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Article ; Online: Developing a Smartwatch-Based Healthcare Application: Notes to Consider.

Ramezani, Ramin / Cao, Minh / Earthperson, Arjun / Naeim, Arash

2023 Volume 23, Issue 15

Abstract: Wearable devices and fitness trackers have gained popularity in healthcare and telemedicine as tools to reduce hospitalization costs, improve personalized health management, and monitor patients in remote areas. Smartwatches, particularly, offer ... ...

Abstract	Wearable devices and fitness trackers have gained popularity in healthcare and telemedicine as tools to reduce hospitalization costs, improve personalized health management, and monitor patients in remote areas. Smartwatches, particularly, offer continuous monitoring capabilities through step counting, heart rate tracking, and activity monitoring. However, despite being recognized as an emerging technology, the adoption of smartwatches in patient monitoring systems is still at an early stage, with limited studies delving beyond their feasibility. Developing healthcare applications for smartwatches faces challenges such as short battery life, wearable comfort, patient compliance, termination of non-native applications, user interaction difficulties, small touch screens, personalized sensor configuration, and connectivity with other devices. This paper presents a case study on designing an Android smartwatch application for remote monitoring of geriatric patients. It highlights obstacles encountered during app development and offers insights into design decisions and implementation details. The aim is to assist programmers in developing more efficient healthcare applications for wearable systems.
MeSH term(s)	Humans ; Aged ; Mobile Applications ; Wearable Electronic Devices ; Fitness Trackers ; Monitoring, Physiologic ; Telemedicine
Language	English
Publishing date	2023-07-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23156652
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Remote Monitoring of Patients With Hematologic Malignancies at High Risk of Febrile Neutropenia: Exploratory Study.

Kroloff, Maxwell / Ramezani, Ramin / Wilhalme, Holly / Naeim, Arash

JMIR formative research

2022 Volume 6, Issue 1, Page(s) e33265

Abstract: Background: Febrile neutropenia is one of the most common oncologic emergencies and is associated with significant, preventable morbidity and mortality. Most patients who experience a febrile neutropenia episode are hospitalized, resulting in ... ...

Abstract	Background: Febrile neutropenia is one of the most common oncologic emergencies and is associated with significant, preventable morbidity and mortality. Most patients who experience a febrile neutropenia episode are hospitalized, resulting in significant economic cost. Objective: This exploratory study implemented a remote monitoring system comprising a digital infrared thermometer and a pulse oximeter with the capability to notify providers in real time of abnormalities in vital signs that could suggest early clinical deterioration and thereby improve clinical outcomes. Methods: The remote monitoring system was implemented and compared to standard-of-care vital signs monitoring in hospitalized patients with underlying hematologic malignancies complicated by a febrile neutropenia episode in order to assess the feasibility and validity of the system. Statistical analysis was performed using the intraclass correlation coefficient (ICC) to assess the consistency between the measurements taken using traditional methods and those taken with the remote monitoring system for each of the vital sign parameters (temperature, heart rate, and oxygen saturation). A linear mixed-effects model with a random subject effect was used to estimate the variance components. Bland-Altman plots were created for the parameters to further delineate the direction of any occurring bias. Results: A total of 23 patients were enrolled in the study (mean age 56, SD 23-75 years; male patients: n=11, 47.8%). ICC analysis confirmed the high repeatability and accuracy of the heart rate assessment (ICC=0.856), acting as a supplement to remote temperature assessment. While the sensitivity and specificity for capturing tachycardia above a rate of 100 bpm were excellent (88% and 97%, respectively), the sensitivity of the remote monitoring system in capturing temperatures >37.8 °C and oxygen saturation <92% was 45% and 50%, respectively. Conclusions: Overall, this novel approach using temperature, heart rate, and oxygen saturation assessments successfully provided real-time, clinically valuable feedback to providers. While temperature and oxygen saturation assessments lagged in terms of sensitivity compared to a standard in-hospital system, the heart rate assessment provided highly accurate complementary data. As a whole, the system provided additional information that can be applied to a clinically vulnerable population. By transitioning its application to high-risk patients in the outpatient setting, this system can help prevent additional use of health care services through early provider intervention and potentially improve outcomes.
Language	English
Publishing date	2022-01-25
Publishing country	Canada
Document type	Journal Article
ISSN	2561-326X
ISSN (online)	2561-326X
DOI	10.2196/33265
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Data generation using simulation technology to improve perception mechanism of autonomous vehicles

