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3 The Basic Principles andPrecautions ofDrug Therapy
361
Malaysia, Australia, and China, comprising 1798 adults with newly diagnosed epilepsy, were included for model development. An attention-based deep learning
Transformer model was adopted in this study [137]. The model consists of an
encoder and decoder, both of which have a multi-head attention mechanism. In this
mechanism, each head is a separate attention layer that learns to attend to different
relationships or patterns in the input data. By using multiple heads, the model can
extract useful information from complex relationships and make more reliable
predictions.
Sixteen clinical features were input into the model: (1) sex; (2) age group at ASM
initiation; (3) history of febrile convulsion; (4) history of childhood CNS infection;
(5) history of head trauma; (6) History of hypoxic brain injury; (7) history of alcohol abuse; (8) history of alcohol abuse; (9) epilepsy in rst-degree relative; (10)
history of cerebrovascular disease; (11) intellectual disability; (12) psychiatric disorder; (13) number of pretreatment seizure; (14) type of epilepsy; (15) EEG ndings; (16) brain imaging ndings. Shapley additive explanations (SHAP) value was
used to assess the relative impact of each feature on the model. The output was one
of seven ASMs (carbamazepine, lamotrigine, levetiracetam, oxcarbazepine, phenytoin, topiramate, or valproate), being considered as the best choice, giving the
highest chance of treatment success as dened as seizure freedom while still using
the rst ASM within the rst year of treatment. The study had two parts. In the rst
part, data from pooling the ve study cohorts was randomly selected for training
and validation. Approach of 80:20 train-test split was adopted. In the second part,
the ML model was trained by using the Glasgow cohort, which is the largest of the
ve cohorts. The model was then validated on each of the remaining four cohorts.
The researchers compared six different ML models, including transformer, multilayered perceptron (MLP), XGBoost, SVM, RF, and logistic regression, in both
parts of the study.
Among the six models, the transformer model attained the best performance with
an AUC of 0.65 (95% CI: 0.65±0.02) and a weighted balanced accuracy of 0.62
(95% CI: 0.62±0.02) on the test set.
In the second part of the study, the Transformer model was trained using the largest cohort and achieved AUCs in the range of 0.52–0.60 and a weighted balanced
accuracy in the range of 0.51–0.62in validation cohorts. Several pre-treatment seizures, EEG, and neuroimaging ndings had the highest SHAP values in the pooled
cohort and hence contributed most to the prediction model.
This study has two remarkable features. First, the dataset in the ML model
development was derived from some of the largest, best-characterized adult cohorts
of newly diagnosed epilepsy in the world [27, 138]. Second, a deep learning ML
model instead of traditional statistical learning models was selected to predict the
best ASM in new-onset epilepsy. This transfer learning technique allows their
model to expand to diverse patient populations, tasks, and datasets with small sample sizes, with comparable performance to their large developmental dataset [139,
140]. The model could bring clinical implications if it could be further improved
and validated.
362
Q. Wang et al.
3.5.4 Prediction ofDRE
Silva-Alves etal. attempted to use both clinical and genetic parameters to predict
drug responsiveness in patients with mesial temporal epilepsy [141]. They used an
RF classier for prediction. They gathered a cohort of 237 patients with mesial
temporal lobe epilepsy, of which 75 were responsive to ASM and 162 were drugresistant, from a single center in Brazil. ASM responsiveness was dened as seizure
freedom for at least 12months. DRE was regarded as a failure in reaching seizure
freedom despite taking two or more ASMs either sequentially or in combination.
Clinical features included gender, age of seizure onset, febrile seizures, and presence of hippocampal sclerosis on MRI.They genotyped 119 SNPs in 11 different
genes. They ran the algorithm with the clinical features only, the genetic features
only, and a combination of both clinical and genetic features. In addition, the
researchers tried different combinations of features to train the model to obtain the
best results. They validated their model by LOOCV.The mean decreased accuracy
(MDA) value is used to show the most relevant features contributing to the best
model [142].
