Biology subjects

Wiley, L. K.

Publications and source records attributed to Wiley, L. K..

3 recordsLinked to original sources

bioRxiv2020

Developing and Deploying a Scalable Computing Platform to Support MOOC Education in Clinical Data Science

One of the challenges of teaching applied data science courses is managing individual students local computing environment. This is especially challenging when teaching massively open online courses (MOOCs) where students come from across the globe and have a variety of access to and types of computing systems. There are additional challenges with using sensitive health information for clinical data science education. Here we describe the development and performance of a computing platform developed to support a series of MOOCs in clinical data science. This platform was designed to restrict and log all access to health datasets while also being scalable, accessible, secure, privacy preserving, and easy to access. Over the 19 months the platform has been live it has supported the computation of more than 2300 students from 101 countries.

scientific communication and education↗

bioRxiv2020

Assessing the Utility and Accuracy of ICD10-CM Nontraumatic Subarachnoid Hemorrhage Codes for Intracranial Aneurysm Research

BackgroundThe 10th revision of International Classification of Disease, Clinical Modification (ICD10-CM) increased the number of codes to identify nontraumatic subarachnoid hemorrhage from one to twenty-two. ICD10-CM codes are able to specify the location of aneurysms causing subarachnoid hemorrhage (aSAH), however it is not clear how frequently or accurately these codes are being used in practice. ObjectiveTo systematically evaluate the usage and accuracy of location-specific ICD10-CM codes for aSAH. MethodsWe extracted all uses of ICD10-CM codes for nontraumatic subarachnoid hemorrhage (I60.x) during the first three years following the implementation of ICD10-CM from the billing module of the EHR for UCHealth. For those codes that specified aSAH location (I60.0-I60.6), EHR documentation was reviewed to determine whether there was an active aSAH, any patient history of aSAH, or unruptured intracranial aneurysm/s and the locations of those outcomes. ResultsBetween October 1, 2015 - September 30, 2018, there were 3,119 instances of nontraumatic subarachnoid hemorrhage ICD10-CM codes (I60.00-I60.9), of which 297 (9.5%) code instances identified aSAH location (I60.0-I60.6). These codes accurately identified current aSAH (64%), any patient history of aSAH (84%), and any patient history of intracranial aneurysm (87%). The accuracy of identified outcome location was 53% in current aSAH, 72% for any history of aSAH, and 76% for any history of an intracranial artery. ConclusionsResearchers should use ICD10-CM codes with caution when attempting to detect active aSAH and/or aneurysm location.

bioinformatics↗

bioRxiv2017

Opportunities And Obstacles For Deep Learning In Biology And Medicine

Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We examine applications of deep learning to a variety of biomedical problems--patient classification, fundamental biological processes, and treatment of patients--and discuss whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges. We find that deep learning has yet to revolutionize or definitively resolve any of these problems, but promising advances have been made on the prior state of the art. Even when improvement over a previous baseline has been modest, we have seen signs that deep learning methods may speed or aid human investigation. More work is needed to address concerns related to interpretability and how to best model each problem. Furthermore, the limited amount of labeled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning powering changes at both bench and bedside with the potential to transform several areas of biology and medicine.

bioinformatics↗