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2022•NeurologyRequires access

A Machine Learning Approach for Identifying Factors that Contribute to Seizure Freedom Following Temporal Lobectomy for Mesial Temporal Lobe Epilepsy (S7.006)

Sara Ratican, Seo Ho Song, George Zanazzi, Jennifer Hong

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Abstract

This study utilizes a machine learning (ML) approach to analyze which clinical features, preoperative evaluations and pathologic outcomes are predictive of seizure freedom in patients undergoing temporal lobectomy for mesial temporal lobe epilepsy (MTLE).

About this research paper

What this paper is about

This study utilizes a machine learning (ML) approach to analyze which clinical features, preoperative evaluations and pathologic outcomes are predictive of seizure freedom in patients undergoing temporal lobectomy for mesial temporal lobe epilepsy (MTLE).

Why it matters

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Method / approach

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Available abstract

This study utilizes a machine learning (ML) approach to analyze which clinical features, preoperative evaluations and pathologic outcomes are predictive of seizure freedom in patients undergoing temporal lobectomy for mesial temporal lobe epilepsy (MTLE).

Key concepts: Mesial temporal lobe epilepsy, Temporal lobe, Epilepsy, Temporal lobectomy, Anterior temporal lobectomy, Neuroscience, Psychology, Medicine

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A Machine Learning Approach for Identifying Factors that Contribute to Seizure Freedom Following Temporal Lobectomy for Mesial Temporal Lobe Epilepsy (S7.006) — Research Paper | ScholarLens