Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance

Alim-Marvasti, Ali; Pérez‐García, Fernando; Dahele, Karan; Romagnoli, Gloria; Diehl, Beate; Sparks, Rachel; Ourselin, Sébastien; Clarkson, Matthew J.; Duncan, John S.

doi:10.3389/fdgth.2021.559103

Cited by 12 publications

(9 citation statements)

References 32 publications

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“…Most of the included studies exploited NLP techniques to identify—and discriminate between—patients based on their documented clinical history and conditions 16–30 . The process often entailed classifying patient reports or interviews into predefined categories based on the prominent differences in textual features.…”

Section: Resultsmentioning

confidence: 99%

Transforming epilepsy research: A systematic review on natural language processing applications

et al. 2022

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Epilepsy is one of the most common neurological disorders worldwide, with significant physical, cognitive, and psychiatric comorbidities. 1 Clinical challenges related to diagnosis and treatment options in epilepsy are only partly met by improvements in ancillary investigations, such as electroencephalography (EEG), magnetic resonance imaging, and genetic testing. 2 Taking history and clinical follow-up will remain an indispensable part of diagnosing

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Section: Resultsmentioning

confidence: 99%

Transforming epilepsy research: A systematic review on natural language processing applications

et al. 2022

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“…Fourteen early fusion studies evaluated their fusion models’ performance against that of single modality models 12 , 13 , 15 , 16 , 18 , 25 , 32 – 34 , 36 , 41 – 44 , 51 . As a result, 13 of these studies exhibited a better performance for fusion when compared with their imaging-only and clinical-only counterparts 12 , 13 , 15 , 16 , 18 , 25 , 32 – 34 , 41 – 44 , 51 .…”

Section: Resultsmentioning

confidence: 99%

“…The study concatenated MRI extracted features with demographic and neuropsychological biomarkers before feeding them to an SVM model for prediction. Ali et al 34 proposed a model to predict Epileptogenic-Zone in the Temporal Lobe by feeding MRI extracted features integrated with set-of-semiology features into various ML models such as LR, SVM, and Gradient Boosting. Ma et al 41 fused MRI and clinicopathological features for predicting metachronous distant metastasis (DM) in breast cancer.…”

Section: Resultsmentioning

confidence: 99%

“…In these studies, EHRs were fused with medical imaging to predict different outcomes, including disease prediction, mortality prediction, survival prediction, and treatment outcome prediction. Ten studies of the prediction tasks were disease prediction 12,17,34,38,39,41,44,46,48,52 , which involved determining whether an individual might develop a given disease in the future. The second most common prediction task was treatment outcome prediction reported in 2 studies 35,47 , followed by one study for mortality prediction and overall survival prediction 25,43 , respectively.…”

Section: Early Predictionmentioning

confidence: 99%

“…The early fusion technique was used in 6 studies 12,34,41,44,48,52 for disease prediction. Minhas et al 12 proposed an early fusion model to predict which subjects will progress from MCI to AD in the future.…”

Section: Early Predictionmentioning

confidence: 99%

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Artificial intelligence-based methods for fusion of electronic health records and imaging data

Mohsen

Ali

Hajj

et al. 2022

Sci Rep

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Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. The most important question when using multimodal data is how to fuse them—a field of growing interest among researchers. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different clinical applications. More specifically, we focus on studies that only fused EHR with medical imaging data to develop various AI methods for clinical applications. We present a comprehensive analysis of the various fusion strategies, the diseases and clinical outcomes for which multimodal fusion was used, the ML algorithms used to perform multimodal fusion for each clinical application, and the available multimodal medical datasets. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched Embase, PubMed, Scopus, and Google Scholar to retrieve relevant studies. After pre-processing and screening, we extracted data from 34 studies that fulfilled the inclusion criteria. We found that studies fusing imaging data with EHR are increasing and doubling from 2020 to 2021. In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies). We found that multimodality fusion models outperformed traditional single-modality models for the same task. Disease diagnosis and prediction were the most common clinical outcomes (reported in 20 and 10 studies, respectively) from a clinical outcome perspective. Neurological disorders were the dominant category (16 studies). From an AI perspective, conventional ML models were the most used (19 studies), followed by DL models (16 studies). Multimodal data used in the included studies were mostly from private repositories (21 studies). Through this scoping review, we offer new insights for researchers interested in knowing the current state of knowledge within this research field.

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Identifying epilepsy surgery referral candidates with natural language processing in an Australian context

Tan,

Goh,

et al. 2024

Epilepsia Open

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ObjectiveEpilepsy surgery is known to be underutilized. Machine learning‐natural language processing (ML‐NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation.MethodsData were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of “epilepsy” was mentioned. Individual case note review was undertaken to characterize the nature of the diagnoses discussed in these notes, and whether those with epilepsy fulfilled prespecified criteria for epilepsy surgery workup (namely focal drug refractory epilepsy without contraindications). ML‐NLP algorithms were then developed using fivefold cross‐validation on the first free‐text clinic note for each patient to identify these criteria.ResultsThere were 457 notes included in the study, of which 250 patients had epilepsy. There were 37 (14.8%) individuals who fulfilled the prespecified criteria for epilepsy surgery referral without described contraindications, 32 (12.8%) of whom were not referred for epilepsy surgical evaluation in the given clinic visit. In the prediction of suitability for epilepsy surgery workup using the prespecified criteria, the tested models performed similarly. For example, the random forest model returned an area under the receiver operator characteristic curve of 0.97 (95% confidence interval 0.93–1.0) for this task, sensitivity of 1.0, and specificity of 0.93.SignificanceThis study has shown that there are patients in tertiary hospitals in South Australia who fulfill prespecified criteria for epilepsy surgery evaluation who may not have been referred for such evaluation. ML‐NLP may assist with the identification of patients suitable for such referral.Plain Language SummaryEpilepsy surgery is a beneficial treatment for selected individuals with drug‐resistant epilepsy. However, it is vastly underutilized. One reason for this underutilization is a lack of prompt referral of possible epilepsy surgery candidates to comprehensive epilepsy centers. Natural language processing, coupled with machine learning, may be able to identify possible epilepsy surgery candidates through the analysis of unstructured clinic notes. This study, conducted in two tertiary hospitals in South Australia, demonstrated that there are individuals who fulfill criteria for epilepsy surgery evaluation referral but have not yet been referred. Machine learning‐natural language processing demonstrates promising results in assisting with the identification of such suitable candidates in Australia.

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Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance

Cited by 12 publications

References 32 publications

Transforming epilepsy research: A systematic review on natural language processing applications

Transforming epilepsy research: A systematic review on natural language processing applications

Artificial intelligence-based methods for fusion of electronic health records and imaging data

Identifying epilepsy surgery referral candidates with natural language processing in an Australian context

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