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Abdollahi M, Rashedi E, Kuber PM, Jahangiri S, Kazempour B, Dombovy M, Azadeh-Fard N. Post-Stroke Functional Changes: In-Depth Analysis of Clinical Tests and Motor-Cognitive Dual-Tasking Using Wearable Sensors. Bioengineering (Basel) 2024; 11:349. [PMID: 38671771 PMCID: PMC11048064 DOI: 10.3390/bioengineering11040349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 03/26/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
Clinical tests like Timed Up and Go (TUG) facilitate the assessment of post-stroke mobility, but they lack detailed measures. In this study, 21 stroke survivors and 20 control participants underwent TUG, sit-to-stand (STS), and the 10 Meter Walk Test (10MWT). Tests incorporated single tasks (STs) and motor-cognitive dual-task (DTs) involving reverse counting from 200 in decrements of 10. Eight wearable motion sensors were placed on feet, shanks, thighs, sacrum, and sternum to record kinematic data. These data were analyzed to investigate the effects of stroke and DT conditions on the extracted features across segmented portions of the tests. The findings showed that stroke survivors (SS) took 23% longer for total TUG (p < 0.001), with 31% longer turn time (p = 0.035). TUG time increased by 20% (p < 0.001) from STs to DTs. In DTs, turning time increased by 31% (p = 0.005). Specifically, SS showed 20% lower trunk angular velocity in sit-to-stand (p = 0.003), 21% longer 10-Meter Walk time (p = 0.010), and 18% slower gait speed (p = 0.012). As expected, turning was especially challenging and worsened with divided attention. The outcomes of our study demonstrate the benefits of instrumented clinical tests and DTs in effectively identifying motor deficits post-stroke across sitting, standing, walking, and turning activities, thereby indicating that quantitative motion analysis can optimize rehabilitation procedures.
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Affiliation(s)
- Masoud Abdollahi
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (P.M.K.); (S.J.); (B.K.); (N.A.-F.)
| | - Ehsan Rashedi
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (P.M.K.); (S.J.); (B.K.); (N.A.-F.)
| | - Pranav Madhav Kuber
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (P.M.K.); (S.J.); (B.K.); (N.A.-F.)
| | - Sonia Jahangiri
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (P.M.K.); (S.J.); (B.K.); (N.A.-F.)
| | - Behnam Kazempour
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (P.M.K.); (S.J.); (B.K.); (N.A.-F.)
| | - Mary Dombovy
- Department of Rehabilitation and Neurology, Unity Hospital, Rochester, NY 14626, USA;
| | - Nasibeh Azadeh-Fard
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (P.M.K.); (S.J.); (B.K.); (N.A.-F.)
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Jahangiri S, Abdollahi M, Rashedi E, Azadeh-Fard N. A machine learning model to predict heart failure readmission: toward optimal feature set. Front Artif Intell 2024; 7:1363226. [PMID: 38449791 PMCID: PMC10915081 DOI: 10.3389/frai.2024.1363226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Background Hospital readmissions for heart failure patients remain high despite efforts to reduce them. Predictive modeling using big data provides opportunities to identify high-risk patients and inform care management. However, large datasets can constrain performance. Objective This study aimed to develop a machine learning based prediction model leveraging a nationwide hospitalization database to predict 30-day heart failure readmissions. Another objective of this study is to find the optimal feature set that leads to the highest AUC value in the prediction model. Material and methods Heart failure patient data was extracted from the 2020 Nationwide Readmissions Database. A heuristic feature selection process incrementally incorporated predictors into logistic regression and random forest models, which yields a maximum increase in the AUC metric. Discrimination was evaluated through accuracy, sensitivity, specificity and AUC. Results A total of 566,019 discharges with heart failure diagnosis were recognized. Readmission rate was 8.9% for same-cause and 20.6% for all-cause diagnoses. Random forest outperformed logistic regression, achieving AUCs of 0.607 and 0.576 for same-cause and all-cause readmissions respectively. Heuristic feature selection resulted in the identification of optimal feature sets including 20 and 22 variables from a pool of 30 and 31 features for the same-cause and all-cause datasets. Key predictors included age, payment method, chronic kidney disease, disposition status, number of ICD-10-CM diagnoses, and post-care encounters. Conclusion The proposed model attained discrimination comparable to prior analyses that used smaller datasets. However, reducing the sample enhanced performance, indicating big data complexity. Improved techniques like heuristic feature selection enabled effective leveraging of the nationwide data. This study provides meaningful insights into predictive modeling methodologies and influential features for forecasting heart failure readmissions.
