9,034 research outputs found
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Extraction from raw text to a knowledge base of entities and fine-grained
types is often cast as prediction into a flat set of entity and type labels,
neglecting the rich hierarchies over types and entities contained in curated
ontologies. Previous attempts to incorporate hierarchical structure have
yielded little benefit and are restricted to shallow ontologies. This paper
presents new methods using real and complex bilinear mappings for integrating
hierarchical information, yielding substantial improvement over flat
predictions in entity linking and fine-grained entity typing, and achieving new
state-of-the-art results for end-to-end models on the benchmark FIGER dataset.
We also present two new human-annotated datasets containing wide and deep
hierarchies which we will release to the community to encourage further
research in this direction: MedMentions, a collection of PubMed abstracts in
which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet,
which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k
entity types. In experiments on all three datasets we show substantial gains
from hierarchy-aware training.Comment: ACL 201
Cluster randomized test-negative design (CR-TND) trials: a novel and efficient method to assess the efficacy of community level dengue interventions
Cluster randomized trials are the gold standard for assessing efficacy of community-level interventions, such as vector control strategies against dengue. We describe a novel cluster randomized trial methodology with a test-negative design, which offers advantages over traditional approaches. It utilizes outcome-based sampling of patients presenting with a syndrome consistent with the disease of interest, who are subsequently classified as test-positive cases or test-negative controls on the basis of diagnostic testing. We use simulations of a cluster trial to demonstrate validity of efficacy estimates under the test-negative approach. This demonstrates that, provided study arms are balanced for both test-negative and test-positive illness at baseline and that other test-negative design assumptions are met, the efficacy estimates closely match true efficacy. We also briefly discuss analytical considerations for an odds ratio-based effect estimate arising from clustered data, and outline potential approaches to analysis. We conclude that application of the test-negative design to certain cluster randomized trials could increase their efficiency and ease of implementation
Cluster-Randomized Test-Negative Design Trials: A Novel and Efficient Method to Assess the Efficacy of Community-Level Dengue Interventions.
Cluster-randomized controlled trials are the gold standard for assessing efficacy of community-level interventions, such as vector-control strategies against dengue. We describe a novel cluster-randomized trial methodology with a test-negative design (CR-TND), which offers advantages over traditional approaches. This method uses outcome-based sampling of patients presenting with a syndrome consistent with the disease of interest, who are subsequently classified as test-positive cases or test-negative controls on the basis of diagnostic testing. We used simulations of a cluster trial to demonstrate validity of efficacy estimates under the test-negative approach. We demonstrated that, provided study arms are balanced for both test-negative and test-positive illness at baseline and that other test-negative design assumptions are met, the efficacy estimates closely match true efficacy. Analytical considerations for an odds ratio-based effect estimate arising from clustered data and potential approaches to analysis are also discussed briefly. We concluded that application of the test-negative design to certain cluster-randomized trials could increase their efficiency and ease of implementation
Interactive Machine Learning with Applications in Health Informatics
Recent years have witnessed unprecedented growth of health data, including millions of biomedical research publications, electronic health records, patient discussions on health forums and social media, fitness tracker trajectories, and genome sequences. Information retrieval and machine learning techniques are powerful tools to unlock invaluable knowledge in these data, yet they need to be guided by human experts. Unlike training machine learning models in other domains, labeling and analyzing health data requires highly specialized expertise, and the time of medical experts is extremely limited. How can we mine big health data with little expert effort? In this dissertation, I develop state-of-the-art interactive machine learning algorithms that bring together human intelligence and machine intelligence in health data mining tasks. By making efficient use of human expert's domain knowledge, we can achieve high-quality solutions with minimal manual effort.
I first introduce a high-recall information retrieval framework that helps human users efficiently harvest not just one but as many relevant documents as possible from a searchable corpus. This is a common need in professional search scenarios such as medical search and literature review. Then I develop two interactive machine learning algorithms that leverage human expert's domain knowledge to combat the curse of "cold start" in active learning, with applications in clinical natural language processing. A consistent empirical observation is that the overall learning process can be reliably accelerated by a knowledge-driven "warm start", followed by machine-initiated active learning. As a theoretical contribution, I propose a general framework for interactive machine learning. Under this framework, a unified optimization objective explains many existing algorithms used in practice, and inspires the design of new algorithms.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147518/1/raywang_1.pd
ABiMed: An intelligent and visual clinical decision support system for medication reviews and polypharmacy management
Background: Polypharmacy, i.e. taking five drugs or more, is both a public
health and an economic issue. Medication reviews are structured interviews of
the patient by the community pharmacist, aiming at optimizing the drug
treatment and deprescribing useless, redundant or dangerous drugs. However,
they remain difficult to perform and time-consuming. Several clinical decision
support systems were developed for helping clinicians to manage polypharmacy.
However, most were limited to the implementation of clinical practice
guidelines. In this work, our objective is to design an innovative clinical
decision support system for medication reviews and polypharmacy management,
named ABiMed.
Methods: ABiMed associates several approaches: guidelines implementation, but
the automatic extraction of patient data from the GP's electronic health record
and its transfer to the pharmacist, and the visual presentation of
contextualized drug knowledge using visual analytics. We performed an ergonomic
assessment and qualitative evaluations involving pharmacists and GPs during
focus groups and workshops.
Results: We describe the proposed architecture, which allows a collaborative
multi-user usage. We present the various screens of ABiMed for entering or
verifying patient data, for accessing drug knowledge (posology, adverse
effects, interactions), for viewing STOPP/START rules and for suggesting
modification to the treatment. Qualitative evaluations showed that health
professionals were highly interested by our approach, associating the automatic
guidelines execution with the visual presentation of drug knowledge.
Conclusions: The association of guidelines implementation with visual
presentation of knowledge is a promising approach for managing polypharmacy.
Future works will focus on the improvement and the evaluation of ABiMed.Comment: 10 pages, 7 figure
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
Increasingly large electronic health records (EHRs) provide an opportunity to
algorithmically learn medical knowledge. In one prominent example, a causal
health knowledge graph could learn relationships between diseases and symptoms
and then serve as a diagnostic tool to be refined with additional clinical
input. Prior research has demonstrated the ability to construct such a graph
from over 270,000 emergency department patient visits. In this work, we
describe methods to evaluate a health knowledge graph for robustness. Moving
beyond precision and recall, we analyze for which diseases and for which
patients the graph is most accurate. We identify sample size and unmeasured
confounders as major sources of error in the health knowledge graph. We
introduce a method to leverage non-linear functions in building the causal
graph to better understand existing model assumptions. Finally, to assess model
generalizability, we extend to a larger set of complete patient visits within a
hospital system. We conclude with a discussion on how to robustly extract
medical knowledge from EHRs.Comment: 12 pages, presented at PSB 202
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