Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care. We performed a retrospective study using neuropathology reports from the German Neuropathology Reference Center for Epilepsy Surgery at Erlangen, Germany. This cohort included 312 patients who underwent epilepsy surgery and were labeled with one or more diagnoses, including dual pathology, hippocampal sclerosis, malformation of cortical dysplasia, tumor, encephalitis, and gliosis. We modeled the diagnosis terms together with their microscopy, immunohistochemistry, anatomy, etiologies, and imaging findings using the description logic-based Web Ontology Language (OWL) in the Epilepsy and Seizure Ontology (EpSO). Three tree-based machine learning models were used to classify the neuropathology reports into one or more diagnosis classes with and without ontology-based feature engineering. We used five-fold cross validation to avoid overfitting with a fixed number of repetitions while leaving out one subset of data for testing, and we used recall, balanced accuracy, and hamming loss as performance metrics for the multi-label classification task. The epilepsy ontology-based feature engineering approach improved the performance of all the three learning models with an improvement of 35.7%, 54.5%, and 33.3% in logistics regression, random forest, and gradient tree boosting models respectively. The run time performance of all three models improved significantly with ontology-based feature engineering with gradient tree boosting model showing a 93.8% reduction in the time required for training and testing of the model. Although, all three models showed an overall improved performance across the three-performance metrics using ontology-based feature engineering, the rate of improvement was not consistent across all input features. To analyze this variation in performance, we computed feature importance scores and found that microscopy had the highest importance score across the three models, followed by imaging, immunohistochemistry, and anatomy in a decreasing order of importance scores. This study showed that ontologies have an important role in feature engineering to make heterogeneous clinical data accessible to machine learning models and also improve the performance of machine learning models in multilabel multiclass classification tasks.
Topological data analysis (TDA) is a powerful approach for investigating complex relationships in brain networks; however, its application requires substantial domain knowledge in programming, mathematics, and data science, especially in the context of data-driven approaches like machine learning (ML). To address this educational barrier, we introduce MaTiLDA, a graphical user interface that enables exploration of common representations of TDA features and their efficacy in various classical machine learning models. This user-friendly tool is the first graphical user interface built to explore TDA representations in machine learning applications. MaTiLDA provides a user-centric method for characterizing complex neural relationships using TDA techniques. To demonstrate the utility of MaTiLDA in characterizing brain network dynamics, we apply this workflow to a cohort of 4 refractory epilepsy patients and evaluate the predictive performance of various TDA feature representations in a series of ML models. The MaTiLDA application can be accessed through https://bmhinformatics.case.edu/nicworkflow/MaTiLDA
Background Early detection of sepsis in patients admitted to the emergency department (ED) is an important clinical objective to help reduce morbidity and mortality. We aimed to use data from Electronic Health Records (EHR) system to characterize the relative importance of a new biomarker called Monocyte Distribution Width (MDW) that has been recently approved by the US Food and Drug Administration (FDA) for sepsis screening in the presence of routinely available hematologic parameters and vital signs measures. Methods In this retrospective cohort study, we included ED patients admitted to the MetroHealth hospital (a large regional safety-net hospital in Cleveland, OH, USA) with suspected infection who later developed severe sepsis. All adult patients presenting to the ED were eligible for inclusion and encounters that did not have complete blood count with differential data or vital signs data were excluded. We developed seven data models and an ensemble of four high accuracy machine learning (ML) algorithms using the Sepsis-3 diagnostic criteria for validation. Using the results generated by the high accuracy ML models, we applied the Local Interpretable Model- Agnostic Explanation (LIME) and Shapley Additive Value (SHAP) post-hoc ML interpretability methods to characterize the contributions of individual hematologic parameters, including MDW, vital signs measures in screening for severe sepsis. Findings We evaluated 7071 adult patients from 303,339 adult ED visits occurring between May 1st, 2020 and August 26th, 2022. Implementation of the seven data models reflected the ED clinical workflow with incremental addition of standard complete blood count (CBC), CBC with differential, with MDW, and finally vital signs measures. Random forest and deep neural network model reported classification area under the receiver operating characteristic curve (AUC) value of up to 93% (CI 92 : 94) and 90% (CI 88 : 91) over data model with hematologic parameters and vital signs measures. We applied the LIME and SHAP ML interpretability methods on these high accuracy ML models. Both the interpretability methods were consistent in their findings that the value of MDW is grossly attenuated (low feature importance scores of 0.015 (SHAP) and 0.0004 (LIME)) in the presence of other routinely reported hematologic parameters and vital signs measures for severe sepsis detection. Interpretation Using ML interpretability methods applied to EHR data, we show that MDW can be replaced with routinely reported CBC with differential together with vital signs measures for severe sepsis screening. MDW requires specialized laboratory equipment and modification of existing care protocols; therefore, these results could guide decisions about allocation of limited resources in cost constrained care settings. Additionally, the analysis shows the practical application of ML interpretability methods in clinical decision making.
The rapid adoption of machine learning (ML) algorithms in medical disciplines has raised concerns about trust and the lack of understanding of their results. Efforts are being made to develop more interpretable models and establish guidelines for transparency and ethical use, ensuring the responsible integration of machine learning in healthcare. In this study we use two ML interpretability methods to gain insights into the dynamics of brain network interactions in epilepsy, a neurological disorder increasingly viewed as a network disorder that affects more than 60 million persons worldwide. Using high-resolution intracranial electroencephalogram (EEG) recordings from a cohort of 16 patients, combined with high-accuracy ML algorithms, we classify EEG recordings into binary classes of seizure and non-seizure, as well as multiple classes corresponding to different phases of a seizure. This study demonstrates, for the first time, that ML interpretability methods can provide new insights into the dynamics of aberrant brain networks in neurological disorders such as epilepsy. Moreover, we show that interpretability methods can effectively identify key brain regions and network connections involved in the disruptions of brain networks, such as those that occur during seizure events. These findings emphasize the importance of continued research into the integration of ML algorithms and interpretability methods in medical disciplines and enable the discovery of new insights into the dynamics of aberrant brain networks in epilepsy patient.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.