# Medical Knowledge Extraction from Graph-Based Modeling of Electronic Health Records

Athanasios Kallipolitis, Parisis Gallos, Andreas Menychtas, Panayiotis Tsanakas, Ilias Maglogiannis

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Athanasios Kallipolitis, Parisis Gallos, Andreas Menychtas, Panayiotis Tsanakas, Ilias Maglogiannis. Medical Knowledge Extraction from Graph-Based Modeling of Electronic Health Records. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.279-290, 10.1007/978-3-031-34111-3_24. http://hal-05449808

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Medical Knowledge Extraction
from Graph-Based Modeling of Electronic
Health Records

Athanasios Kallipolitis¹(✉) ID, Parisis Gallos² ID, Andreas Menychtas¹,² ID,
Panayiotis Tsanakas³ ID, and Ilias Maglogiannis¹ ID

¹ Department of Digital Systems, University of Piraeus, Piraeus, Greece
{nasskall,imaglo}@unipi.gr, amenychtas@bioassist.gr

² BioAssist S.A, Kastritsiou 4, Rion, Greece
parisgallos@bioassist.gr

³ School of Electrical and Computer Engineering, National Technical University, Athens, Greece

panag@cs.ntua.gr

**Abstract.** The variety and dimensionality of health-related data cannot be addressed by the human perception to arrive at useful knowledge or conclusions for proposing individualized treatment, diagnosis, or prognosis for a disease. Treating this wealth of heterogeneous data in a tabular manner deprives us of the knowledge that is hidden in interactions between the different types of data. In this paper, the potentials of graph-based data modeling and management are explored. Entities such as patients, encounters, observations, and immunizations are structured as graph elements with meaningful connections and are, consequently, encoded to form graph embeddings. The graph embeddings contain information about the graph structure in the vicinity of the node. This vicinity contains multiple low-level graph embeddings that are further encoded into a single high-level vector for utilization in downstream tasks by applying higher-order statistics on Gaussian Mixture Models With reference to the Covid-19 pandemic, we make use of synthetic data for predicting the risk of a patient's fatality with a focus to prevent hospital overpopulation. Initial results demonstrate that utilizing networks of health data entities for the generation of compact medical representations has a positive impact on the performance of machine learning tasks. Since the generated Electronic Health Record vectors are label independent, they can be utilized for any classification or clustering task words.

**Keywords:** Graph Embeddings · Electronic Health Records · Fisher Vector · Heterogenous Graphs

# 1 Introduction

The transformation of health records into their electronic equivalent has given birth to numerous applications related to the management of information in databases and the production of valuable, health-related insights. These refer among others to selective
retrieval, visualization, and the use of machine learning [1] as well as deep learning tech-
niques [2, 4]. Starting from the adoption of electronic health records (EHRs) in healthcare
organizations, a huge amount of information is collected and used for administrative,
financial, and clinical purposes [3]. The variety and dimensionality of this information
cannot be addressed by human perception. Moreover, it should be taken into consider-
ation that EHRs contain a variety of interconnected medical entities that share valuable
information among them and in turn, form networks of information exchange. Storing
such entities in tabular form deprives the possibility of maximally exploiting the knowl-
edge that is hidden in the network's structure and the included interconnections. This
whole ecosystem of health-related information along with the included interconnections
is an ideal set-up for graph theory to kick in. Utilizing the existing graph knowledge in
medical data modeling can make a positive contribution in extracting usable knowledge
in forecasting, risk assessment, and population grouping on personalized or population
health issues.

Due to the Covid-19 pandemic, one of the major problems that Emergency Depart-
ments (ICUs) face in a worldwide spectrum is the phenomenon of "crowding", in the
sense that the demand for emergency medical services exceeds the available resources
for care and treatment of patients. One of the causes is the visits of patients to the ICU,
which are characterized as “inappropriate”, “non-urgent” or “potentially avoidable”. In
this paper, we explore the potential of managing and analyzing EHR that form graphs
on the use case scenario of classifying the incoming patients into two classes, “urgent”
and “non-urgent”. By creating compact representations of medical entities and their
interconnections that are stored as nodes and edges in a graph, we show that the clas-
sification task can be performed more efficiently by exploiting the structure and hidden
connections of the EHR network. Towards this end, we propose a novel method for repre-
senting health-related nodes and their corresponding relations into vectors by extending
the metapath2vec paradigm [5]. Furthermore, multiple instances of embeddings that are
related to specific entities are encoded into a single vector per entity by utilizing the
Fisher Vector technique [6] to enable their utilization for downstream tasks. The whole
methodology improves the classification results in comparison to treating the data in
their initial tabular form and is unsupervised in order to maintain a task-independent
character.

