Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

Provenance-Centered Dataset of Drug-Drug Interactions

Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank), etc. While each one of these approaches is properly statistically validated, they do not take into consideration the overlap between them as one of their decision making variables. In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide researchers a resource for leveraging the work of others into their prediction methods. As one of the main issues to overcome the usage of external resources is their mappings between drug names and identifiers used, we also provide the set of mappings we curated to be able to compare the multiple sources we aggregate in our dataset.Comment: In Proceedings of the 14th International Semantic Web Conference (ISWC) 201

Detecting Adverse Drug Events Using a Deep neural network Model

Adverse drug events represent a key challenge in public health, especially with respect to drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to this problem have used different types of drug-related information utilizing different types of machine learning algorithms to predict potential interactions between drugs. In this work, our focus is on the use of genetic information about the drugs, in particular, the protein sequence and protein structure of drug protein targets to predict potential interactions between drugs. We collected information on drug-drug interactions (DDIs) from the DrugBank database and divided them into multiple datasets based on the type of information, such as, chemical structure, protein targets, side effects, pathways, protein-protein interactions, protein structure, information about indications. We proposed a similarity-based Neural Network framework called protein sequence-structure similarity network (S3N), and used this to predict the novel DDI’s. The drug-drug similarities are computed using different categories of drug information based on multiple similarity metrics. We compare the results with those from the state-of-the art methods on this problem. Our results show that proposed method is quite competitive, at times outperforming the state-of-the-art. Our performance evaluations on different datasets showed the predictive performance as follows: Precision 91\%-98\%, Recall 90\%-96\%, F1 Score 86\%-95\%, AUC 88\%-99\% Accuracy 86\%-95\%. To further investigate the reliability of the proposed method, we utilize 158 drugs related to cardiovascular disease to evaluate the performance of our model and find out the new interactions among the drugs. Our model showed 90\% accuracy of detecting the existing drug interactions and identified 60 new DDI’s for the cardiovascular drugs. Our evaluation demonstrates the effectiveness of S3N in predicting DDI’s

Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988