Predicting patient outcome from clinical journals and biomedical articles: Using the MIMIC-IV database, multiple in-hospital mortality prediction models are created, to which improvements are attempted through the use of word embeddings trained on scientific biomedical literature
Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
Henriksson, Fannie
Svensson, Philip
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
There exists an immense amount of data within the medical and healthcare field.
Two examples of these are the patient’s clinical records and biomedical research
literature. The clinical records often contain numerical information suitable for developing
AI-driven systems to support medical workers, and with some work, we
believe that data from the biomedical literature can also be used to improve these
systems.
In this work, clinical records from ICU patients in the MIMIC-IV database are
used to create multiple prediction models to predict the risk of a subject passing
away during their hospital stay (in-hospital mortality prediction). From the clinical
records, abnormal observations are categorised into medical terms which are then
used to find relevant biomedical research work from the PubMed database. This
data is used to train word embeddings using Word2Vec. The word vectors for these
categorised observations are then added as a concatenation to the clinical records
data, in hopes of improving the predictions.
The results show very little difference with the additional information from the word
embeddings. These are small improvements but unfortunately, we can not conclude
that this can help improve such prediction models. We do however believe it suggest
that there is valuable information that can be extracted from biomedical articles and
that further work could show this.
Beskrivning
Ämne/nyckelord
AI , ML , NLP , healthcare , data engineering , Word2Vec , MIMIC-IV