## 2 Related Work

The early steps of health data analysis focused mainly on tabular data and their pro-
cessing through simple statistical techniques or machine learning techniques limited by
handcrafted features [7]. The data are transformed into solid encodings via neural net-
works and are therefore used as tabular data for inference tasks. However, potentials are
confined by the fact that each category of information is treated as an isolated island.
To overcome these limitations, graph representations have recently been added to this
research landscape. Graphs can store health data with the advantage that they are treated
as interconnected entities. Each category of information (patients, appointments, obser-
vations, organizations, drugs) can be coded as part of the neighborhood in a way similar
to word2vec [8].
Transferring the scenario from word2vec to node2vec [9] and health-related data, we consider a patient with the accompanying information regarding his demographics and a series of laboratory tests, or the drugs administered per appointment in a health-care structure as a graph. Then, the information surrounding the patient's neighborhood (i.e., laboratory tests, vaccinations, appointments in health facilities, treatments) can be summarized in a vector representation of reduced space by mapping the neighborhood of the patient to the patient. Starting from the Word2Vec paradigm, which in the graph contextual environment can be referred to as Node2Vec, several approaches have been proposed in the literature for the representation of graphs into embeddings. A large family of these approaches depends on the Deep Graph Infomax [10] training procedure and includes Graph Convolutional Networks (GCNs) [11], Graph Attention Networks (GATs) [12], Approximate Personalized Propagation of Neural Predictions (APPNPs) [13], GraphSAGE [14], Relational Graph Convolutional Networks (RGCNs) [15] and ClusterGCN [16]. This procedure is unsupervised and is guided by the maximization of the mutual information between the whole graph and parts of the graph. It consists of five steps as depicted in the workflow in Fig. 1. The first step concerns the mutation of the original graph where the data is stored through a function C. The function C changes the position in the contents of the nodes. The second step codes the information of each node based on its neighborhood using a graph2vec algorithm (E). A summary vector of the original graph is created using the Summary R (eg global average pooling). The next step uses a neural network of discriminator type, which receives the nodes of both graphs as inputs and is trained based on a cost function that is maximized for samples of the original graph and minimized for samples of the corrupted graph.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Deep Graph Infomax workflow presentation.</figcaption></figure>
While GCN is an extension of Convolutional Neural Networks for graph components and exploits graph convolutions to predict node features from the features of the surrounding nodes, GATs utilize an attention mechanism that learns the importance of each node feature for the specific node and RGCNs generalize the GCN notion to heterogeneous graphs with multiple edge types. APPNPs emphasize the insight that is gained by GCNs concerning the nodes that do not participate in the neighborhood and utilize the relationship between GCNs and PageRank to derive an improved scheme for the node-to-node propagation procedure.

Extracting knowledge from medical data is a dynamic process that is constantly evolving as new trends in disease, treatment plans, communicating populations, and new generation drugs are discovered. During this ever-changing process, prior knowledge of a prediction algorithm requires updating to provide knowledge that corresponds to the real state of the world rather than an outdated version. This requirement for continuous updating is in line with the need for a knowledge mechanism that does not require the updating of the entire graph upon insertion of a new node. Therefore, the use of the GraphSAGE algorithm is preferred over others that require updating the embedding model in each new node integration [17]. The calculation of each node encoding is performed by a neural network that tries to predict each node from each surrounding node neighborhood. An important drawback of the mentioned approaches is that in their majority they cannot handle heterogeneous graphs.

# 3 Methodology

The proposed methodology is mainly divided into six discrete steps: Tabular Data preprocessing, Tabular Data to graph, Graph embeddings, Multiple embeddings vector, Embedding fusion, and Classification (Fig. 2).

