Multi-layer Representation Learning for Medical Concepts (1602.05568v1)

Abstract: Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification. Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits will have broad applications in healthcare analytics. However, in Electronic Health Records (EHR) the visit sequences of patients include multiple concepts (diagnosis, procedure, and medication codes) per visit. This structure provides two types of relational information, namely sequential order of visits and co-occurrence of the codes within each visit. In this work, we propose Med2Vec, which not only learns distributed representations for both medical codes and visits from a large EHR dataset with over 3 million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts. In the experiments, Med2Vec displays significant improvement in key medical applications compared to popular baselines such as Skip-gram, GloVe and stacked autoencoder, while providing clinically meaningful interpretation.

Multi-layer Representation Learning for Medical Concepts

The paper presents Med2Vec, a neural network-based model aimed at learning efficient representations of medical concepts from Electronic Health Records (EHR). The paper highlights the necessity of effective representation learning in healthcare, paralleling its critical role in fields like natural language processing and image recognition. Med2Vec is designed to handle the complexity of EHR data, which includes heterogeneous data types such as diagnosis, medication, and procedure codes, each with inherent latent relationships and co-occurrence patterns.

Methodology

Med2Vec employs a multi-layer perceptron (MLP) architecture to learn two distinct vector representations:

1. Code-Level Representation: Med2Vec leverages intra-visit co-occurrence data, akin to word embedding techniques like Skip-gram, to form non-negative, interpretable vectors for individual medical codes.
2. Visit-Level Representation: It further utilizes the sequential nature of patient visits to construct visit-level representations, capturing temporal patterns within patient records.

The authors emphasize the interpretability of the learned vectors, a crucial requirement in the clinical domain, by ensuring the vectors are non-negative and align with clinical concepts discernible to medical professionals.

Results and Evaluation

The model is evaluated on its ability to predict medical codes and clinical severity levels. Med2Vec outperforms several baselines, including Skip-gram, GloVe, and autoencoders, in both predictive accuracy and interpretability, as validated by clinical experts. Notably, Med2Vec demonstrates robustness across hyperparameter variations, maintaining performance consistency, an essential feature for handling diverse healthcare datasets.

Theoretical and Practical Implications

The implications of Med2Vec extend beyond immediate predictive accuracy improvements. The ability to derive interpretable and clinically meaningful representations from raw, high-dimensional EHR data underscores its potential for enhancing clinical decision support systems. Moreover, the paper highlights Med2Vec’s scalability, crucial for real-world deployment in large healthcare settings. By enabling the extraction of succinct patterns from complex datasets, Med2Vec paves the way for improved patient stratification, personalized treatment recommendations, and overall healthcare delivery efficiency.

Future Directions

Future research could explore integration with more advanced architectures like transformers to capture even deeper contextual dependencies. Furthermore, expanding Med2Vec’s applicability to multi-modal datasets, incorporating images and text along with structured EHR data, could substantially broaden its utility. There is also potential in automating the interpretability assessment to accelerate clinical validation processes.

In conclusion, Med2Vec presents a substantial leap in the domain of healthcare representation learning, offering a scalable, interpretable solution poised to impact both theoretical research and practical applications in healthcare analytics.