The results showed that the clinical plus genetic features contributed model had
the best performance. The best model included the presence of hippocampal sclerosis and 56 SNPs. Finally, the model based on a combination of clinical and genetic
data achieved an AUC of 0.82, compared with an AUC of 0.46 for the model with
clinical data only and an AUC of 0.80 with genetic data only. SNP in the CYP1A2
gene had the greatest MDA value and hence had the greatest contribution in the
prediction model, followed by the presence of hippocampal sclerosis on MRI.
In the current study, the genetic component has a dominant contribution. It is
reected in the AUC of 0.80 for the genetic data only model compared with the
AUC of 0.46 for the clinical only model. It contrasts with the aforementioned model
developed by de Jong etal., in which clinical data bears more weight. This could be
explained by the homogeneity of localization of epilepsy focus, that is, the temporal
lobe, in this study, while in de Jong’s study, the epilepsy foci were variable. It may
imply that the epilepsy subtype should be targeted if genetic features are to be incorporated into the ML model.
An etal. aimed to predict DRE by making use of administrative data through an
ML model [143]. They made use of a large claim database in the US.The researchers identied epilepsy and DRE cases indirectly. Epilepsy was dened by three
criteria: (1) cases with one or more epilepsy diagnosis claim with ICD-9 or ICD-10
for epilepsy and recurrent seizures, or two claims for convulsion with appropriate
ICD-9 or ICD-10 codes; (2) cases with more than one claim for an ASM; (3) cases
with the rst ASM prescribed as monotherapy for at least 60days of supply. The
authors identied 292, 892 epilepsy patients for inclusion in the dataset. Drugresponsive cases were dened as ordering of the same ASM regimen without the
need for change for at least a year. Treatment failure was dened as a different ASM
and claimed no matter whether it was an add-on, replacement of a current agent, or
as rescue therapy. The denition of DRE in this study was different from the widely
3 The Basic Principles andPrecautions ofDrug Therapy
363
accepted consensus endorsed by the ILAE, which states failure of two appropriately
chosen ASMs [105]. In this study, the failure of three ASMs was considered as DRE
instead. Those who just failed one or two ASMs were excluded from the analysis.
The proportion of DRE in their cohort was about 13%. For each patient, an index
date was dened as the date when the rst ASM was claimed. The period preceding
the index date, traced back to the commencement of the health care plan, was
dened as the observation window. The period following the index date, up until the
date of the last claim, was dened as the evaluation window. The data extracted
from the observation window was used to predict the drug responsiveness of epilepsy. Three ML algorithms were compared: multivariate logistic regression (MLR),
SVM, and RF.A total of 1270 features were extracted from each patient, encompassing the demographics, comorbidities, insurance policy, treatments, and encounters. Half of the extracted features were nally used in the dataset to build the model.
The dataset was randomly divided into a training set (60%), a validation set (20%),
and a test set (20%). As a reference, the researchers formulated a benchmark model,
which only contained two features, namely the age at epilepsy onset and sex.
Information Gain value was used to demonstrate the relative contribution of features
to the predictive model [144]. They used Brier scores and calibration plots to assess
the reliability of the predicted probabilities output by the ML models [145, 146].
The RF was the best model in this study and achieved an AUC of 0.76 (95% CI:
0.76–0.77), outperforming the benchmark model with an AUC of 0.66 (95% CI:
0.65–0.66). Moreover, predicted probabilities for DRE were well-calibrated with
the observed frequencies in the data. The research group analyzed the period from
the index date to the date of initiation of the third ASM regimen, which was used as
a surrogate marker of failure of the second ASM, which fullled the ILAE’s denition of DRE.The period was 1.97years on average. In other words, the RF model
could predict ILAE-dened DRE approximately twoyears before a patient failed
two ASMs.
