Patient outcome prediction using knowledge graph representation learning
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2021
Författare
Fazlinovic, Adnan
Modi, Trilokinath
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The project focuses on using knowledge graphs in a healthcare setting, classifying
patient re-admissions. Knowledge graphs are a type of heterogeneous network
consisting of entities and relations. Knowledge graph embedding method aims to
generate lower-dimensional latent vector representation of these entities and relations
while preserving their relational properties.
The data consists of patient admission details along with their underlying diagnoses,
prescriptions consumed and procedures performed. To exploit the true nature
of knowledge graphs, more information to the patient graph is added by combining
various biomedical databases to obtain a richer set of relationships.
Cleaning patient records and converting the information in more standardized form,
as well as gathering information and create a knowledge graph structure in the form
of triplets are conducted. The generation of latent vector representations of the entities
and relations are done with various embedding methods, where the final phase
is to classify patient re-admissions.
The methods investigated achieves to represent entities and relations in latent vector
form when evaluating the embeddings based on the proposed loss functions.
However, the embeddings generated doesn’t supply enough information that can
accurately predict the patient readmission status in an extended down-stream fashion.
The potential problems could be either of not enough features that explains the
variability, not enough rich information regarding the different data sources used,
or the effect of class imbalance. A stratified test subset was created from the same
excerpt of training data to quantify the results.
Beskrivning
Ämne/nyckelord
healthcare, EHR, biomedical ontologies, representation learning, knowledge graphs, embeddings.