## 3.1 Tabular Data Preprocessing

The initial inputs are simple.csv files that contain information about the patients, their encounters, the corresponding observations registered at each encounter, and their immunizations. Taking into consideration the following assumption that the data do not require cleaning, the preprocessing steps include the transformation of categorical features to one-hot encoded features, the transformation of string type variables to dates, where applicable (birthdate, deathdate), the conversion of string to numerical values (value, code) and the zero to one normalization of all numerical features. Each entity of medical data, patient, encounter, medication, and observation is represented by a vector of many values, each describing a different type of information. In Table 1 an example of the containing information for the patient and observation entity is shown. UUID (Universal unique identifier) stands for a 128-bit value to uniquely identify an instance. All the above-mentioned variables result in a vector with n values describing the patient and m values describing an observation. If the initial variables of the patient entity are k, they are extended to n > k due to the one hot encoding procedure.
## 3.2 Tabular Data to Graph

Each category of health entity (patient, encounter, observation) contains common keys, upon which the rows of the entities are connected to form meaningful graphs. Graphs are formed by joining data frames. A single patient in his EHR is connected to many encounters and immunizations. In turn, each encounter is connected to observations and medications. All relationships have equal to one weight and are considered undirected. In this context, a graph is formed that contains patients, encounters, observations, and immunizations as nodes and their relationships as edges. The tabular data are stored as attributes, internally in each node.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** General workflow architecture. The main steps of proposed methodology on the right side with the corresponding byproducts on the left.</figcaption></figure>
<table><caption>**Table 1.** Description and type of variables contained in the initial dataset.</caption><thead><tr><th>Entity</th><th>Variable</th><th>Type</th></tr></thead><tbody><tr><td rowspan="7">Patient</td><td>Id</td><td>UUID</td></tr><tr><td>Birthdate</td><td>String</td></tr><tr><td>Deathdate</td><td>String</td></tr><tr><td>Marital</td><td>String</td></tr><tr><td>Race</td><td>String</td></tr><tr><td>Ethnicity</td><td>String</td></tr><tr><td>Gender</td><td>String</td></tr><tr><td rowspan="8">Observation</td><td>Patient</td><td>UUID</td></tr><tr><td>Encounter</td><td>UUID</td></tr><tr><td>Category</td><td>String</td></tr><tr><td>Code</td><td>String</td></tr><tr><td>Description</td><td>String</td></tr><tr><td>Value</td><td>String</td></tr><tr><td>Unit</td><td>String</td></tr><tr><td>Type</td><td>String</td></tr></tbody></table>

## 3.3 Graph Embeddings

Once the graph is formed, the next step concerns the creation of meta-path random walks that can be applied in heterogeneous graphs since these walks take into consideration the different types of nodes and avoid the pitfall of bias towards highly concentrated and visible nodes. In the case of EHR, the following are examples of meta-paths: a) observation, encounter, observation, b) encounter – patient - encounter, c) observation – encounter – patient – encounter - observation, d) immunization – patient – immunization. The generated random walks are the equivalent of sentences in the word2vec paradigm and are utilized to transform the structure of the graph into the skip-gram analog. Up to this point, the steps are inspired by the implementation in [5]. However, instead of utilizing the plain skip-gram model to form node embeddings, the implementation is enhanced to utilize Doc2Vec [18] and Fasttext [19]. Doc2Vec expands the Word2Vec algorithm by the utilization of an additional input feature that represents meta-path information. On the other hand, the Fasttext embedding model exploits intra-node meaningful information to describe each node of the health-related heterogeneous graph with a single vector. Both manage to incorporate information concerning the structure and attributes of the surrounding nodes regardless of their entity type. A critical difference between the two approaches is that Fasttext requires no retraining for the updating of new nodes in contradiction to Doc2Vec and Word2Vec. Each health entity patient, observation, and encounter is encoded to a vector containing w values which is a hyperparameter of each implementation and for the use case scenario it is set to 64 by a trial-and-error process to achieve better classification results. This vector encapsulates knowledge about the graph
that is missing from the initial tabular representation and can be concatenated to the n or m-valued vector of the 3.1 subsection. Although there is redundancy in the concatenated information, experimentation has demonstrated that concatenating both vectors is beneficial.

## 3.4 Multiple Embeddings Vector

Since our classification tasks concern patients, multiple low-level observation graph embeddings need to be encoded into a high-level vector for each patient. For this purpose, the Fisher vector technique [6]. Unlike other works where the temporal information is encoded by neural network configurations such as Recurrent Neural Networks or Long Short-Term Memories, the technique is based on the utilization of Gaussian distributions for modeling the generation procedure of the graph embeddings and their encoding by higher-order statistics. The Gaussian Mixture model consists of k Gaussian distributions and their parameters are the following $\lambda = \{w_i, \mu_i, \Sigma_i, i = 1,...,k\}$, where w is the height, $\mu$ the mean and $\Sigma$ the covariance matrix of the distribution. The probability that a random descriptor $x_r$ is generated by the GMM is described by Eq. (1). Before the passing of the graph embeddings to the model, the embeddings are concatenated with the initial one-hot encoded observation vector that contains the node attributes.

$$p_r(\lambda) = \sum_{i=1}^{k} w_i N(x_i; \mu_i \Sigma_i) \qquad (1)$$

The algorithm attempts to compute the gradient of the sample's likelihood with respect to $\lambda$ parameters and results in a $k(1 + 2w)$ representation. The Fisher vector embedding technique is a far more sophisticated approach from the Bag of Words paradigm that applies hard assignment to each cluster and ignores higher order statistics such as the covariance matrix $\Sigma$. Since our data consists of entities that occur during various periods in time, each set of instances that belong to an entity can be encoded into a single vector that incorporates the information during time efficiently.

