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Article: Auto Response Generation in Online Medical Chat Services.

Jahanshahi, Hadi / Kazmi, Syed / Cevik, Mucahit

Journal of healthcare informatics research

2022 Volume 6, Issue 3, Page(s) 344–374

Abstract: Telehealth helps to facilitate access to medical professionals by enabling remote medical services for the patients. These services have become gradually popular over the years with the advent of necessary technological infrastructure. The benefits of ... ...

Abstract	Telehealth helps to facilitate access to medical professionals by enabling remote medical services for the patients. These services have become gradually popular over the years with the advent of necessary technological infrastructure. The benefits of telehealth have been even more apparent since the beginning of the COVID-19 crisis, as people have become less inclined to visit doctors in person during the pandemic. In this paper, we focus on facilitating chat sessions between a doctor and a patient. We note that the quality and efficiency of the chat experience can be critical as the demand for telehealth services increases. Accordingly, we develop a smart auto-response generation mechanism for medical conversations that helps doctors respond to consultation requests efficiently, particularly during busy sessions. We explore over 900,000 anonymous, historical online messages between doctors and patients collected over 9 months. We implement clustering algorithms to identify the most frequent responses by doctors and manually label the data accordingly. We then train machine learning algorithms using this preprocessed data to generate the responses. The considered algorithm has two steps: a filtering (i.e., triggering) model to filter out infeasible patient messages and a response generator to suggest the top-3 doctor responses for the ones that successfully pass the triggering phase. Among the models utilized, BERT provides an accuracy of 85.41% for precision@3 and shows robustness to its parameters.
Language	English
Publishing date	2022-07-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2895595-X
ISSN	2509-498X ; 2509-4971
ISSN (online)	2509-498X
ISSN	2509-4971
DOI	10.1007/s41666-022-00118-x
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Article: The fractional-order discrete COVID-19 pandemic model: stability and chaos.

Abbes, Abderrahmane / Ouannas, Adel / Shawagfeh, Nabil / Jahanshahi, Hadi

Nonlinear dynamics

2022 , Page(s) 1–19

Abstract: This paper presents and investigates a new fractional discrete COVID-19 model which involves three variables: the new daily cases, additional severe cases and deaths. Here, we analyze the stability of the equilibrium point at different values of the ... ...

Abstract	This paper presents and investigates a new fractional discrete COVID-19 model which involves three variables: the new daily cases, additional severe cases and deaths. Here, we analyze the stability of the equilibrium point at different values of the fractional order. Using maximum Lyapunov exponents, phase attractors, bifurcation diagrams, the 0-1 test and approximation entropy (ApEn), it is shown that the dynamic behaviors of the model change from stable to chaotic behavior by varying the fractional orders. Besides showing that the fractional discrete model fits the real data of the pandemic, the simulation findings also show that the numbers of new daily cases, additional severe cases and deaths exhibit chaotic behavior without any effective attempts to curb the epidemic.
Language	English
Publishing date	2022-08-15
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2012600-1
ISSN	1573-269X ; 0924-090X
ISSN (online)	1573-269X
ISSN	0924-090X
DOI	10.1007/s11071-022-07766-z
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Article ; Online: A new buffering theory of social support and psychological stress.

Bekiros, Stelios / Jahanshahi, Hadi / Munoz-Pacheco, Jesus M

PloS one

2022 Volume 17, Issue 10, Page(s) e0275364

Abstract: A dynamical model linking stress, social support, and health has been recently proposed and numerically analyzed from a classical point of view of integer-order calculus. Although interesting observations have been obtained in this way, the present work ... ...

Abstract	A dynamical model linking stress, social support, and health has been recently proposed and numerically analyzed from a classical point of view of integer-order calculus. Although interesting observations have been obtained in this way, the present work conducts a fractional-order analysis of that model. Under a periodic forcing of an environmental stress variable, the perceived stress has been analyzed through bifurcation diagrams and two well-known metrics of entropy and complexity, such as spectral entropy and C0 complexity. The results obtained by numerical simulations have shown novel insights into how stress evolves with frequency and amplitude of the perturbation, as well as with initial conditions for the system variables. More precisely, it has been observed that stress can alternate between chaos, periodic oscillations, and stable behaviors as the fractional order varies. Moreover, the perturbation frequency has revealed a narrow interval for the chaotic oscillations, while its amplitude may present different values indicating a low sensitivity regarding chaos generation. Also, the perceived stress has been noted to be highly sensitive to initial conditions for the symptoms of stress-related ill-health and for the social support received from family and friends. This work opens new directions of research whereby fractional calculus might offer more insight into psychology, life sciences, mental disorders, and stress-free well-being.
MeSH term(s)	Calculi ; Entropy ; Humans ; Nonlinear Dynamics ; Social Support ; Stress, Psychological
Language	English
Publishing date	2022-10-12
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.0275364
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Book ; Online: S-DABT

Jahanshahi, Hadi / Cevik, Mucahit

Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems

2022

Abstract: Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule and Dependency-aware Bug Triage (S- ... ...

