 Years in Early Stages of Breast Cancer in Women Adherent to Oral Endocrine Therapy: A Probabilistic Graphical Modeling Approach
Breast cancer (BC) is globally the most frequent cancer in women. Adherence to endocrine therapy (ET) in hormone-receptor-positive BC patients is active and voluntary for the first five years after diagnosis. This study examines the impact of adherence to ET on 10-year excess mortality (EM) in patients diagnosed with Stages I to III BC (N = 2297). Since sample size is an issue for estimating age- and stage-specific survival indicators, we developed a method, ComSynSurData, for generating a large synthetic dataset (SynD) through probabilistic graphical modeling of the original cohort. We derived population-based survival indicators using a Bayesian relative survival model fitted to the SynD. Our modeling showed that hormone-receptor-positive BC patients diagnosed beyond 49 years of age at Stage I or beyond 59 years at Stage II do not have 10-year EM if they follow the prescribed ET regimen. This result calls for developing interventions to promote adherence to ET in patients with hormone receptor-positive BC and in turn improving cancer survival. The presented methodology here demonstrates the potential use of probabilistic graphical modeling for generating reliable synthetic datasets for validating population-based survival indicators when sample size is an issue