Nazanin Esmaili

Nazanin Esmaili
University of Pittsburgh | Pitt · Industrial Engineering

Doctor of Philosophy

About

28
Publications
2,686
Reads
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268
Citations
Citations since 2016
26 Research Items
266 Citations
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2016201720182019202020212022020406080100120140
2016201720182019202020212022020406080100120140

Publications

Publications (28)
Article
Full-text available
Background Patients with pituitary lesions experience decrements in quality of life (QoL) and treatment aims to arrest or improve QoL decline. Objective To detect associations with QoL in trans-nasal endoscopic skull base surgery patients and train supervised learning classifiers to predict QoL improvement at 12 months. Methods A supervised learn...
Article
Full-text available
Background Robotic (RTKA) and computer-navigated total knee arthroplasty (CNTKA) are increasingly replacing manual techniques in orthopaedic surgery. This systematic review compared clinical outcomes associated with RTKA and CNTKA and investigated the utility of natural language processing (NLP) for the literature synthesis.MethodsA comprehensive s...
Article
Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality implications for patients. Deep learning has shown promise for facilitating improved diagnostic accur...
Preprint
Full-text available
Background: Non-contrast computed tomography of the brain (NCCTB) is commonly used in clinical practice to detect intracranial pathology but is subject to interpretation errors. Machine learning is capable of augmenting clinical decision making and there is an opportunity to apply deep learning to improve the clinical interpretation of NCCTB scans....
Article
Topic modelling is an important approach of unsupervised machine learning that allows automatically extracting the main “topics” from large collections of documents. In addition, topic modelling is able to identify the topic proportions of each individual document, which can be helpful for organizing the collections. Many topic modelling algorithms...
Chapter
Natural language processing (NLP), a domain of artificial intelligence (AI) that models human language, has been used in medicine to automate diagnostics, detect adverse events, support decision making and predict clinical outcomes. However, applications to the clinical neurosciences appear to be limited. NLP has matured with the implementation of...
Chapter
Topic modeling is an unsupervised natural language processing approach for automatically extracting the main topics from a large collection of documents, and simultaneously assigning the individual documents to the extracted topics. While many algorithms for topic modelling have been proposed in the literature, to date there has been little use of...
Article
Full-text available
Objectives To evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema an...
Article
Full-text available
Objectives Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists. Design...
Article
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI...
Article
Full-text available
Background Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretation. We therefore aimed to assess the accuracy of radiologists with and without the assistance of a de...
Article
Full-text available
Despite its simple acquisition technique, the chest X‐ray remains the most common first‐line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X‐ray interpretation. While promising, these machine learning algor...
Article
Full-text available
Topic modeling is an important application of natural language processing (NLP) that can automatically identify the set of main topics of a given, typically large, collection of documents. In addition to identifying the main topics in the given collection, topic modeling infers which combination of topics is addressed by each individual document (t...
Article
Background Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial. There is little literature on the health service utilization patterns of MVA patients. To fill this...
Preprint
Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translation of the individual sentences in the document needs to retain aspects of the discourse at document level. However, document-level translation models are usually not tra...
Article
Full-text available
Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applicat...
Article
Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurological symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to (1) investi...
Preprint
Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuo...
Article
We study an (R, s, S) inventory control policy with stochastic demand, lost sales, zero lead‐time and a target service level to be satisfied. The system is modeled as a discrete time Markov chain for which we present a novel approach to derive exact closed‐form solutions for the limiting distribution of the on‐hand inventory level at the end of a r...
Article
Full-text available
Background: Transport injuries commonly result in significant disease burden, leading to physical disability, mental health deterioration and reduced quality of life. Analyzing the patterns of healthcare service utilization after transport injuries can provide an insight into the health of the affected parties, allow improved health system resourc...
Article
We propose a mixed integer programming (MIP) model to help clinicians store medications and medical supplies optimally in space-constrained, decentralized Automated Dispensing Cabinets (ADCs) located on hospital patient floors. We also propose a second MIP model that addresses human errors associated with the selection of pharmaceuticals from floor...
Chapter
Full-text available
We propose a greedy primal-dual type heuristic to jointly optimize the selection of an inventory control policy and the allocation of shelf space in order to minimize the expected counting and replenishment costs, while accounting for space limitations. The problem is motivated by an application in the healthcare sector. It addresses the limitation...
Conference Paper
Full-text available
The frequent use of PAR levels for controlling inventories, and the associated manual effort for tracking usage and ordering replenishments leads to a great deal of inefficiency in inventory management in hospitals and clinics. These processes not only waste significant time and money but also result in numerous errors. Given that each item has its...
Chapter
Full-text available
We address the joint allocation of storage and shelf-space, using an application motivated by the management of inventory items at Outpatient Clinics (OCs). OCs are limited health care facilities that provide patients with convenient outpatient care within their own community, as opposed to having them visit a major hospital. Currently, patients wh...

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