Cao, Minh / Ramezani, Ramin

2022

Abstract: Recent advancements in computer graphics technology allow more realistic ren-dering of car driving environments. They have enabled self-driving car simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large amounts of synthetic data ... ...

Abstract	Recent advancements in computer graphics technology allow more realistic ren-dering of car driving environments. They have enabled self-driving car simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large amounts of synthetic data that can complement the existing real-world dataset in training autonomous car perception. Furthermore, since self-driving car simulators allow full control of the environment, they can generate dangerous driving scenarios that the real-world dataset lacks such as bad weather and accident scenarios. In this paper, we will demonstrate the effectiveness of combining data gathered from the real world with data generated in the simulated world to train perception systems on object detection and localization task. We will also propose a multi-level deep learning perception framework that aims to emulate a human learning experience in which a series of tasks from the simple to more difficult ones are learned in a certain domain. The autonomous car perceptron can learn from easy-to-drive scenarios to more challenging ones customized by simulation software. Comment: 16 pages, 7 figures, 2 tables, submitted to CONF-CDS 2022
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	629
Publishing date	2022-06-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A developed composite hard-gelatin capsules: delayed-release enteric properties

Nezhad Mohseni, Mozhgan / Najafpour-Darzi, Ghasem / Ramezani, Ramin / Jahani, Azin

Heliyon. 2022 Dec., v. 8, no. 12 p.e12265-

2022

Abstract: Present study focused on improvement of the formulation of conventional hard gelatin capsules using gastric acid-resistant polymers. We have utilized the same approach of making conventional drug capsules to develop novel capsules with delayed release ... ...

Abstract	Present study focused on improvement of the formulation of conventional hard gelatin capsules using gastric acid-resistant polymers. We have utilized the same approach of making conventional drug capsules to develop novel capsules with delayed release properties. For this purpose, delayed-release capsules were produced by improving the formulation of hard gelatin capsules. In addition, the effect of adding intestinal polymers such as Hydroxy propyl methyl cellulose phthalate, Glucomannan, and Polyvinyl alcohol to hard gelatin capsules were investigated. The capsules' release rate was determined. The degradation tests in an acidic environment were performed and the results were recorded. In fact, the delayed-release hard gelatin capsules pass through the stomach with small amount of the drug release; but their shell remains intact and dissolves as it enters the intestine environment. This article shows that enteric polymers with out interactions, only by changing the formulations will have delayed release properties. this makes sensitive drugs pass through stomach environment and have higher absorption.
Keywords	absorption ; acid tolerance ; drugs ; gelatin ; glucomannans ; intestines ; methylcellulose ; phthalates ; polyvinyl alcohol ; stomach ; Delayed-release ; Hard gelatin capsules ; Hydroxy propyl methyl cellulose phthalate ; Glucomannan
Language	English
Dates of publication	2022-12
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2022.e12265
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Machine learning-based modeling of acute respiratory failure following emergency general surgery operations.

Hadaya, Joseph / Verma, Arjun / Sanaiha, Yas / Ramezani, Ramin / Qadir, Nida / Benharash, Peyman

PloS one

2022 Volume 17, Issue 4, Page(s) e0267733

Abstract: Background: Emergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, ... ...

