Discussion
In this proof-of-concept study, we have demonstrated the use of unsupervised machine-learning methods to identify different themes in medical records of patients with PNES. The majority of these themes were interpreted as congruent by four experts. This method is efficient in gaining quick comprehension of the medical records. Furthermore, topic models provide an insight into the thinking process of health professionals at the time of initial management. While the method is applied to a small dataset from the scanned medical records here as proof of concept, it is scalable to large volumes of medical data available in electronic medical records and for any disease process. The topics generated by machine learning were congruent with interpretations by clinicians indicating this method can be used for screening of medical conditions among large volumes of medical records in clinical practice.
Natural language processing (NLP), also described as text mining, is a way of converting unstructured text into a structured format paving the way for automated analysis. With the increasing use of electronic medical records, the need for NLP to handle large volumes of medical information has also risen.18 There are three main approaches adopted in NLP. The machine-learning approach involves fully automated text processing, whereas the rule-based approach depends on predefined rules by experts. The hybrid approach is a combination of the two methods.19
The topic modelling approach has been previously adopted by medical researchers to identify patterns of comorbid medical conditions,20 detect medication non-compliance using posts in patient forums,21 describe public health information from the social media,22 cluster analysis of large biomedical datasets23 and predict inpatient clinical order patterns.24 The method used in this study assigns words to topics (themes) based on the probability of membership of the topic but the clinical interpretation of the topic is still needed. We approached this by asking four experts to assign meaning to the collection of words in each topic. This aspect of the work can be prone to bias as different experts can interpret the collection of words in different ways or use various words to convey a similar meaning. For example, the terms ‘phenomenology’ and ‘semiology’ were used to described observations on seizures. A drawback of topic modelling is that it is not a classification tool and cannot separate PNES from ES. Machine-learning methods for classification include generalised linear model, naive Bayes classification, tree-based approaches, support vector machine and neural network. These methods were not used here as they are not adept at evaluating the thematic structures of the documents we used in the study.
This study provides useful insights into how doctors in the emergency department view or conceptualise seizures among patients presenting with PNES. This approach allows one to postulate that the clinicians were considering the diagnosis of ES rather than PNES. This was inferred from the frequent use of descriptive semiological terms of ES such as ‘tonic’ and ‘clonic’ in the documents. Furthermore, ‘epilepsy’, ‘epileptic’ and ‘GTC’ (implying generalised tonic–clonic seizure) appeared more often in topics (2, 3, 6, 10, 12), whereas ‘pseudoseizure’ featured only once in topic 13 indicating the focus of doctors was ES. Topic 1 is a collection of terms describing the seizure semiology, but descriptions of terms relating to PNES such as ‘arching’ appeared only once. Other typical terms used in the description of PNES such as ‘pelvic thrusting’, ‘eye closure’, ‘head-shake’, ‘asymmetry’ and ‘asynchrony’ were not observed at all. This view is consistent with the frequent mention of antiepileptic medications used in the treatment of seizure and status epilepticus; these medications include phenytoin, clonazepam, diazepam and midazolam (topic 6 on acute treatment). The frequent use of the term ‘loading’ is a likely reference to intravenous loading of phenytoin in status epilepticus and appeared in topics 6 and 14. Aligned with that, ‘status’ appeared in topics 3 and 5 suggesting that the doctors were considering the diagnosis of status epilepticus. Misdiagnosis of non-epileptic psychogenic status as true status epilepticus leads to inappropriate interventions resulting in considerable morbidity, healthcare utilisation cost and even mortality.7 These observations raise the need to improve education on seizure diagnosis among medical professionals.8 10 Related to this matter is the need to document observations of these events in free-text form avoiding the use of jargon. This is a potential trap with electronic medical record whereby commonly used phrases are saved for repeated use. This situation may lead to homogenising of neurological descriptions.
What we have illustrated here is only one use of topic modelling with medical records in the setting of the emergency department. This approach does not have to be restricted to this location or neurological disorders. It can be used for other medical conditions and in any location. Furthermore, the method can also be applied to qualitative data from surveys or suggestions from team-building meetings.
Limitations
There are several limitations to this study. The unigram (bag-of-words) approach we adopted does not arrange words according to their meanings. In this approach, each word is treated equally and the relationships among words are not explored. In order to overcome this limitation, we used word combinations when we considered the sequence of words to be important (example: ‘status epilepticus’). An alternative method is bigram approach. However, the package ‘topicmodels’ does not handle the bigram analysis.16 Additionally, the use of stop-word filter denotes that a negative meaning of the sentence may not be discovered in the themes (example: ‘no incontinence’ vs ‘incontinence’). At the time of the data acquisition, electronic medical records were not operational in our institution. The data were transcribed from scanned medical records. This task introduced a potential source of error with manual copying of texts. It is hoped that the introduction of electronic medical records will render the analysis of written text easier. Another limitation of this study is that we have only studied patients with PNES who had VEM. This approach was undertaken to ensure the analysis was related to patients with conformed PNES. The drawback of this approach is that patients with PNES who did not have VEM could not be captured. Hence, it is possible that more severe cases of PNES were included in the study introducing potential bias. Furthermore, our study did not include patients presenting with ES. As such the results cannot be directly extrapolated to all patients presenting with seizures.
