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memr: An R package for medical free-text records embedding and visualization |
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31 January 2020 |
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paper.bib |
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The memr package in R allows for creating embeddings, i.e. vector
representations, of medical free-text records written by doctors. It also
provides a wide spectrum of tools to data visualization and medical
visits' segmentation. These tools aim to develop computer-supported medicine by
facilitating medical data analysis and iterpretation. The package can be exploited for
many applications like the recommendation prediction, patients' clustering etc. that
can aid doctors in their practice.
Processing of free-text clinical records plays an important role in computer-supported medicine. A detailed description of symptoms, examination and an interview is often stored in an unstructured way as free-text, hard to process but rich in important information.
The most straightforward approach to the processing of clinical notes could be their clustering with respect to different features like diagnosis or type of treatment. Grouping of visits can fulfil many potential goals. If we are able to group visits into clusters based on interview with a patient and medical examination then (1) we can follow recommendations that were suggested to patients with similar history to create a list of possible diagnoses, (2) reveal that the current diagnosis is unusual, (3) identify subsets of visits with the same diagnosis but different symptoms.
The basis of memr is creating of medical terms embeddings based on GloVe algorithm [@pennington2014glove] implemented in R package text2vec by [@text2vec]. Having a vector of terms extracted from visits' descriptions merged_terms we can compute term embeddings by:
term_vectors <- embed_terms(merged_terms)
We can validate the quality of computed embeddings by performing and visualizing 'word analogy task' introduced by [@mikolov2013efficient]. The function visualize_analogies produces PCA plots (with ggplot2 package) of given pairs of terms (terms_pairs):
visualize_analogies(term_vectors, terms_pairs)
The embeddings of medical terms of interview and examination are averaged and concatenated to obtain a representation of a visit:
visits_vectors <- embed_list_visits(interviews, examinations,
interview_term_vectors,
examination_term_vectors)
If we have more information about visits, such as doctors' specialties or ICD-10 codes (visits), memr can help with
data analysis and visualization. We can perform visits clustering of specified doctor's specialty by the k-means algorithm. Then we can visualize the visits by the t-SNE algorithm [@maaten2008visualizing] with the use of Rtsne [@krijthe2015rtsne] package (on the plot each dot corresponds to one visit):
clusters <- cluster_visits(visits_vectors, visits, spec = "pediatry",
cluster_number = 6)
visualize_visit_embeddings(visits_vectors, visits, color_by = "cluster",
clusters)
Having recommendations in our data, we can show the most popular recommendations for each cluster by the function get_cluster_recommendations.
memr allows also for visualization of ICD-10 codes by averaging visits' embeddings:
visualize_icd10(visits_vectors, visits)
The package was created during the research financially supported by the Polish Centre for Research and Development (Grant POIR.01.01.01-00-0328/17). The results of the research are described in the paper [@dobrakowski2019patients].