The limitation of the study based on the claim database has been discussed
before. Another major drawback of this study is that the denition of DRE departs
from the denition accepted globally. A proxy of failure of three, rather than two,
ASM regimens was considered as DRE.The aim of this may be to enable a greater
contrast in data between DRE and non-DRE, and hence enhance the model performance [147]. This may again highlight the limitations of performing a clinical study
by employing an administrative database.
Delen etal. aimed to predict DRE by training ML algorithms on data retrieved
from a nationwide electronic medical record (EMR) database [148]. The researchers identied epilepsy patients by the ICD code of epilepsy in the diagnosis record.
Drug responsiveness was assessed indirectly. DRE was dened by the use of nonASM treatments, namely epilepsy surgery or VNS, while all cases with solely ASM
treatment were assumed to be non-DRE.A total of 37,024 were nally recruited in
the cohort. Among them, 806 had DRE and 36,218 had non-refractory disease. The
features listed in the dataset included patient’s demographic features namely age,
gender, race, and marital status; physician’s initial diagnosis; total comorbidity
count; and presence of eight common comorbidities: hypertension, depression and
364
Q. Wang et al.
anxiety, hyperlipidemia, drug abuse, head and neck symptoms, esophageal disease,
anemia, and diabetes mellitus. The dataset was randomly split into a 70:30 ratio for
training and validation, respectively. The contribution of each feature was evaluated
by excluding features one at a time when running the model repeatedly. In each
iteration, the performance difference of the model between including and excluding
the specic feature could be calculated, reecting the importance of the feature.
Since the cohort was grossly imbalanced with a very small fraction of DRE cases
(2.2%), the researchers used additional balancing measures to avoid biases.
Three ML algorithms were compared, including the decision tree, RF, and
GBT.The performances of the three classiers adopting each of the different balancing approaches were compared. The best combination attained an F1-score of
0.59, accuracy of 75%, and AUC of 0.83. The analysis of the relative importance of
input features showed that comorbidity count was the most important feature in the
prediction of DRE, followed by drug abuse, hypertension, and hyperlipidemia.
This study denes DRE with an unusual criterion; only cases with epilepsy surgery or VNS were considered as DRE.This is a marked discrepancy with the global
consensus. It is well known that epilepsy surgery is underused in the DRE population. The reasons are multifactorial, including under-referral, lack of expertise, and
low patient acceptance. The pitfall is reected by the very low proportion (only
2.2%) of DRE in the cohort, compared to the commonly quoted proportion of
around 30%. Although complex statistical measures have been employed, trying to
balance the biased gure, the limitation could not be overcome. The high AUC but
a relatively low F1 may imply a skewed dataset. The ndings of comorbidities,
including hypertension and hyperlipidemia, play an important role in predicting
DRE are not entirely convincing.
The studies discussed above were summarized in Table3.5.
3.5.5 Conclusions
The application of AI in epilepsy prognosis prediction is still in its infancy, although
and growing number of research studies have shown encouraging results. A crucial
factor in the successful development of a powerful predictive model lies in the availability of high-quality developmental datasets. An optimal cohort from which the
dataset is generated should consist of reliable diagnoses, comprehensive investigations, and a longitudinal follow-up history. It would be ideal if the dataset could be
collected from different centers to minimize biases in prescription preferences,
patient demographics, and clinical backgrounds. External validation is an essential
step to ensure the generalizability of an ML model. It involves testing the model on
a new dataset independent of the model development. Unfortunately, this important
step is lacking in most of the existing published models. Furthermore, the inclusion
of genetics, radiological, and EEG features holds great potential to enhance the
performance of the ML model. Similar to therapeutic agents or medical devices, the
ultimate assessment of an ML model prior to widespread clinical use is a randomized controlled trial to verify its efcacy and safety in practice.