Abstract	Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule and Dependency-aware Bug Triage (S-DABT), a bug triaging method that utilizes integer programming and machine learning techniques to assign bugs to suitable developers. Unlike prior works that largely focus on a single component of the bug reports, our approach takes into account the textual data, bug fixing costs, and bug dependencies. We further incorporate the schedule of developers in our formulation to have a more comprehensive model for this multifaceted problem. As a result, this complete formulation considers developers' schedules and the blocking effects of the bugs while covering the most significant aspects of the previously proposed methods. Our numerical study on four open-source software systems, namely, EclipseJDT, LibreOffice, GCC, and Mozilla, shows that taking into account the schedules of the developers decreases the average bug fixing times. We find that S-DABT leads to a high level of developer utilization through a fair distribution of the tasks among the developers and efficient use of the free spots in their schedules. Via the simulation of the issue tracking system, we also show how incorporating the schedule in the model formulation reduces the bug fixing time, improves the assignment accuracy, and utilizes the capability of each developer without much comprising in the model run times. We find that S-DABT decreases the complexity of the bug dependency graph by prioritizing blocking bugs and effectively reduces the infeasible assignment ratio due to bug dependencies. Consequently, we recommend considering developers' schedules while automating bug triage. Comment: An extension of "DABT: A Dependency-aware Bug Triaging Method" arXiv:2104.12744
Keywords	Computer Science - Software Engineering ; Computer Science - Machine Learning
Subject code	000
Publishing date	2022-04-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Conduction and validation of a novel prognostic signature in cervical cancer based on the necroptosis characteristic genes via integrating of multiomics data.

Xu, Tu / Jiang, Jingwen / Xiang, Xiaoqing / Jahanshahi, Hadi / Zhang, Yong / Chen, Xiaoyan / Li, Lesai

Computers in biology and medicine

2023 Volume 168, Page(s) 107656

Abstract: The significance of necroptosis in recurrent or metastatic cervical cancer remains unclear. In this study, we utilized various bioinformatics methods to analyze the cancer genome atlas (TCGA) data, gene chip and the single-cell RNA-sequencing (scRNA seq) ...

Abstract	The significance of necroptosis in recurrent or metastatic cervical cancer remains unclear. In this study, we utilized various bioinformatics methods to analyze the cancer genome atlas (TCGA) data, gene chip and the single-cell RNA-sequencing (scRNA seq) data. And a necroptosis-related genes signature for prognostic assessment of patients with cervical cancer was constructed successfully. Survival analysis, receiver operating characteristic (ROC) curve, the support vector machine recursive feature elimination (SVM-RFE) algorithm and random forest analysis were performed to validate this signature. Patients in TCGA-CESC cohort were grouped into "high-necroptosis score (H-NCPS)" vs "low-necroptosis score (L-NCPS)" subgroups based on the median of necroptosis score of each patient. Analyses of the tumor microenvironment manifested "H-NCPS" patients associated with lower degree of immune infiltration. Through the utilization of survival analysis, cell communication, and Gene Set Enrichment Analysis (GSEA), PGK1 was determined to be the pivotal gene within the 9-gene signature associated with necroptosis. The high expression of PGK1 in cervical cancer cells was confirmed through the utilization of quantitative real-time polymerase chain reaction (RT-qPCR) and the human protein atlas (HPA). In the interim, PGK1 prompted the transition of M1 macrophages to M2 macrophages and influenced the occurrence and development of necroptosis. In conclusion, the 9-gene signature developed from necroptosis-related genes has shown significant predictive capabilities for the prognosis of cervical cancer, offered valuable guidance for individualized and targeted treatment approaches for patients.
MeSH term(s)	Humans ; Female ; Uterine Cervical Neoplasms/genetics ; Prognosis ; Multiomics ; Necroptosis/genetics ; Computational Biology ; Tumor Microenvironment
Language	English
Publishing date	2023-11-10
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107656
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Article: Active Learning for Multi-way Sensitivity Analysis with Application to Disease Screening Modeling.

Cevik, Mucahit / Angco, Sabrina / Heydarigharaei, Elham / Jahanshahi, Hadi / Prayogo, Nicholas

Journal of healthcare informatics research

2022 Volume 6, Issue 3, Page(s) 317–343

Abstract: Sensitivity analysis is an important aspect of model development as it can be used to assess the level of confidence that is associated with the outcomes of a study. In many practical problems, sensitivity analysis involves evaluating a large number of ... ...

