5,175 research outputs found
Knowledge-based Biomedical Data Science 2019
Knowledge-based biomedical data science (KBDS) involves the design and
implementation of computer systems that act as if they knew about biomedicine.
Such systems depend on formally represented knowledge in computer systems,
often in the form of knowledge graphs. Here we survey the progress in the last
year in systems that use formally represented knowledge to address data science
problems in both clinical and biological domains, as well as on approaches for
creating knowledge graphs. Major themes include the relationships between
knowledge graphs and machine learning, the use of natural language processing,
and the expansion of knowledge-based approaches to novel domains, such as
Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages
with 3 table
Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in
healthcare that focuses on individualized patient care by mapping the patient's
health information in a holistic and multi-dimensional way. PCKGs integrate
various types of health data to provide healthcare professionals with a
comprehensive understanding of a patient's health, enabling more personalized
and effective care. This literature review explores the methodologies,
challenges, and opportunities associated with PCKGs, focusing on their role in
integrating disparate healthcare data and enhancing patient care through a
unified health perspective. In addition, this review also discusses the
complexities of PCKG development, including ontology design, data integration
techniques, knowledge extraction, and structured representation of knowledge.
It highlights advanced techniques such as reasoning, semantic search, and
inference mechanisms essential in constructing and evaluating PCKGs for
actionable healthcare insights. We further explore the practical applications
of PCKGs in personalized medicine, emphasizing their significance in improving
disease prediction and formulating effective treatment plans. Overall, this
review provides a foundational perspective on the current state-of-the-art and
best practices of PCKGs, guiding future research and applications in this
dynamic field
Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models
Contextual Relation Extraction (CRE) is mainly used for constructing a
knowledge graph with a help of ontology. It performs various tasks such as
semantic search, query answering, and textual entailment. Relation extraction
identifies the entities from raw texts and the relations among them. An
efficient and accurate CRE system is essential for creating domain knowledge in
the biomedical industry. Existing Machine Learning and Natural Language
Processing (NLP) techniques are not suitable to predict complex relations from
sentences that consist of more than two relations and unspecified entities
efficiently. In this work, deep learning techniques have been used to identify
the appropriate semantic relation based on the context from multiple sentences.
Even though various machine learning models have been used for relation
extraction, they provide better results only for binary relations, i.e.,
relations occurred exactly between the two entities in a sentence. Machine
learning models are not suited for complex sentences that consist of the words
that have various meanings. To address these issues, hybrid deep learning
models have been used to extract the relations from complex sentence
effectively. This paper explores the analysis of various deep learning models
that are used for relation extraction.Comment: This Paper Presented in the International Conference on FOSS
Approaches towards Computational Intelligence and Language TTechnolog on
February 2023, Thiruvananthapura
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