Abstract	Background: Emergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored. Objective: The present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to traditional regression models using a nationally-representative cohort. Methods: Non-elective hospitalizations for EGS (appendectomy, cholecystectomy, repair of perforated ulcer, large or small bowel resection, lysis of adhesions) were identified in the 2016-18 Nationwide Readmissions Database. Factors associated with PRF were identified using ML techniques and logistic regression. The performance of XGBoost and logistic regression was evaluated using the receiver operating characteristic curve and coefficient of determination (R2). The impact of PRF on mortality, length of stay (LOS) and hospitalization costs was secondarily assessed using generalized linear models. Results: Of 1,003,703 hospitalizations, 8.8% developed PRF. The XGBoost model exhibited slightly superior discrimination compared to logistic regression (0.900, 95% CI 0.899-0.901 vs 0.894, 95% CI 0.862-0.896). Compared to logistic regression, XGBoost demonstrated excellent calibration across all risk levels (R2: 0.998 vs 0.962). Congestive heart failure, neurologic disorders, and coagulopathy were significantly associated with increased risk of PRF. After risk-adjustment, PRF was associated with 10-fold greater odds (95% confidence interval (CI) 9.8-11.1) of mortality and incremental increases in LOS by 3.1 days (95% CI 3.0-3.2) and $11,900 (95% CI 11,600-12,300) in costs. Conclusions: Logistic regression and XGBoost perform similarly in overall classification of PRF risk. However, due to superior calibration at extremes of risk, ML-based models may prove more useful in the clinical setting, where probabilities rather than classifications are desired.
MeSH term(s)	Humans ; Logistic Models ; Machine Learning ; ROC Curve ; Respiratory Distress Syndrome ; Respiratory Insufficiency
Language	English
Publishing date	2022-04-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0267733
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Temporal convolutional networks and data rebalancing for clinical length of stay and mortality prediction.

Bednarski, Bryan P / Singh, Akash Deep / Zhang, Wenhao / Jones, William M / Naeim, Arash / Ramezani, Ramin

Scientific reports

2022 Volume 12, Issue 1, Page(s) 21247

Abstract: It is critical for hospitals to accurately predict patient length of stay (LOS) and mortality in real-time. We evaluate temporal convolutional networks (TCNs) and data rebalancing methods to predict LOS and mortality. This is a retrospective cohort study ...

Abstract	It is critical for hospitals to accurately predict patient length of stay (LOS) and mortality in real-time. We evaluate temporal convolutional networks (TCNs) and data rebalancing methods to predict LOS and mortality. This is a retrospective cohort study utilizing the MIMIC-III database. The MIMIC-Extract pipeline processes 24 hour time-series clinical objective data for 23,944 unique patient records. TCN performance is compared to both baseline and state-of-the-art machine learning models including logistic regression, random forest, gated recurrent unit with decay (GRU-D). Models are evaluated for binary classification tasks (LOS > 3 days, LOS > 7 days, mortality in-hospital, and mortality in-ICU) with and without data rebalancing and analyzed for clinical runtime feasibility. Data is split temporally, and evaluations utilize tenfold cross-validation (stratified splits) followed by simulated prospective hold-out validation. In mortality tasks, TCN outperforms baselines in 6 of 8 metrics (area under receiver operating characteristic, area under precision-recall curve (AUPRC), and F-1 measure for in-hospital mortality; AUPRC, accuracy, and F-1 for in-ICU mortality). In LOS tasks, TCN performs competitively to the GRU-D (best in 6 of 8) and the random forest model (best in 2 of 8). Rebalancing improves predictive power across multiple methods and outcome ratios. The TCN offers strong performance in mortality classification and offers improved computational efficiency on GPU-enabled systems over popular RNN architectures. Dataset rebalancing can improve model predictive power in imbalanced learning. We conclude that temporal convolutional networks should be included in model searches for critical care outcome prediction systems.
MeSH term(s)	Humans ; Prospective Studies ; Retrospective Studies
Language	English
Publishing date	2022-12-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-25472-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Causal Inference in medicine and in health policy, a summary

Zhang, Wenhao / Ramezani, Ramin / Naeim, Arash

2021

Abstract: A data science task can be deemed as making sense of the data or testing a hypothesis about it. The conclusions inferred from data can greatly guide us to make informative decisions. Big data has enabled us to carry out countless prediction tasks in ... ...