3 The Basic Principles andPrecautions ofDrug Therapy
Important clinical features
such as frequency of seizure,
indication of ASM, etc.
could not be obtained from
the claim databases
Very large number of recruited
cases from a national wide
claims database
Small number of patients
were recruited
Biomarkers of EEG was
employed and analyzed by
QEEG technique in the ML
development
Small number of patients
were recruited
The reasons of why some
frequency bands of certain
electrodes of EEG had
relatively greater
contribution to the ML
model outcome than the
others were not explained
Combination of biomarkers of
EEG and clinical features were
employed in the ML
development
Comparison of clinical
features only model with EEG
and clinical combination
model proved the better
performance of the combined
365
(continued)
model
Number of patients
recruited Strength of study Limitation of study
34,990 patients from
a claim database
ML model: RF
Retrospective recruitments from
claims database for both training and
validation
Index date of ASM change (initiation
of new claim, add-on or substitution)
was used to train the ML to predict
To develop an
ML model to
select the ASM
with highest
successful
outcome for an
individual
Author (year) Aim of study Highlights of study design
Prediction of ASM response
Devinsky etal.
Table 3.5 Summary of the aim, strengths and limitations of the included studies
[125] (2016)
20 patients from
ASM regimen with lowest likelihood
of regimen change
ML model: SVM
To develop a
Ouyang etal.
single center
EEG recorded before and one to
three months after ASM initiation or
change for QEEG features selection
and model training
EEG recorded before and six months
ML model to
differentiate
ASM effective
and ineffective
groups
[126] (2018)
46 patients from
after ASM initiation or change for
model validation
ML model: SVM
To develop a
Zhang etal.
single center
Retrospective recruitment of patients
on LEV, employing both clinical and
EEG sample entropy in various
frequency bands as input features
ML model to
predict
probability of
seizure freedom
in patients
treated with
LEV
[127] (2018)
366
Patients were recruited from
single center, prescription
preference, disease pattern
may affect the model’s
generalizability
Different ML models were
compared to choose the best
performing one
The model failed to predict
Genetic features were
treatment response other
than with CBZ or VPA in
other independent cohorts
upon external validation
incorporated into the ML
model.
Multi-SNP model were
compared to individual SNPs
models and proved to be
outperforming
Q. Wang et al.
287 patients from
Number of patients
Table 3.5 (continued)
single center
recruited Strength of study Limitation of study
ML models: Decision trees, RF,
SVM, logistic
Regression, XGBoost
To develop a
ML model to
predict the
Yao etal. [130]
(2019)
Author (year) Aim of study Highlights of study design
Retrospective recruitment of
patients, employing 14 clinical
features to train and validate the
model
outcomes ASM
of patients with
newly
diagnosed
epilepsy
community- treated
115 Australian
patients in training
dataset; 63 patients
from the same
ML model: kNN
Prospectively recruited patient cohort
was employed for genetic data
collection.
To develop and
externally
validate a ML
model to
Petrovski etal.
[131] (2009)
and Shazadi
etal. [132]
patients for external
hospital population
for validation; 108
Selected SNPs were input to train the
model
The model was validated by
Australian and then the UK cohorts
predict the
outcomes ASM
of patients with
newly
(2014)
validation; further
external validation
on two independent
UK cohorts (281
from Glasgow and
491 from SANAD
diagnosed
epilepsy
trial)
3 The Basic Principles andPrecautions ofDrug Therapy
Moderate AUC (0.62)
suggested room for further
Small contribution of
pharmacogenetics may
indicate cost-ineffectiveness
of employment of genetic
data or a need of
improvement of genetic
input selection
Both clinical and genetic
features including those related
specically to BRV
pharmacodynamics were
incorporated in the ML model
Relative high AUC (0.75) was
achieved in external validation
in independent cohort
Comparison between genetics
plus clinical model to the
clinical only model proves that
the combined model was
superior
improvement
Large number of patients from
cohorts from different
countries were recruited for
model development
High sensitivity but low
specicity of the model,
probably limit its
application. It may be more
Deep learning model of
transformer was employed
and genetics (multi- SNPs) in
ML development
Combination of genetics and
Employment of both clinical
367
(continued)
suitable in screening rather
than diagnosing.
