2 research outputs found
Computationally Efficient Labeling of Cancer Related Forum Posts by Non-Clinical Text Information Retrieval
An abundance of information about cancer exists online, but categorizing and
extracting useful information from it is difficult. Almost all research within
healthcare data processing is concerned with formal clinical data, but there is
valuable information in non-clinical data too. The present study combines
methods within distributed computing, text retrieval, clustering, and
classification into a coherent and computationally efficient system, that can
clarify cancer patient trajectories based on non-clinical and freely available
information. We produce a fully-functional prototype that can retrieve, cluster
and present information about cancer trajectories from non-clinical forum
posts. We evaluate three clustering algorithms (MR-DBSCAN, DBSCAN, and HDBSCAN)
and compare them in terms of Adjusted Rand Index and total run time as a
function of the number of posts retrieved and the neighborhood radius.
Clustering results show that neighborhood radius has the most significant
impact on clustering performance. For small values, the data set is split
accordingly, but high values produce a large number of possible partitions and
searching for the best partition is hereby time-consuming. With a proper
estimated radius, MR-DBSCAN can cluster 50000 forum posts in 46.1 seconds,
compared to DBSCAN (143.4) and HDBSCAN (282.3). We conduct an interview with
the Danish Cancer Society and present our software prototype. The organization
sees a potential in software that can democratize online information about
cancer and foresee that such systems will be required in the future
Predicting Survival Time of Ball Bearings in the Presence of Censoring
Ball bearings find widespread use in various manufacturing and mechanical
domains, and methods based on machine learning have been widely adopted in the
field to monitor wear and spot defects before they lead to failures. Few
studies, however, have addressed the problem of censored data, in which failure
is not observed. In this paper, we propose a novel approach to predict the time
to failure in ball bearings using survival analysis. First, we analyze bearing
data in the frequency domain and annotate when a bearing fails by comparing the
Kullback-Leibler divergence and the standard deviation between its break-in
frequency bins and its break-out frequency bins. Second, we train several
survival models to estimate the time to failure based on the annotated data and
covariates extracted from the time domain, such as skewness, kurtosis and
entropy. The models give a probabilistic prediction of risk over time and allow
us to compare the survival function between groups of bearings. We demonstrate
our approach on the XJTU and PRONOSTIA datasets. On XJTU, the best result is a
0.70 concordance-index and 0.21 integrated Brier score. On PRONOSTIA, the best
is a 0.76 concordance-index and 0.19 integrated Brier score. Our work motivates
further work on incorporating censored data in models for predictive
maintenance.Comment: Accepted at AAAI Fall Symposium 2023 on Survival Predictio