Publications by authors named "Ahmad Reza Pourghaderi"

6 Publications

  • Page 1 of 1

Machine learning-based demand forecasting in cancer palliative care home hospitalization.

J Biomed Inform 2022 Apr 28:104075. Epub 2022 Apr 28.

Health Systems Research Center (HSRC), Singapore Health Services, 31 Third Hospital Avenue, Singapore 168753, Singapore; Health Services and Systems Research (HSSR), Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore; Ala Cancer Prevention and Control Center (MACSA), Isfahan 8197113766, Iran. Electronic address:

Objective: To develop an effective Management Information System (MIS) that is empowered by predictive models that can forecast the demand of end-stage cancer home hospitalized patients in individual and population levels, and help palliative care service systems operate smoothly where the demand is highly fluctuating, resources are limited, expensive, and hardly adjustable in a short time, and the backlog and shortage costs are high.

Method: Inspired by real problems faced by a palliative care center providing various medical, nursing, psychological, and social services in a home-based setting, two Long Short-Term Memory (LSTM) based deep learning models are proposed for demand forecasting at both individual and population levels. The individual-level model can predict the type and time of the next service required for a specific patient with a given demographic and health profile, and the population-level model helps with the prediction of next week's demand for various services in a center supporting a specific patient population. Predicted demand informs on optimal resource and operations plan through a well designed MIS.

Results: Experiments were conducted on a dataset consisting of more than 4000 cancer patients with a Palliative Performance Scale (PPS) of 40 and below discharged from hospital to home under a national palliative care center's home hospitalization service in Iran from September 2012 to July 2019. The models outperformed conventional time-series forecasting methods where applicable. Results indicate that the proposed models were capable of forecasting patients' demand with astonishing performances both individually and on larger scales.

Conclusion: Intelligent demand forecasting can help palliative care home hospitalization systems to overcome the challenge of progressive demand growth when a considerable portion of patients are approaching death, followed by a sudden drop in demand when those patients pass away. It helps to improve resource utilization and quality of care concurrently.
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http://dx.doi.org/10.1016/j.jbi.2022.104075DOI Listing
April 2022

Maximum expected survival rate model for public access defibrillator placement.

Resuscitation 2022 01 6;170:213-221. Epub 2021 Dec 6.

Health Services and Systems Research, Duke-NUS Medical School, Singapore; Health Services Research Centre, Singapore Health Services, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore. Electronic address:

Aim: Mathematical optimization of automated external defibrillator (AED) placement has demonstrated potential to improve survival of out-of-hospital cardiac arrest (OHCA). Existing models mostly aim to improve accessibility based on coverage radius and do not account for detailed impact of delayed defibrillation on survival. We aimed to predict OHCA survival based on time to defibrillation and developed an AED placement model to directly maximize the expected survival rate.

Methods: We stratified OHCAs occurring in Singapore (2010-2017) based on time to defibrillation and developed a regression model to predict the Utstein survival rate. We then developed a novel AED placement model, the maximum expected survival rate (MESR) model. We compared the performance of MESR with a maximum coverage model developed for Canada that was shown to be generalizable to other settings (Denmark). The survival gain of MESR was assessed through 10-fold cross-validation for placement of 20 to 1000 new AEDs in Singapore. Statistical analysis was performed using χ and McNemar's tests.

Results: During the study period, 15,345 OHCAs occurred. The power-law approximation with R of 91.33% performed best among investigated models. It predicted a survival of 54.9% with defibrillation within the first two minutes after collapse that was reduced by more than 60% without defibrillation within the first 4 minutes. MESR outperformed the maximum coverage model with P-value < 0.05 (<0.0001 in 22 of 30 experiments).

Conclusion: We developed a novel AED placement model based on the impact of time to defibrillation on OHCA outcomes. Mathematical optimization can improve OHCA survival.
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http://dx.doi.org/10.1016/j.resuscitation.2021.11.039DOI Listing
January 2022

Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework.

Resuscitation 2022 01 26;170:126-133. Epub 2021 Nov 26.

Pre-hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore. Electronic address:

Background: Accurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who achieved the return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communications with next-of-kins. We aimed to develop the Survival After ROSC in Cardiac Arrest (SARICA), a practical clinical decision tool to predict survival in OHCA patients who attained ROSC.

Methods: We utilized real-world Singapore data from the population-based Pan-Asian Resuscitation Outcomes Study between 2010-2018. We excluded patients without ROSC. The dataset was segmented into training (60%), validation (20%) and testing (20%) cohorts. The primary endpoint was survival (to 30-days or hospital discharge). AutoScore, an interpretable machine-learning based clinical score generation algorithm, was used to develop SARICA. Candidate factors were chosen based on objective demographic and clinical factors commonly available at the time of admission. Performance of SARICA was evaluated based on receiver-operating curve (ROC) analyses.

Results: 5970 patients were included, of which 855 (14.3%) survived. A three-variable model was determined to be most parsimonious. Prehospital ROSC, age, and initial heart rhythm were identified for inclusion via random forest selection. Finally, SARICA consisted of these 3 variables and ranged from 0 to 10 points, achieving an area under the ROC (AUC) of 0.87 (95% confidence interval: 0.84-0.90) within the testing cohort.