clinical features greatly
improve the model when
compare with clinical only
model
235 patients from
the treatment arm of
a multicenter clinical
trial as training
dataset; 47 patients
from the treatment
arm of another
multicenter clinical
trial as validation
dataset; 235 patients
from the placebo
and logistic regression- based
stacking
Retrospective recruiting patients
from two clinical trials on BRV
Both clinical and highly selected
genetic features were employed to
develop the model
ML models: Sparse multi-block
PLS-DA, multi-modal neural
network, elastic net classier, GBT,
To develop a
ML model to
predict the
response to
BRV
De Jong [134]
(2021)
The placebo dataset was used to train
1798 patients from
arm of the same
study of training
dataset as placebo
the model to prove the absence of
placebo effects in the nal results
ve cohorts from
dataset
ML models: Transformer, MLP,
XGBoost, SVM, RF, and logistic
To develop a
ML model to
Hakeem etal.
[136] (2022)
four countries
regression
16 clinical features as input for
model development
Two approaches to train and validate
predict the
outcome of rst
ASM for newly
diagnosed
237 patients from
single center
in order to select the best model
ML model: RF
Both clinical and genetic (SNP)
variables as input to develop the
epilepsy
To develop an
ML model to
predict drug
Prediction of DRE
Silva- Alves
etal. [141]
(2017)
model
resistant mesial
TLE
368
Unusual denition of DRE
(those who failed three
ASMs rather than two) was
used
A great number of cases from
a claim database were
employed
Unusual denition of DRE
(those with VNS and
A great number of cases from
a nationwide EMR database
epilepsy surgery) was used
resulting in very low
proportion of DRE
were employed
Q. Wang et al.
partial least squares- discriminant analysis, QEEG
292,892 patients
from a claim
Number of patients
recruited Strength of study Limitation of study
(continued)
Author (year) Aim of study Highlights of study design
Table 3.5
database
ML models: SVM, RF,
MLR
Retrospective recruitments from
To develop a
ML model to
predict DRE
An etal. [143]
(2018)
37,024 patients from
a EMR database
claims database for both training and
validation
Proxy indicators, such as changes in
ASM claim, as surrogates for clinical
features
ML model: Decision trees, RF, GBT
Retrospective recruitments from
claims database for both training and
validation
Proxy indicators as surrogates for
clinical features
To develop a
ML model to
predict DRE
Delen etal.
[148] (2020)
ASM antiseizure medication, AUC area under curve, DRE drug resistant epilepsy, EMR electronic medical record, GBT gradient boosting Tree, kNN k-Nearest
Neighbor, ML machine learning, MLP multilayer perceptron, MLR multivariate logistic regression, PLS-DA
quantitative EEG, RF random forest; sparse multi-block partial least squares discriminant analysis, SNP single nucleotide polymorphism, SVM support vector
machine, TLE temporal lobe epilepsy, VNS vagus nerve stimulation, XGBoost extreme gradient boosting
3 The Basic Principles andPrecautions ofDrug Therapy
Achieving all these goals requires close collaboration between clinicians, data
scientists, and ML specialists. The synergistic efforts of such interdisciplinary teams
could lead to a paradigm shift in the management of epilepsy, ultimately achieving
the goal of Personalized Medicine for epilepsy.
369
3.6 Acute Symptomatic Epileptic Seizures
Based on the ancient disease of epilepsy, the International ILAE named the phenomenon of epilepsy epileptic seizure, epileptic syndrome, epileptic status, and epilepsy. Epilepsy can be divided into primary, cryptogenic, and symptomatic epilepsy
according to its cause. Recently, the ILAE has dened epilepsy as a chronic brain
disease, so the rst seizure caused by an acute brain disease is not in the category of
epilepsy, but the clinical manifestations of such patients and seizures are almost the
same, so it needs to be renamed. Acute symptomatic seizures may be more consistent with the connotation of seizures in such patients [1, 149].