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Affiliation(s)
- Sonia Jahangiri
- Industrial and Systems Engineering Department, Rochester Institute of Technology, Rochester, NY, United States
| | - Masoud Abdollahi
- Industrial and Systems Engineering Department, Rochester Institute of Technology, Rochester, NY, United States
| | - Ehsan Rashedi
- Industrial and Systems Engineering Department, Rochester Institute of Technology, Rochester, NY, United States
| | - Nasibeh Azadeh-Fard
- Industrial and Systems Engineering Department, Rochester Institute of Technology, Rochester, NY, United States
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Abdollahi M, Rashedi E, Jahangiri S, Kuber PM, Azadeh-Fard N, Dombovy M. Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking. Sensors (Basel) 2024; 24:812. [PMID: 38339529 PMCID: PMC10857540 DOI: 10.3390/s24030812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/09/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy. OBJECTIVE Develop machine learning models using inertial sensors to objectively classify fall risk in stroke survivors. Determine optimal sensor configurations and clinical test protocols. METHODS 21 stroke survivors performed balance, Timed Up and Go, 10 Meter Walk, and Sit-to-Stand tests with and without dual-tasking. A total of 8 motion sensors captured lower limb and trunk kinematics, and 92 spatiotemporal gait and clinical features were extracted. Supervised models-Support Vector Machine, Logistic Regression, and Random Forest-were implemented to classify high vs. low fall risk. Sensor setups and test combinations were evaluated. RESULTS The Random Forest model achieved 91% accuracy using dual-task balance sway and Timed Up and Go walk time features. Single thorax sensor models performed similarly to multi-sensor models. Balance and Timed Up and Go best-predicted fall risk. CONCLUSION Machine learning models using minimal inertial sensors during clinical assessments can accurately quantify fall risk in stroke survivors. Single thorax sensor setups are effective. Findings demonstrate a feasible objective fall screening approach to assist rehabilitation.
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Affiliation(s)
- Masoud Abdollahi
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (S.J.); (P.M.K.); (N.A.-F.)
| | - Ehsan Rashedi
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (S.J.); (P.M.K.); (N.A.-F.)
| | - Sonia Jahangiri
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (S.J.); (P.M.K.); (N.A.-F.)
| | - Pranav Madhav Kuber
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (S.J.); (P.M.K.); (N.A.-F.)
| | - Nasibeh Azadeh-Fard
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA; (M.A.); (S.J.); (P.M.K.); (N.A.-F.)
