79,092 research outputs found
Large-Scale Online Semantic Indexing of Biomedical Articles via an Ensemble of Multi-Label Classification Models
Background: In this paper we present the approaches and methods employed in
order to deal with a large scale multi-label semantic indexing task of
biomedical papers. This work was mainly implemented within the context of the
BioASQ challenge of 2014. Methods: The main contribution of this work is a
multi-label ensemble method that incorporates a McNemar statistical
significance test in order to validate the combination of the constituent
machine learning algorithms. Some secondary contributions include a study on
the temporal aspects of the BioASQ corpus (observations apply also to the
BioASQ's super-set, the PubMed articles collection) and the proper adaptation
of the algorithms used to deal with this challenging classification task.
Results: The ensemble method we developed is compared to other approaches in
experimental scenarios with subsets of the BioASQ corpus giving positive
results. During the BioASQ 2014 challenge we obtained the first place during
the first batch and the third in the two following batches. Our success in the
BioASQ challenge proved that a fully automated machine-learning approach, which
does not implement any heuristics and rule-based approaches, can be highly
competitive and outperform other approaches in similar challenging contexts
A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings
People with Parkinson's disease must be regularly monitored by their
physician to observe how the disease is progressing and potentially adjust
treatment plans to mitigate the symptoms. Monitoring the progression of the
disease through a voice recording captured by the patient at their own home can
make the process faster and less stressful. Using a dataset of voice recordings
of 42 people with early-stage Parkinson's disease over a time span of 6 months,
we applied multiple machine learning techniques to find a correlation between
the voice recording and the patient's motor UPDRS score. We approached this
problem using a multitude of both regression and classification techniques.
Much of this paper is dedicated to mapping the voice data to motor UPDRS scores
using regression techniques in order to obtain a more precise value for unknown
instances. Through this comparative study of variant machine learning methods,
we realized some old machine learning methods like trees outperform cutting
edge deep learning models on numerous tabular datasets.Comment: Accepted at "HIMS'20 - The 6th Int'l Conf on Health Informatics and
Medical Systems"; https://americancse.org/events/csce2020/conferences/hims2
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
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