## 3.5 Embeddings Fusion

The patient vector that derives from the graph embedding process is concatenated with the tabular patient attribute vector and the Fisher observations vector to form a compact and meaningful representation.

## 3.6 Classification

To predict the fatality event, the XGBoost classifier is selected as an efficient ensemble classification scheme that bases its success on the exploitation of the gradient boosting technique in combination with decision trees. The use of an ensemble based on decision trees provides the ability to explore explainability properties of the above-mentioned classification scheme by calculating the sum of gini impurity or info gain metric.
# 4 Experimental Results

The utilization of synthetic health data is a popular choice for researchers since real health data is accompanied by specific legal restrictions, privacy, security, and intellectual property restrictions. One of the most popular datasets available due to COVID-19 is the homonymous dataset [20] available in both 10k and 100k versions. For the use case of avoiding overpopulation in COVID-19 clinics, the entity data frames are filtered to exclude any information that will favor the predictive model. Therefore, encounters and observations after the date of the Covid-19 diagnosis are removed. The ground truth for the binary classification task is provided by selecting all patients that suffered from the disease and dividing them into a positive and negative class according to the fatality event. The predicted probability can be utilized to make decisions for keeping a patient in a hospital. Since the dataset is highly imbalanced towards the negative class, samples from this class are reduced to contain only double (IMB2) or triple (IMB3) or four times (IMB4) the number of positive samples. It is split into a training and test set with proportion a of 66.6 - 33.3% and a three-fold cross-validation scheme is applied. The classification task is performed with a) the tabular patient attributes (PA), b) the Fasttext graph embeddings (FE), c) the Word2Vec (WV) graph embeddings, d) the Doc2Vec (DV) graph embeddings, e) the tabular patient attributes vector that is concatenated with the Fasttext embedding (PAFE), f) the tabular patient attributes vector that is concatenated with the Word2Vec embedding (PAWV), g), the tabular patient attributes vector that is concatenated with the Doc2Vec embedding (PADV), h) the tabular patient attributes vector that is concatenated with the Fasttext graph embedding and the Fisher vector (PAFEFV), i) the patient attributes vector that is concatenated with the Word2Vec embedding and the Fisher vector (PAWVFV), j) the patient attributes vector that is concatenated with the Doc2Vec embedding and the Fisher vector (PADVFV). The utilized metrics for classification performance include accuracy and balanced accuracy. Results for all the above-mentioned scenarios are depicted in Table 2. The review of the presented results demonstrates that the best results are achieved by the PAFEFV implementation and as the imbalance increases, the PADVFV proves better. The plain Fasttext graph embeddings are worst in predicting the classification task than the tabular data, in contrast to Doc2Vec and Word2Vec approaches that show improved results. In most cases, the Doc2Vec embedding demonstrates strong resilience to imbalance bias.