Conclusion: We developed and internally validated the SARICA score to accurately predict survival of OHCA patients with ROSC at the time of admission. SARICA is clinically practical and developed using an interpretable machine-learning framework. SARICA has unknown generalizability pending external validation studies.
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http://dx.doi.org/10.1016/j.resuscitation.2021.11.029DOI Listing
January 2022

Drone-delivered automated external defibrillators: How to site them?

Resuscitation 2021 06 24;163:189-190. Epub 2021 Apr 24.

Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore; Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore. Electronic address:

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http://dx.doi.org/10.1016/j.resuscitation.2021.04.011DOI Listing
June 2021

The effectiveness of public health interventions against COVID-19: Lessons from the Singapore experience.

PLoS One 2021 30;16(3):e0248742. Epub 2021 Mar 30.

Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.

Background: In dealing with community spread of COVID-19, two active interventions have been attempted or advocated-containment, and mitigation. Given the extensive impact of COVID-19 globally, there is international interest to learn from best practices that have been shown to work in controlling community spread to inform future outbreaks. This study explores the trajectory of COVID-19 infection in Singapore had the government intervention not focused on containment, but rather on mitigation. In addition, we estimate the actual COVID-19 infection cases in Singapore, given that confirmed cases are publicly available.

Methods And Findings: We developed a COVID-19 infection model, which is a modified SIR model that differentiate between detected (diagnosed) and undetected (undiagnosed) individuals and segments total population into seven health states: susceptible (S), infected asymptomatic undiagnosed (A), infected asymptomatic diagnosed (I), infected symptomatic undiagnosed (U), infected symptomatic diagnosed (E), recovered (R), and dead (D). To account for the infection stages of the asymptomatic and symptomatic infected individuals, the asymptomatic infected individuals were further disaggregated into three infection stages: (a) latent (b) infectious and (c) non-infectious; while the symptomatic infected were disaggregated into two stages: (a) infectious and (b) non-infectious. The simulation result shows that by the end of the current epidemic cycle without considering the possibility of a second wave, under the containment intervention implemented in Singapore, the confirmed number of Singaporeans infected with COVID-19 (diagnosed asymptomatic and symptomatic cases) is projected to be 52,053 (with 95% confidence range of 49,370-54,735) representing 0.87% (0.83%-0.92%) of the total population; while the actual number of Singaporeans infected with COVID-19 (diagnosed and undiagnosed asymptomatic and symptomatic infected cases) is projected to be 86,041 (81,097-90,986), which is 1.65 times the confirmed cases and represents 1.45% (1.36%-1.53%) of the total population. A peak in infected cases is projected to have occurred on around day 125 (27/05/2020) for the confirmed infected cases and around day 115 (17/05/2020) for the actual infected cases. The number of deaths is estimated to be 37 (34-39) among those infected with COVID-19 by the end of the epidemic cycle; consequently, the perceived case fatality rate is projected to be 0.07%, while the actual case fatality rate is estimated to be 0.043%. Importantly, our simulation model results suggest that there about 65% more COVID-19 infection cases in Singapore that have not been captured in the official reported numbers which could be uncovered via a serological study. Compared to the containment intervention, a mitigation intervention would have resulted in early peak infection, and increase both the cumulative confirmed and actual infection cases and deaths.

Conclusion: Early public health measures in the context of targeted, aggressive containment including swift and effective contact tracing and quarantine, was likely responsible for suppressing the number of COVID-19 infections in Singapore.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248742PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8009429PMC
April 2021

Impact of COVID-19 on acute isolation bed capacity and nursing workforce requirements: A retrospective review.

J Nurs Manag 2021 Jul 8;29(5):1220-1227. Epub 2021 Feb 8.

Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore.

Aim: To understand the impact of COVID-19 on isolation bed capacity requirements, nursing workforce requirements and nurse:patient ratios.

Background: COVID-19 created an increased demand for isolation beds and nursing workforce globally.

Methods: This was a retrospective review of bed capacity, bed occupancy and nursing workforce data from the isolation units of a tertiary hospital in Singapore from 23 January 2020 to 31 May 2020. R v4.0.1 and Tidyverse 1.3.0 library were used for data cleaning and plotly 4.9.2.1 library for data visualization.

Results: In January to March 2020, isolation bed capacity was low (=<203 beds). A sharp increase in bed capacity was seen from 195 to 487 beds during 25 March to 29 April 2020, after which it plateaued. Bed occupancy remained lower than bed capacity throughout January to May 2020. After 16 April 2020, we experienced a shortage of 1.1 to 70.2 nurses in isolation wards. Due to low occupancy rates, nurse:patient ratio remained acceptable (minimum nurse:patient ratio = 0.26).

Conclusion: COVID-19 caused drastic changes in isolation bed capacity and nursing workforce requirements.

Implications For Nursing Management: Building a model to predict nursing workforce requirements during pandemic surges may be helpful for planning and adequate staffing.
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http://dx.doi.org/10.1111/jonm.13260DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8013355PMC
July 2021
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