| | - Mary Dombovy
- Department of Rehabilitation and Neurology, Unity Hospital, Rochester, NY 14626, USA;
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Samidoust P, Esmaeili Delshad MS, Navid Talemi R, Mojtahedi K, Samidoust A, Jahangiri S, Ashoobi MT. Incidence, characteristics, and outcome of COVID-19 in patients on liver transplant program: a retrospective study in the north of Iran. New Microbes New Infect 2021; 44:100935. [PMID: 34493955 PMCID: PMC8413100 DOI: 10.1016/j.nmni.2021.100935] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 07/29/2021] [Accepted: 08/09/2021] [Indexed: 12/12/2022] Open
Abstract
The risk of severe coronavirus disease-2019 (COVID-19) disease seems to be higher in individuals with solid organ transplantation. Therefore, the purpose of the present research is to investigate the incidence of COVID-19 and laboratory data and epidemiologic factors in liver transplant recipients and the patients on the waiting list for liver transplantation. In this study, we evaluated the records of patients on the waiting list for liver transplantation and of recipients of a liver transplant. Demographic data, underlying disease, history of drug use and participants' outcomes were collected. The diagnosis of SARS-CoV-2 infection for all patients was confirmed using a nasopharyngeal swab specimen with real-time RT-PCR. During the study period, 172 patients were enrolled, among whom 85 patients (49.4%) were on the waiting list for liver transplantation, and 87 patients (50.6%) were recipients of a liver transplant. Out of them, 10 (5.8%) had a positive result for SARS-CoV-2. Of these patients, 7.05% (6/85) and 4.6% (4/87) of patients on the waiting list and recipients of liver transplants were positive for SARS-CoV-2, respectively. Patients on the waiting list with COVID-19 infection had a higher median of albumin, ALT, AST, TBIL, DBIL, HDL and LDL value. In summary, the incidence of COVID-19 in liver transplant patients was slightly higher. The existence of underlying liver diseases should be well known as one of the poor predictive factors for worse outcomes in patients with COVID-19. So, comparative studies are recommended to identify risk factors for COVID-19 in patients with liver injury.
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Key Words
- ACE2, angiotensin-converting enzyme-2
- AST, aspartate aminotransferase
- CDC, Centers for Disease Control
- COVID-19
- CTscan, computed tomography scan
- DBIL, direct bilirubin
- HBV, Hepatitis B
- HDL, High-density lipoprotein
- Iran
- NASH, Non-alcoholic steatohepatitis
- NSAIDs, nonsteroidal anti-inflammatory drugs
- PSC, primary sclerosing cholangitis
- SARS-CoV-2
- SARS-CoV-2, severe acute respiratory syndrome coronavirus 2
- SD, standard deviation
- TBIL, total bilirubin
- liver transplant recipients
- liver transplantation
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Affiliation(s)
- P Samidoust
- Razi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | - M S Esmaeili Delshad
- Razi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | - R Navid Talemi
- Razi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | - K Mojtahedi
- Gastrointestinal and Liver Diseases Research Center, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | - A Samidoust
- Razi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | - S Jahangiri
- Razi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | - M T Ashoobi
- Razi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
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Arrazola JM, Bergholm V, Brádler K, Bromley TR, Collins MJ, Dhand I, Fumagalli A, Gerrits T, Goussev A, Helt LG, Hundal J, Isacsson T, Israel RB, Izaac J, Jahangiri S, Janik R, Killoran N, Kumar SP, Lavoie J, Lita AE, Mahler DH, Menotti M, Morrison B, Nam SW, Neuhaus L, Qi HY, Quesada N, Repingon A, Sabapathy KK, Schuld M, Su D, Swinarton J, Száva A, Tan K, Tan P, Vaidya VD, Vernon Z, Zabaneh Z, Zhang Y. Quantum circuits with many photons on a programmable nanophotonic chip. Nature 2021; 591:54-60. [PMID: 33658692 PMCID: PMC11008968 DOI: 10.1038/s41586-021-03202-1] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 01/04/2021] [Indexed: 01/31/2023]
Abstract
Growing interest in quantum computing for practical applications has led to a surge in the availability of programmable machines for executing quantum algorithms1,2. Present-day photonic quantum computers3-7 have been limited either to non-deterministic operation, low photon numbers and rates, or fixed random gate sequences. Here we introduce a full-stack hardware-software system for executing many-photon quantum circuit operations using integrated nanophotonics: a programmable chip, operating at room temperature and interfaced with a fully automated control system. The system enables remote users to execute quantum algorithms that require up to eight modes of strongly squeezed vacuum initialized as two-mode squeezed states in single temporal modes, a fully general and programmable four-mode interferometer, and photon number-resolving readout on all outputs. Detection of multi-photon events with photon numbers and rates exceeding any previous programmable quantum optical demonstration is made possible by strong squeezing and high sampling rates. We verify the non-classicality of the device output, and use the platform to carry out proof-of-principle demonstrations of three quantum algorithms: Gaussian boson sampling, molecular vibronic spectra and graph similarity8. These demonstrations validate the platform as a launchpad for scaling photonic technologies for quantum information processing.
