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2nd Edition

13 October 2020


Tommaso Lo Barco


The global trend toward "digital health" in the US and in Europe has led to an unprecedented adoption of Electronic Health Records (EHRs): by the end of 2014, 83% of US physicians and 75% of hospitals used some form of EHRs ( Office of the National Coordinator for Health Information Technology Health Record Adoption: 2004-2014, 2015) (Adler-Milstein, et al., 2015).

An increasing number of hospitals are now equipped with digital data banks (Clinical Data Warehouse) consisting of data collected in the patient care process through EHRs, and which can be used for statistical or research purposes.
Clinical and epidemiological studies that exploit big data to detect and propose interdisciplinary treatments for patients with similar medical history, diagnosis and outcomes have enormously increased in recent years.

Among the most impacting applications deriving from the use of Big Data and EHRs are those that allow to intervene directly in the diagnostic process, avoiding delays or errors.
In the same way it is interesting how Big Data can be used to elaborate predictive models, able, for example, to calculate the risk of disease.

It is intuitive that the use of this type of support in the context of rare diseases is highly desirable, where the scarce knowledge of the disease due to the small number of patients often leads to a delay in diagnosis and the difficulty of predicting the outcome.
However, the need to compute large amounts of data in order to elaborate effective models has meant that today there are models that can be applied only to relatively common pathologies, mainly using numerical data (laboratory or computer-based data deriving from imaging techniques).

Natural Language Processing (NLP) is an innovative informatic technology able to extract information directly from digital written documents. Recently, the models that have been developed are able to exploit this technique by applying it to patient medical reports, in order to suggest diagnostic hypotheses for common pathologies, or describe the clinical phenotype of a rare pathology (Liang, et al., 2019) (Garcelon, et al., 2018).

The starting point for this work derives from the opportunity to adopt this technique in the context of a rare pathology: Dravet Syndrome.

This condition presents a significant problem of diagnostic delay. Although the clinical characteristics for diagnosing are all presented within the second year of life, from the data present in the literature it is clear that only in 2 countries in the world can manage a timely diagnosis, with an average delay

that, only in Europe, it is around 3-4 years from the onset of illness. The delayed diagnosis, besides representing a frustrating condition for the family, can determine the use of contraindicated drugs that can exacerbate seizures, increase the risk of status epilepticus, worsen cognitive outcome, as well as delaying the employ of helpful drugs.

The main differential diagnosis with Dravet Syndrome at onset is represented by a benign condition characterized solely by the predisposition to have seizures during fever.
This work shows how in the visit reports of subjects produced within the 2 years of life are already present some terms that, statistically more relevant in subjects with Dravet Syndrome than in subjects with "febrile convulsions", could be used to elaborate Alert systems designed to direct suspicion and / or advise targeted follow-up.

We have also demonstrated how the automatic extrapolation of concepts from the visit reports of a cohort of patients with rare pathology followed over the years, allows to reconstruct a "longitudinal model of disease" compatible with what is known from the literature. Having validated this new mode of extrapolating a longitudinal phenotype, it justifies its use in other rare conditions whose outcome is, contrary to Dravet Syndrome, poorly known.

In addition to the possibility of exploiting NLP for automatic extrapolation of concepts from reports, this technique can also be used to the targeted search of terms within visit reports. This opportunity allowed us to easily collect data on weight, height and head circumference of our cohort of patients with Dravet Syndrome, subsequently implemented with patients from a second center. The analysis of the results documents a slowing in rate of cranial growth in these patients, a data not previously reported in the literature.

Further uses that derive from the application of the NLP could be the retrospective research in a CDW of patients suffering from a specific condition not yet diagnosed, searching for keywords within the visit reports. Moreover, the comparison with a "control" population could reveal clinical data not yet present in the literature.

In conclusion, we demonstrated how Natural Language Processing applied to narrative medical reports, both through a data-driven analysis and targeted searches, allows to make diagnosis and to reconstruct the phenotype of a specific rare condition, like Dravet Syndrome, not only confirming what is present in the literature, but also contributing to enlarge the known phenotype.

Dottoressa Elisa Maria Zamperioli

Background and aim: Dravet syndrome (DS), also known as severe myoclonic epilepsy in childhood (SMEI), is a rare, drug-resistant epileptic and developmental encephalopathy associated with neurological and cognitive-behavioral impairment. Individuals with Dravet syndrome exhibit a unique set of clinical characteristics, making some of the commonly used cognitive and adaptive functioning rating scales inapplicable.; these scales frequently demonstrate "floor effects" or "ceiling effects", rendering them ineffective in assessing the overall functioning of the patient. Thus, there is a need for a specific, multidimensional, population-built scale to investigate disabilities and comorbidities in individuals with Dravet syndrome, in order to plan appropriate treatment strategies.

Methods: In this study, we conducted a longitudinal retrospective analysis of the epileptic phenotype, neurological and cognitive features of 40 patients with Dravet syndrome, aged 3 to 38 years old, who were followed at the Department of Childhood Neuropsychiatry, Verona, Italy. Additionally, we performed a prospective study of neuropsychological data obtained from the application of the D.A.N.D scale and other standardized scales.

Results: Our analysis revealed a progressive cognitive decline in the patient poolover time, which correlated with an arrest or regression in adaptive functioning. We found significant clinical variables in the population ( SCN1A gene truncating mutation, episodes of status epilepticus, absence, myoclonia in early life) correlate with increased motor impairment, worsening of personal autonomy, academic and learning abilities, associated with socialization impairment and worsening of executive functions.

Conclusions: Our study highlights the need for a sensitive and effective instrument to assess characteristic disabilities in individuals with Dravet syndrome accurately. From our results, the D.A.N.D. scale appears to be a practical and accurate tool capable of analyzing all multidisciplinary issues. This allows appropriate formulation of therapeutic and rehabilitation plans (psycho-educational programs, specialized assessments and treatments) in order to ensure the best possible quality of life for these patients.


Recognized as legal personality with provision of the Regional Council of Veneto dated 09.12.2015 n ° 815.

Agreement with the AOUI of Verona Prot. 1306 of 12/01/2021. Approved with Resolution no. 1392 of 12/30/2020

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