Big Data: Application of Folksonomy for Clinical Nephrology Research

The daily activity in the medical field generates a multitude of data from clinical records and reports, collected from anamnesis and physical examination, laboratory and other tests, diagnosis and treatment. At present in our environment, most patient medical information is collected in the electronic health record.

The analysis of data from the clinical records allows for quality control of medical actions, to obtain observational and prospective data to generate scientific evidence and to select patients with certain characteristics in order to propose their participation in clinical trials.

Classically, obtaining this data requires a great load of work consisting of the manual revision of the reports to obtain data of interest, to set up and feed databases (collection of data from quantitative variables and transforming the information expressed in written language into numerical variables) and its subsequent analysis. This is a time-consuming process, and usually does not allow the real time reanalysis of parameters not considered of interest in the initial project. Any reconsideration involves redoing the entire manual process.

The term big data, increasingly used in our daily lives, implies an amount of data in which the volume, variability and speed of processing required makes it very complex to analyse using manual systems or standard software for handling them (Palanisamy and Thirunavukarasu 2019; Escarvage et al. 2019).

Most systems that perform natural language understanding (NLU) or natural language processing (NLP) require an ontology or master entity to later analyse the documents (El Wazir 2019). In these types of systems, the categories or labels are established by the master entity - top down distribution - so they do not discover anything that is not already considered in the ontology.

Furthermore, it should be taken into consideration that, on many occasions, creating an ontology in the medical field can become a bigger project than the analysis of some tens of thousands of documents (as can be seen with the Snomed project (snomed.org) that has been developing for a long time).

The concept of folksonomy comes from two terms: “folk” and “taxonomy.” Taxonomy is the art of labelling documents, and “folk” refers “popular.”

Therefore, this term refers to a taxonomy defined by the data contained in the documents, without the previous need to generate an ontology or master entity on terms of interest. Folksonomy allows concept tags to be automatically highlighted in natural language to reveal the internal content. This advanced analytics solution transforms unstructured text documents into structured text documents and discovers information. Folksonomy is a real-time classification system that is automatic, based on the labels and the frequency with which they appear, and is the only viable way to be able to work with huge amounts of documents. The way this system works is known as bottom up and the Bismart Folksonomy solution is the first software that can manage this type of classification (bismart.com).

The use of NLP algorithms together with folksonomy in the medical field would allow the investment of just as much time in the generation of databases as that required by the usual care activity and the real time analysis of the new data collected. Thus, big data would bring significant benefits to the medical sector (Dong et al. 2017).

In this paper, we report on the first pilot experience in the use of folksonomy together with artificial intelligence in NLP to analyse clinical data from hospital discharge reports of the Nephrology Department of the Hospital del Mar in Barcelona.

The research team collected 1,631 hospitalisation discharge reports from the Nephrology Department of the Hospital del Mar between 2016 and 2018. The documents were written in two languages: Catalan and Spanish.

These reports, which were in PDF format, were scanned using Optical Character Recognition systems and the different fields in the documents were separated using algorithms based on pattern detection, while each document was anonymised and saved in the cloud. Then, data normalisation processes and lemmatisation were carried out, algorithms were applied, and folksonomy was executed. The Bismart Folksonomy web portal was installed so that the addition of synonyms and/or implications could be carried out (Figure 1). This process allowed the anonymisation of each medical discharge, and the identification of the relevant information collected in each section of the discharge reports: diagnosis, reason for consultation, personal medical history, usual treatment, complementary tests, evolution and treatment at discharge.

Medical terms and acronyms specific to the specialty appeared in the documents. This added further complexity for data extraction. Since folksonomy does not work with languages but with terms, the problem was solved by using synonyms.

In nephrology, the classification of the chronic kidney disease (CKD) stage (Astor et al. 2011) is of great importance since it has prognostic and therapeutic implications. The review of the disease stage or renal situation according to the information collected in the “diagnoses” section in the discharge reports only allowed the identification of the CKD stage in some 300 reports. Thanks to the tool’s ability to add synonyms, the words “grau,” “estadio” and “estadi” were assigned to the word “grado” (“grade”), making it possible to find the CKD stage in 768 reports.

In order to classify the CKD stage of the rest of the reports, algorithms were generated with heuristic rules for their correct identification based on: (a) the presence of the words ‘acute kidney failure’ and synonyms in the diagnostic section implied the label of acute kidney failure, (b) the presence of the words ‘kidney transplant recipient’ and synonyms in the reason for consultation section implied the label stage 5 CKD, c) the identification of the words “chronic kidney disease stage X” and synonyms among personal medical history allowed the labeling of reports as CKD stages 1 to 5, d) the use of creatinine in the entry laboratory tests together with age and gender (data collected among anthropometric variables) allowed the estimation of glomerular filtration rate (eGFR) by entering the formula of CKD-EPI (Castro et al. 2009) in the software. Despite this, 79 documents were left unclassified in terms of renal situation, so a manual review and assignment of the renal situation was carried out. Thus, all reports were classified as: acute kidney failure, CKD stages 1- 5 or no renal disease.

As a pilot test, 3 questions were raised:

• What percentage of admissions to nephrology are diabetic and receive treatment with metformin associated or not with another hypoglycaemic drug? What is the CKD stage of patients admitted and treated with metformin? How many and what is the renal situation of patients treated with metformin and diagnosed with lactic acidosis related with the drug?

• What is the attitude of the nephrologists in the Nephrology Department of Hospital del Mar in relation to the withdrawal or maintenance of inhibitors of the renin-angiotensin system of the patients admitted?

• Emotional health in CKD patients. What is the percentage of admissions to nephrology who receive some hypnotic/sedative/antidepressant treatment even though there is no diagnosis related to this pathology included in the patient’s medical history?

Metformin remains the most widely used hypoglycaemic drug to treat type 2 diabetes. The benefits of metformin in terms of morbi-mortality even in moderate CKD stages (up to 45ml/min/1.73m2 eGFR) have been clearly demonstrated (Cameron et al. 2017). However, in moderate CKD stages the dose should be adjusted, and its use is contraindicated in advanced CKD, its administration being associated to the presence of lactic acidosis especially in patients with eGFR below 30ml/min/1.73m2 (Alexander et al. 2018).

Diabetic patients were identified based on the presence of this diagnosis in the “diagnoses” section of the discharge reports. Thus, a lower than expected percentage of diabetic patients were identified, so the search was extended with new heuristic rules, assigning the diagnosis of diabetes to those reports in which a hypoglycaemic drug was among the usual treatment. Given the high number of hypoglycaemics available on the market, the inclusion of each one of them individually in the searches generated a greater complexity to the project. Thus, it was decided to join the different hypoglycaemics into groups, and the use of the ATC (anatomical therapeutic chemical classification system (WHO, EMA)) was chosen. This classification categorises drugs into groups and subgroups. This process involves detecting the trade names of drugs and active principles separately and applying graph analysis algorithms to obtain these ATC groups. In discharge reports both trade names and active principles can be found, so the detection of ATC groups is not a trivial exercise. In the case of hypoglycaemics, these correspond to Group A, sub-group A10 of the ATC classification.

Finally, 651 of 1631 reports were identified as having a diagnosis of diabetes (39.91% of reports), 85 of which were treated with metformin (subgroup A10BA of the ATC active ingredient classification).

The classification of these patients in relation to their CKD stage is shown in Table 1.