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Affiliation(s)
| | | | | | | | | | - I Dhand
- Xanadu, Toronto, Ontario, Canada
| | | | - T Gerrits
- National Institute of Standards and Technology, Boulder, CO, USA
| | | | - L G Helt
- Xanadu, Toronto, Ontario, Canada
| | - J Hundal
- Xanadu, Toronto, Ontario, Canada
| | | | | | - J Izaac
- Xanadu, Toronto, Ontario, Canada
| | | | - R Janik
- Xanadu, Toronto, Ontario, Canada
| | | | | | - J Lavoie
- Xanadu, Toronto, Ontario, Canada
| | - A E Lita
- National Institute of Standards and Technology, Boulder, CO, USA
| | | | | | | | - S W Nam
- National Institute of Standards and Technology, Boulder, CO, USA
| | | | - H Y Qi
- Xanadu, Toronto, Ontario, Canada
| | | | | | | | - M Schuld
- Xanadu, Toronto, Ontario, Canada
| | - D Su
- Xanadu, Toronto, Ontario, Canada
| | | | - A Száva
- Xanadu, Toronto, Ontario, Canada
| | - K Tan
- Xanadu, Toronto, Ontario, Canada
| | - P Tan
- Xanadu, Toronto, Ontario, Canada
| | | | - Z Vernon
- Xanadu, Toronto, Ontario, Canada.
| | | | - Y Zhang
- Xanadu, Toronto, Ontario, Canada
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Ghiam AF, Taeb S, Huang X, Jahangiri S, Ray J, Hoey C, Fokas E, Vesprini D, Bristow R, Boutros P, Liu S. The Biological Role and Clinical Significance of Long Noncoding RNA Urothelial Carcinoma Associated 1 (UCA1) in Prostate Cancer (PCa). Int J Radiat Oncol Biol Phys 2016. [DOI: 10.1016/j.ijrobp.2016.06.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Salehzadeh F, Jafariasl M, Jahangiri S, Hosseiniasl S. P01-004 – MEFV genes and FMF. Pediatr Rheumatol Online J 2013. [PMCID: PMC3952680 DOI: 10.1186/1546-0096-11-s1-a8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Salehzadeh F, Yasrebi O, Jahangiri S, Hosseiniasl S. P01-005 – Idiopathic uveitis and FMF. Pediatr Rheumatol Online J 2013. [PMCID: PMC3953228 DOI: 10.1186/1546-0096-11-s1-a9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Salehzadeh F, Vahedi M, Jahangiri S, Hosseiniasl S. P03-001 - PFAPA and MEFV genes. Pediatr Rheumatol Online J 2013. [PMCID: PMC3952435 DOI: 10.1186/1546-0096-11-s1-a196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Jahangiri S, Talebi M, Eslami G, Pourshafie MR. Prevalence of virulence factors and antibiotic resistance in vancomycin-resistant Enterococcus faecium isolated from sewage and clinical samples in Iran. Indian J Med Microbiol 2010; 28:337-41. [DOI: 10.4103/0255-0857.71828] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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Salehzadeh F, Jahangiri S, Emami D, Emami D, Shiari R. Familial Mediterranean fever in Iranian children. First report from Iran. Clin Exp Rheumatol 2010; 28:287. [PMID: 20483055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2010] [Accepted: 05/14/2010] [Indexed: 05/29/2023]
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Jahangiri S, Talebi M, Eslami G, Pourshafie M. Analysis of Putative Virulence Factors in Enterococcus faecium Isolated from Different Sources in Iran. Int J Infect Dis 2008. [DOI: 10.1016/j.ijid.2008.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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Jahangiri S, Taghikhani M, Behnejad H, Ahmadi S. Theoretical investigation of imidazolium based ionic liquid/alcohol mixture: a molecular dynamic simulation. Mol Phys 2008. [DOI: 10.1080/00268970802068495] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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