Abstract	A data science task can be deemed as making sense of the data or testing a hypothesis about it. The conclusions inferred from data can greatly guide us to make informative decisions. Big data has enabled us to carry out countless prediction tasks in conjunction with machine learning, such as identifying high risk patients suffering from a certain disease and taking preventable measures. However, healthcare practitioners are not content with mere predictions - they are also interested in the cause-effect relation between input features and clinical outcomes. Understanding such relations will help doctors treat patients and reduce the risk effectively. Causality is typically identified by randomized controlled trials. Often such trials are not feasible when scientists and researchers turn to observational studies and attempt to draw inferences. However, observational studies may also be affected by selection and/or confounding biases that can result in wrong causal conclusions. In this chapter, we will try to highlight some of the drawbacks that may arise in traditional machine learning and statistical approaches to analyze the observational data, particularly in the healthcare data analytics domain. We will discuss causal inference and ways to discover the cause-effect from observational studies in healthcare domain. Moreover, we will demonstrate the applications of causal inference in tackling some common machine learning issues such as missing data and model transportability. Finally, we will discuss the possibility of integrating reinforcement learning with causality as a way to counter confounding bias. Comment: 31 pages, 17 figures, to appear in the second edition of the handbook of computational intelligence
Keywords	Computer Science - Machine Learning
Subject code	501
Publishing date	2021-05-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Parsimonious machine learning models to predict resource use in cardiac surgery across a statewide collaborative.

Verma, Arjun / Sanaiha, Yas / Hadaya, Joseph / Maltagliati, Anthony Jason / Tran, Zachary / Ramezani, Ramin / Shemin, Richard J / Benharash, Peyman

JTCVS open

2022 Volume 11, Page(s) 214–228

Abstract: Objective: We sought to several develop parsimonious machine learning models to predict resource utilization and clinical outcomes following cardiac operations using only preoperative factors.: Methods: All patients undergoing coronary artery bypass ... ...

Abstract	Objective: We sought to several develop parsimonious machine learning models to predict resource utilization and clinical outcomes following cardiac operations using only preoperative factors. Methods: All patients undergoing coronary artery bypass grafting and/or valve operations were identified in the 2015-2021 University of California Cardiac Surgery Consortium repository. The primary end point of the study was length of stay (LOS). Secondary endpoints included 30-day mortality, acute kidney injury, reoperation, postoperative blood transfusion and duration of intensive care unit admission (ICU LOS). Linear regression, gradient boosted machines, random forest, extreme gradient boosting predictive models were developed. The coefficient of determination and area under the receiver operating characteristic (AUC) were used to compare models. Important predictors of increased resource use were identified using SHapley summary plots. Results: Compared with all other modeling strategies, gradient boosted machines demonstrated the greatest performance in the prediction of LOS (coefficient of determination, 0.42), ICU LOS (coefficient of determination, 0.23) and 30-day mortality (AUC, 0.69). Advancing age, reduced hematocrit, and multiple-valve procedures were associated with increased LOS and ICU LOS. Furthermore, the gradient boosted machine model best predicted acute kidney injury (AUC, 0.76), whereas random forest exhibited greatest discrimination in the prediction of postoperative transfusion (AUC, 0.73). We observed no difference in performance between modeling strategies for reoperation (AUC, 0.80). Conclusions: Our findings affirm the utility of machine learning in the estimation of resource use and clinical outcomes following cardiac operations. We identified several risk factors associated with increased resource use, which may be used to guide case scheduling in times of limited hospital capacity.
Language	English
Publishing date	2022-04-20
Publishing country	Netherlands
Document type	Journal Article
ISSN	2666-2736
ISSN (online)	2666-2736
DOI	10.1016/j.xjon.2022.04.017
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The Presence of a Cost-Volume Relationship in Robotic-assisted Thoracoscopic Lung Resections.