To assess the performance of a clustering technique, we apply k-means clustering in three configurations ($k = 3, 4, 5$) to the generated embeddings in the IMB4 dataset. The clustering experiment in $k = 3,4$ and 5 aims to verify the effectiveness of the proposed methodology in an unsupervised manner. The selection of the $k$ parameter has been done arbitrarily, however there exist techniques that can indicate the optimal $k$ value based on the intra/ inter cluster characteristics such as Elbow score and Silhouette index. The utilized metrics for evaluation of the clustering performance are the homogeneity, completeness, v measurement, adjusted rand index, mutual info, and silhouette index scores. The results are presented in Table 3. The best score for all clustering metrics is one. By reviewing the clustering results, it is inferred that in most cases the implementations that exploit the Doc2Vec embeddings provide better results. However, it should be noted that extending the patient vector with additional information from the Fisher vector is not always beneficial for clustering as it is for the classification task. There are some
<table><caption>**Table 2.** Binary Classification results. Accuracy is labelled as Acc, balanced accuracy as Bacc.</caption><thead><tr><th rowspan="3">Case</th><th colspan="6">Classification Results</th></tr><tr><th colspan="2">IMB2</th><th colspan="2">IMB3</th><th colspan="2">IMB4</th></tr><tr><th>Acc</th><th>Bacc</th><th>Acc</th><th>Bacc</th><th>Acc</th><th>Bacc</th></tr></thead><tbody><tr><td>PA</td><td>0.70</td><td>0.67</td><td>0.77</td><td>0.69</td><td>0.78</td><td>0.66</td></tr><tr><td>FE</td><td>0.71</td><td>0.60</td><td>0.75</td><td>0.52</td><td>0.80</td><td>0.51</td></tr><tr><td>WV</td><td>0.77</td><td>0.72</td><td>0.80</td><td>0.67</td><td>0.80</td><td>0.58</td></tr><tr><td>DV</td><td>0.84</td><td>0.84</td><td>0.80</td><td>0.72</td><td>0.80</td><td>0.62</td></tr><tr><td>PAFE</td><td>0.80</td><td>0.75</td><td>0.82</td><td>0.69</td><td>0.83</td><td>0.60</td></tr><tr><td>PAWV</td><td>0.83</td><td>0.81</td><td>0.85</td><td>0.77</td><td>0.86</td><td>0.70</td></tr><tr><td>PADV</td><td>0.86</td><td>0.86</td><td>0.87</td><td>0.81</td><td>0.88</td><td>0.76</td></tr><tr><td>PAFEFV</td><td><strong>0.95</strong></td><td><strong>0.95</strong></td><td><strong>0.96</strong></td><td><strong>0.94</strong></td><td>0.95</td><td>0.90</td></tr><tr><td>PAWVFV</td><td><strong>0.95</strong></td><td><strong>0.94</strong></td><td>0.95</td><td>0.93</td><td>0.95</td><td>0.90</td></tr><tr><td>PADVVFV</td><td>0.93</td><td>0.92</td><td>0.94</td><td>0.92</td><td><strong>0.96</strong></td><td><strong>0.93</strong></td></tr></tbody></table>

implementations with the Fisher vectors that demonstrate a significant decrease in the clustering metrics when compared to vectors with less information.

<table><caption>**Table 3.** Clustering Results.</caption><thead><tr><th rowspan="2">Case</th><th rowspan="2">k</th><th colspan="6">Clustering Results</th></tr><tr><th>Homogeneity</th><th>Completeness</th><th>V measurement</th><th>Adjusted random</th><th>Mutual info</th><th>Silhouette index</th></tr></thead><tbody><tr><td rowspan="3">PA</td><td>3</td><td>0.007</td><td>0.004</td><td>0.005</td><td>0.003</td><td>0.004</td><td>0.322</td></tr><tr><td>4</td><td>0.008</td><td>0.003</td><td>0.004</td><td>0.001</td><td>0.003</td><td>0.356</td></tr><tr><td>5</td><td>0.013</td><td>0.045</td><td>0.007</td><td>0.016</td><td>0.005</td><td>0.42</td></tr><tr><td rowspan="3">FE</td><td>3</td><td>0.003</td><td>0.001</td><td>0.002</td><td>0.010</td><td>0.001</td><td>0.041</td></tr><tr><td>4</td><td>0.003</td><td>0.001</td><td>0.002</td><td>0.004</td><td>0.001</td><td>0.039</td></tr><tr><td>5</td><td>0.003</td><td>0.013</td><td>0.019</td><td>0.004</td><td>0.001</td><td>0.043</td></tr><tr><td rowspan="3">WV</td><td>3</td><td>0.168</td><td>0.126</td><td>0.143</td><td>0.332</td><td>0.143</td><td>0.443</td></tr><tr><td>4</td><td>0.168</td><td>0.112</td><td>0.135</td><td>0.337</td><td>0.133</td><td>0.408</td></tr><tr><td>5</td><td>0.21</td><td>0.080</td><td>0.116</td><td>0.112</td><td>0.115</td><td>0.130</td></tr><tr><td rowspan="3">DV</td><td>3</td><td>0.154</td><td>0.111</td><td>0.129</td><td>0.307</td><td>0.128</td><td><strong>0.530</strong></td></tr><tr><td>4</td><td>0.159</td><td>0.103</td><td>0.126</td><td>0.323</td><td>0.125</td><td>0.534</td></tr><tr><td>5</td><td>0.172</td><td>0.106</td><td>0.131</td><td><strong>0.344</strong></td><td>0.130</td><td>0.55</td></tr></tbody></table>

