329 research outputs found
KindMed: Knowledge-Induced Medicine Prescribing Network for Medication Recommendation
Extensive adoption of electronic health records (EHRs) offers opportunities
for its use in various clinical analyses. We could acquire more comprehensive
insights by enriching an EHR cohort with external knowledge (e.g., standardized
medical ontology and wealthy semantics curated on the web) as it divulges a
spectrum of informative relations between observed medical codes. This paper
proposes a novel Knowledge-Induced Medicine Prescribing Network (KindMed)
framework to recommend medicines by inducing knowledge from myriad
medical-related external sources upon the EHR cohort, rendering them as medical
knowledge graphs (KGs). On top of relation-aware graph representation learning
to unravel an adequate embedding of such KGs, we leverage hierarchical sequence
learning to discover and fuse clinical and medicine temporal dynamics across
patients' historical admissions for encouraging personalized recommendations.
In predicting safe, precise, and personalized medicines, we devise an attentive
prescribing that accounts for and associates three essential aspects, i.e., a
summary of joint historical medical records, clinical condition progression,
and the current clinical state of patients. We exhibited the effectiveness of
our KindMed on the augmented real-world EHR cohorts, etching leading
performances against graph-driven competing baselines
StratMed: Relevance Stratification for Low-resource Medication Recommendation
With the growing imbalance between limited medical resources and escalating
demands, AI-based clinical tasks have become paramount. Medication
recommendation, as a sub-domain, aims to amalgamate longitudinal patient
history with medical knowledge, assisting physicians in prescribing safer and
more accurate medication combinations. Existing methods overlook the inherent
long-tail distribution in medical data, lacking balanced representation between
head and tail data, which leads to sub-optimal model performance. To address
this challenge, we introduce StratMed, a model that incorporates an innovative
relevance stratification mechanism. It harmonizes discrepancies in data
long-tail distribution and strikes a balance between the safety and accuracy of
medication combinations. Specifically, we first construct a pre-training method
using deep learning networks to obtain entity representation. After that, we
design a pyramid-like data stratification method to obtain more generalized
entity relationships by reinforcing the features of unpopular entities. Based
on this relationship, we designed two graph structures to express medication
precision and safety at the same level to obtain visit representations.
Finally, the patient's historical clinical information is fitted to generate
medication combinations for the current health condition. Experiments on the
MIMIC-III dataset demonstrate that our method has outperformed current
state-of-the-art methods in four evaluation metrics (including safety and
accuracy)
Social Search: retrieving information in Online Social Platforms -- A Survey
Social Search research deals with studying methodologies exploiting social
information to better satisfy user information needs in Online Social Media
while simplifying the search effort and consequently reducing the time spent
and the computational resources utilized. Starting from previous studies, in
this work, we analyze the current state of the art of the Social Search area,
proposing a new taxonomy and highlighting current limitations and open research
directions. We divide the Social Search area into three subcategories, where
the social aspect plays a pivotal role: Social Question&Answering, Social
Content Search, and Social Collaborative Search. For each subcategory, we
present the key concepts and selected representative approaches in the
literature in greater detail. We found that, up to now, a large body of studies
model users' preferences and their relations by simply combining social
features made available by social platforms. It paves the way for significant
research to exploit more structured information about users' social profiles
and behaviors (as they can be inferred from data available on social platforms)
to optimize their information needs further
Report on the SIGIR 2013 Workshop on Health Search and Discovery
Abstract The workshop brought together 40 researchers and practitioners from academia and industry to discuss search and discovery in the medical domain. Presentations and discussions spanned several challenging and important topics, including directions improving the accessibility of medical and health information for lay people (with associated enhancements to result ranking algorithms and search interfaces), and methods for discovering biomedical phenomena from the information that people seek online, as evidenced in query streams and other sources such as social and news media. A thread throughout the workshop was the opportunity for new methods and applications to enhance the quality of life of people suffering from medical disorders, carry out surveillance of emerging diseases and other threats, and, more generally, to improve the health and well-being of people via tools to support their health-related information behavior
Deliverable D4.1 Specification of user profiling and contextualisation
This deliverable presents a comprehensive research of past work in the field of capturing and interpreting user preferences and context and an overview of relevant digital media-specific techniques, aiming to provide insights and ideas for innovative context-aware user preference learning and to justify the user modelling strategies considered within LinkedTV’s WP4. Based on this research and a study over the specific technical and conceptual requirements of LinkedTV, a prototypical design for profiling and contextualizing user needs in a linked media environment is specified
Congenial Web Search : A Conceptual Framework for Personalized, Collaborative, and Social Peer-to-Peer Retrieval
Traditional information retrieval methods fail to address the fact that information consumption and production are social activities. Most Web search engines do not consider the social-cultural environment of users' information needs and the collaboration between users. This dissertation addresses a new search paradigm for Web information retrieval denoted as Congenial Web Search. It emphasizes personalization, collaboration, and socialization methods in order to improve effectiveness. The client-server architecture of Web search engines only allows the consumption of information. A peer-to-peer system architecture has been developed in this research to improve information seeking. Each user is involved in an interactive process to produce meta-information. Based on a personalization strategy on each peer, the user is supported to give explicit feedback for relevant documents. His information need is expressed by a query that is stored in a Peer Search Memory. On one hand, query-document associations are incorporated in a personalized ranking method for repeated information needs. The performance is shown in a known-item retrieval setting. On the other hand, explicit feedback of each user is useful to discover collaborative information needs. A new method for a controlled grouping of query terms, links, and users was developed to maintain Virtual Knowledge Communities. The quality of this grouping represents the effectiveness of grouped terms and links. Both strategies, personalization and collaboration, tackle the problem of a missing socialization among searchers. Finally, a concept for integrated information seeking was developed. This incorporates an integrated representation to improve effectiveness of information retrieval and information filtering. An integrated information retrieval process explores a virtual search network of Peer Search Memories in order to accomplish a reputation-based ranking. In addition, the community structure is considered by an integrated information filtering process. Both concepts have been evaluated and shown to have a better performance than traditional techniques. The methods presented in this dissertation offer the potential towards more transparency, and control of Web search
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