Verma, Arjun / Hadaya, Joseph / Richardson, Shannon / Vadlakonda, Amulya / Ramezani, Ramin / Revels, Sha'Shonda / Benharash, Peyman

Annals of surgery

2022 Volume 278, Issue 2, Page(s) e377–e381

Abstract: Objective: To characterize the relationship between institutional robotic-assisted pulmonary lobectomy volume and hospitalization costs.: Background: The high cost of robotic-assisted thoracoscopic surgery (RATS) is among several drivers of ... ...

Abstract	Objective: To characterize the relationship between institutional robotic-assisted pulmonary lobectomy volume and hospitalization costs. Background: The high cost of robotic-assisted thoracoscopic surgery (RATS) is among several drivers of hesitation among nonadopters. Studies examining the impact of institutional experience on costs of RATS lobectomy are lacking. Methods: Adults undergoing RATS lobectomy for primary lung cancers were identified from the 2016 to 2018 Nationwide Readmissions Database. A multivariable regression to model hospitalization costs was developed with the inclusion of hospital RATS lobectomy volume as restricted cubic splines. The volume corresponding to the inflection point of the spline was used to categorize hospitals as high- (HVH) or low-volume (LVH). We subsequently examined the association of HVH status with adverse events, length of stay, costs, and 30-day, nonelective readmissions. Results: An estimated 14,756 patients underwent RATS lobectomy during the study period, with median cost of $23,000. Upon adjustment for patient and operative characteristics, hospital RATS volume was inversely associated with costs. Although only 17.2% of centers were defined as HVH, 51.7% of patients were managed at these centers. Patients at HVH and LVH had similar age, sex, and distribution of comorbidities. Notably, patients at HVH had decreased risk-adjusted odds of adverse events (adjusted odds ratio: 0.62, P <0.001), as well as significantly reduced length of stay (-0.8 d, P <0.001) and costs (-$3900, P <0.001). Conclusions: Increasing hospital RATS lobectomy volume was associated with reduced hospitalization costs. Our findings suggest the presence of streamlined care pathways at high-volume centers, which influence costs of care.
MeSH term(s)	Humans ; Robotic Surgical Procedures ; Thoracic Surgery, Video-Assisted ; Pneumonectomy/adverse effects ; Length of Stay ; Lung ; Lung Neoplasms/surgery ; Retrospective Studies
Language	English
Publishing date	2022-09-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	340-2
ISSN	1528-1140 ; 0003-4932
ISSN (online)	1528-1140
ISSN	0003-4932
DOI	10.1097/SLA.0000000000005699
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Book ; Online: ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique

Cao, Minh / Zhao, Tianqi / Li, Yanxun / Zhang, Wenhao / Benharash, Peyman / Ramezani, Ramin

2022

Abstract: Electrocardiogram (ECG) is a simple non-invasive measure to identify heart-related issues such as irregular heartbeats known as arrhythmias. While artificial intelligence and machine learning is being utilized in a wide range of healthcare related ... ...

Abstract	Electrocardiogram (ECG) is a simple non-invasive measure to identify heart-related issues such as irregular heartbeats known as arrhythmias. While artificial intelligence and machine learning is being utilized in a wide range of healthcare related applications and datasets, many arrhythmia classifiers using deep learning methods have been proposed in recent years. However, sizes of the available datasets from which to build and assess machine learning models is often very small and the lack of well-annotated public ECG datasets is evident. In this paper, we propose a deep transfer learning framework that is aimed to perform classification on a small size training dataset. The proposed method is to fine-tune a general-purpose image classifier ResNet-18 with MIT-BIH arrhythmia dataset in accordance with the AAMI EC57 standard. This paper further investigates many existing deep learning models that have failed to avoid data leakage against AAMI recommendations. We compare how different data split methods impact the model performance. This comparison study implies that future work in arrhythmia classification should follow the AAMI EC57 standard when using any including MIT-BIH arrhythmia dataset. Comment: 14 pages, 5 figures, 4 tables, submitted to The 4th International Conference on Computing and Data Science (CONF-CDS 2022)
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2022-06-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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