3.6.1 Denition ofAcute Symptomatic Epilepsy
No international organization has established an accepted denition of acute symptomatic epilepsy or guidelines for its prevention and treatment. However, this professional term has been described in the literature. A PubMed database search
revealed 1110 related studies on acute symptomatic epilepsy as the main topic, indicating that this topic has a basis for publication. According to the literature, acute
symptomatic epilepsy refers to the rst seizure caused by an acute brain disease
(structural or functional). Because it is the rst seizure, it is not accepted according
to the ILAE denition for the diagnosis of epilepsy. However, epilepsy is clearly
dened by the ILAE for such patients if there are repeated seizures in the future,
indicating that there is some intrinsic link between them. Therefore, studying the
clinical characteristics of acute symptomatic seizures can help us address the presence of such seizures.
3.6.2 Historical Evolution
According to the PubMed database, Feikes etal. [150] rst mentioned acute symptomatic seizures in 1964. They mentioned acute symptomatic seizures in patients
presenting with hyponatremia after acute surgery. In 1967, Varavithya etal. [151]
mentioned the existence of acute symptomatic seizures in their summary of acute
hyponatremia in children. In 1984, when studying alcohol abuse, Pilke etal. [152]
reported that acute symptomatic seizures occur after alcohol withdrawal. In 1992,
Hauser etal. [153] proposed that age is the determining factor for the occurrence of
370
acute symptomatic seizures in a variety of metabolic or central nervous system injuries in their study on epileptic seizures. In 2024, Grau-Lopez etal. [154] summarized the etiology of acute symptomatic epilepsy and proposed prevention and
treatment methods, which increased the recognition of acute symptomatic epilepsy.
Q. Wang et al.
3.6.3 Etiology ofAcute Symptomatic Epilepsy
The occurrence of acute symptomatic epilepsy is mainly related to brain dysfunction, and the causes of brain dysfunction can be divided into two categories: structural and nonstructural damage. Acute symptomatic epilepsy was rst reported by
Feikes etal. [150], who reported that hyponatremia after surgery could cause acute
symptomatic epilepsy. In 2024, Grau-Lopez etal. [154] analyzed the etiology of
symptomatic seizures after acute structural brain injury in 194 patients and reported
that the identiable etiologies included hemorrhagic stroke (44.8%), ischemic
stroke (19.5%), traumatic brain injury (18.5%), and meningoencephalitis (17%).
Subsequently, Pais-Cunha et al. [155] analyzed the etiology of cerebral venous
thrombosis and acute symptomatic epilepsy in 54 children and reported that progressive brain injury (8 patients with malignant tumors, 5 with blood disease), head
and neck infection, head trauma, intracranial hemorrhage, and cerebral infarction
were common causes. Hernandez-Prieto et al. [156] studied 165 newborns and
reported that 43 of these patients had acute symptomatic seizures, 19 patients (34%)
of whom had hypoxic-ischemic encephalopathy, and 22 patients (40%) had other
acute diseases. Of these, 6 (11%) patients were found to have genetic mutations.
Cerebral microhemorrhage (CMB) is a marker of small vessel disease and is very
common in ischemic stroke patients, but its relationship with acute symptomatic
epilepsy has not been well studied. In 2023, Lekoubou etal. [157] conducted a retrospective cohort study on hospitalized patients with precirculation ischemic stroke
complicated with microhemorrhage and reported that among 381 patients, 17
(4.5%) had seizures, and the incidence of epilepsy was three times greater in patients
with microbleeds than in those without microbleeds. These studies suggest that the
incidence of acute symptomatic epilepsy may vary by etiology, but it is not uncommon. Based on animal experiments, Karan etal. [158] conrmed that local brain
injury can cause acute symptomatic seizures.
3.6.4 Epidemiological Investigation
Tsur etal. [159] investigated 816,252 newly enlisted soldiers in Israel. In addition
to excluding soldiers with a history of epilepsy and no records of seizures, 346 soldiers experienced acute symptomatic epilepsy, and they believed that the incidence
of acute symptomatic epilepsy was 16.2 cases/100,000 cases/year. De Stefano etal.