(continued)
<table><caption>**Table 3.** (continued)</caption><thead><tr><th rowspan="2">Case</th><th rowspan="2">k</th><th colspan="6">Clustering Results</th></tr><tr><th>Homogeneity</th><th>Completeness</th><th>V measurement</th><th>Adjusted random</th><th>Mutual info</th><th>Silhouette index</th></tr></thead><tbody><tr><td rowspan="3">PAFE</td><td>3</td><td>0.169</td><td>0.001</td><td>0.002</td><td>0.010</td><td>0.001</td><td>0.035</td></tr><tr><td>4</td><td>0.175</td><td>0.001</td><td>0.001</td><td>0.007</td><td>0.001</td><td>0.034</td></tr><tr><td>5</td><td>0.151</td><td>0.002</td><td>0.003</td><td>0.0016</td><td>0.002</td><td>0.033</td></tr><tr><td rowspan="3">PAWV</td><td>3</td><td>0.003</td><td>0.106</td><td>0.131</td><td>0.233</td><td>0.130</td><td>0.094</td></tr><tr><td>4</td><td>0.002</td><td>0.073</td><td>0.102</td><td>0.130</td><td>0.101</td><td>0.015</td></tr><tr><td>5</td><td>0.007</td><td>0.054</td><td>0.080</td><td>0.092</td><td>0.079</td><td>0.030</td></tr><tr><td rowspan="3">PADV</td><td>3</td><td>0.152</td><td>0.110</td><td>0.128</td><td>0.307</td><td>0.127</td><td>0.447</td></tr><tr><td>4</td><td>0.160</td><td>0.084</td><td>0.11</td><td>0.231</td><td>0.109</td><td>0.128</td></tr><tr><td>5</td><td>0.173</td><td>0.070</td><td>0.1</td><td>0.128</td><td>0.099</td><td>0.052</td></tr><tr><td rowspan="3">PAFEFV</td><td>3</td><td>0.368</td><td>0.169</td><td>0.232</td><td>0.166</td><td>0.231</td><td>0.201</td></tr><tr><td>4</td><td>0.38</td><td>0.138</td><td>0.203</td><td>0.091</td><td>0.202</td><td>0.14</td></tr><tr><td>5</td><td>0.38</td><td>0.12</td><td>0.182</td><td>0.039</td><td>0.182</td><td>0.122</td></tr><tr><td rowspan="3">PAWVFV</td><td>3</td><td>0.353</td><td>0.173</td><td>0.234</td><td>0.194</td><td>0.231</td><td>0.236</td></tr><tr><td>4</td><td>0.356</td><td>0.136</td><td>0.201</td><td>0.099</td><td>0.205</td><td>0.173</td></tr><tr><td>5</td><td>0.348</td><td>0.110</td><td>0.199</td><td>0.078</td><td>0.191</td><td>0.166</td></tr><tr><td rowspan="3">PADVFV</td><td>3</td><td>0.36</td><td><strong>0.177</strong></td><td><strong>0.242</strong></td><td>0.229</td><td><strong>0.242</strong></td><td>0.248</td></tr><tr><td>4</td><td>0.37</td><td>0.143</td><td>0.210</td><td>0.087</td><td>0.209</td><td>0.180</td></tr><tr><td>5</td><td><strong>0.39</strong></td><td>0.128</td><td>0.187</td><td>0.079</td><td>0.186</td><td>0.168</td></tr></tbody></table>

# 5 Conclusions and Future Work

In this paper, we propose an unsupervised embedding approach that extracts meaningful information from networks of electronic health records to improve the initial tabular data for downstream tasks. The extension of the Word2Vec paradigm to Doc2Vec and Fasttext leads to better classification performance, while the Fasttext approach has the advantage of requiring no retraining upon insertion of new nodes. By utilizing the Fisher vector approach, which is a rare case in the EHR literature for low to high-level representation, information from multiple nodes can be concatenated to the final classification vector to boost the performance in high numbers. Clustering results are also improved by the graph structure information and demonstrate much higher performance with the addition of fisher vectors. It is important to mention that since the proposed methodology is label independent, the provided vectors can be utilized for different supervised or unsupervised ML tasks. Future work is focused on the handling of big data, the integration of an interpretability mechanism that will provide reasonable explanations of the predicted results, and the quantitative comparison with state-of-the-art approaches.
**Acknowledgment.** This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: MediLudus - Personalized home care based on research has based on game and gamified elements T1EDK-03049).

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