Machine Learning Techniques for Predicting the Short-Term Outcome of Resective Surgery in Lesional-Drug Resistance Epilepsy

• URL: http://arxiv.org/abs/2302.10901v1
• Date: Fri, 10 Feb 2023 13:04:47 GMT
• Title: Machine Learning Techniques for Predicting the Short-Term Outcome of Resective Surgery in Lesional-Drug Resistance Epilepsy
• Authors: Zahra Jourahmad, Jafar Mehvari Habibabadi, Houshang Moein, Reza Basiratnia, Ali Rahmani Geranqayeh, Saeed Shiry Ghidary, and Seyed-Ali Sadegh-Zadeh
• Abstract summary: Seven dif-ferent categorization algorithms were used to analyze the data. The support vector machine (SVM) with the linear kernel yielded 76.1% in terms of accuracy.
• Score: 1.759008116536278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we developed and tested machine learning models to predict epilepsy surgical outcome using noninvasive clinical and demographic data from patients. Methods: Seven dif-ferent categorization algorithms were used to analyze the data. The techniques are also evaluated using the Leave-One-Out method. For precise evaluation of the results, the parameters accuracy, precision, recall and, F1-score are calculated. Results: Our findings revealed that a machine learning-based presurgical model of patients' clinical features may accurately predict the outcome of epilepsy surgery in patients with drug-resistant lesional epilepsy. The support vector machine (SVM) with the linear kernel yielded 76.1% in terms of accuracy could predict results in 96.7% of temporal lobe epilepsy (TLE) patients and 79.5% of extratemporal lobe epilepsy (ETLE) cases using ten clinical features. Significance: To predict the outcome of epilepsy surgery, this study recommends the use of a machine learning strategy based on supervised classification and se-lection of feature subsets data mining. Progress in the development of machine learning-based prediction models offers optimism for personalised medicine access.

Related papers

• Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models [0.0]
  We present a comparative analysis of five machine learning models for the prediction of epileptic seizures using EEG data. The results of our analysis demonstrate the performance of each model in terms of accuracy. The ET model exhibited the best performance with an accuracy of 99.29%.
  arXiv  Detail & Related papers  (2023-08-06T08:50:08Z)
• Prediction of Post-Operative Renal and Pulmonary Complications Using Transformers [69.81176740997175]
  We evaluate the performance of transformer-based models in predicting postoperative acute renal failure, pulmonary complications, and postoperative in-hospital mortality. Our results demonstrate that transformer-based models can achieve superior performance in predicting postoperative complications and outperform traditional machine learning models.
  arXiv  Detail & Related papers  (2023-06-01T14:08:05Z)
• This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text [56.32427751440426]
  In clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention. We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines.
  arXiv  Detail & Related papers  (2022-10-16T10:12:07Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)
• Literature-Augmented Clinical Outcome Prediction [10.46990394710927]
  We introduce techniques to help bridge this gap between EBM and AI-based clinical models. We propose a novel system that automatically retrieves patient-specific literature based on intensive care (ICU) patient information. Our model is able to substantially boost predictive accuracy on three challenging tasks in comparison to strong recent baselines.
  arXiv  Detail & Related papers  (2021-11-16T11:19:02Z)
• Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks [0.0]
  Time series medical generative adversarial networks (GANs) were developed to generate synthetic wound prognosis factors. Conditional training strategies were utilized to enhance training and generate classified data in terms of healing or non-healing.
  arXiv  Detail & Related papers  (2021-05-03T20:26:48Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Patient-independent Epileptic Seizure Prediction using Deep Learning Models [39.19336481493405]
  The purpose of a seizure prediction system is to successfully identify the pre-ictal brain stage, which occurs before a seizure event. Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset. We propose two patient-independent deep learning architectures with different learning strategies that can learn a global function utilizing data from multiple subjects.
  arXiv  Detail & Related papers  (2020-11-18T23:13:48Z)
• A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data [5.844828229178025]
  Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission.
  arXiv  Detail & Related papers  (2020-11-18T15:56:28Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.