Abstract	Sensitivity analysis is an important aspect of model development as it can be used to assess the level of confidence that is associated with the outcomes of a study. In many practical problems, sensitivity analysis involves evaluating a large number of parameter combinations which may require an extensive amount of time and resources. However, such a computational burden can be avoided by identifying smaller subsets of parameter combinations that can be later used to generate the desired outcomes for other parameter combinations. In this study, we investigate machine learning-based approaches for speeding up the sensitivity analysis. Furthermore, we apply feature selection methods to identify the relative importance of quantitative model parameters in terms of their predictive ability on the outcomes. Finally, we highlight the effectiveness of active learning strategies in improving the sensitivity analysis processes by reducing the total number of quantitative model runs required to construct a high-performance prediction model. Our experiments on two datasets obtained from the sensitivity analysis performed for two disease screening modeling studies indicate that ensemble methods such as Random Forests and XGBoost consistently outperform other machine learning algorithms in the prediction task of the associated sensitivity analysis. In addition, we note that active learning can lead to significant speed-ups in sensitivity analysis by enabling the selection of more useful parameter combinations (i.e., instances) to be used for prediction models.
Language	English
Publishing date	2022-07-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2895595-X
ISSN	2509-498X ; 2509-4971
ISSN (online)	2509-498X
ISSN	2509-4971
DOI	10.1007/s41666-022-00117-y
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Book ; Online: nTreeClus

Jahanshahi, Hadi / Baydogan, Mustafa Gokce

a Tree-based Sequence Encoder for Clustering Categorical Series

2021

Abstract: The overwhelming presence of categorical/sequential data in diverse domains emphasizes the importance of sequence mining. The challenging nature of sequences proves the need for continuing research to find a more accurate and faster approach providing a ... ...

Abstract	The overwhelming presence of categorical/sequential data in diverse domains emphasizes the importance of sequence mining. The challenging nature of sequences proves the need for continuing research to find a more accurate and faster approach providing a better understanding of their (dis)similarities. This paper proposes a new Model-based approach for clustering sequence data, namely nTreeClus. The proposed method deploys Tree-based Learners, k-mers, and autoregressive models for categorical time series, culminating with a novel numerical representation of the categorical sequences. Adopting this new representation, we cluster sequences, considering the inherent patterns in categorical time series. Accordingly, the model showed robustness to its parameter. Under different simulated scenarios, nTreeClus improved the baseline methods for various internal and external cluster validation metrics for up to 10.7% and 2.7%, respectively. The empirical evaluation using synthetic and real datasets, protein sequences, and categorical time series showed that nTreeClus is competitive or superior to most state-of-the-art algorithms. Comment: Published in Neurocomputing (Available online 22 April 2022)
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	006
Publishing date	2021-02-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: ADPTriage

Jahanshahi, Hadi / Cevik, Mucahit / Mousavi, Kianoush / Başar, Ayşe

Approximate Dynamic Programming for Bug Triage

2022

Abstract: Bug triaging is a critical task in any software development project. It entails triagers going over a list of open bugs, deciding whether each is required to be addressed, and, if so, which developer should fix it. However, the manual bug assignment in ... ...

Abstract	Bug triaging is a critical task in any software development project. It entails triagers going over a list of open bugs, deciding whether each is required to be addressed, and, if so, which developer should fix it. However, the manual bug assignment in issue tracking systems (ITS) offers only a limited solution and might easily fail when triagers must handle a large number of bug reports. During the automated assignment, there are multiple sources of uncertainties in the ITS, which should be addressed meticulously. In this study, we develop a Markov decision process (MDP) model for an online bug triage task. In addition to an optimization-based myopic technique, we provide an ADP-based bug triage solution, called ADPTriage, which has the ability to reflect the downstream uncertainty in the bug arrivals and developers' timetables. Specifically, without placing any limits on the underlying stochastic process, this technique enables real-time decision-making on bug assignments while taking into consideration developers' expertise, bug type, and bug fixing time. Our result shows a significant improvement over the myopic approach in terms of assignment accuracy and fixing time. We also demonstrate the empirical convergence of the model and conduct sensitivity analysis with various model parameters. Accordingly, this work constitutes a significant step forward in addressing the uncertainty in bug triage solutions
Keywords	Computer Science - Software Engineering ; Computer Science - Machine Learning
Subject code	006
Publishing date	2022-11-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak.

Yousefpour, Amin / Jahanshahi, Hadi / Bekiros, Stelios

Chaos, solitons, and fractals

2020 Volume 136, Page(s) 109883

Abstract: Understanding the early transmission dynamics of diseases and estimating the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control ... ...

Abstract	Understanding the early transmission dynamics of diseases and estimating the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully designed. This study has two main scientific contributions that are: (1) This is pioneer research that proposes policies regarding COVID-19, (2) This is also the first research that addresses COVID-19 and considers its economic consequences through a multi-objective evolutionary algorithm. Numerical simulations conspicuously demonstrate that by applying the proposed optimal policies, governments could find useful and practical ways for control of the disease.
Keywords	covid19
Language	English
Publishing date	2020-05-16
Publishing country	England
Document type	Journal Article
ZDB-ID	2003919-0
ISSN	1873-2887 ; 0960-0779
ISSN (online)	1873-2887
ISSN	0960-0779
DOI	10.1016/j.chaos.2020.109883
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Article: A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19.

Jahanshahi, Hadi / Munoz-Pacheco, Jesus M / Bekiros, Stelios / Alotaibi, Naif D

Chaos, solitons, and fractals

2021 Volume 143, Page(s) 110632

Abstract: COVID-19 is a novel coronavirus affecting all the world since December last year. Up to date, the spread of the outbreak continues to complicate our lives, and therefore, several research efforts from many scientific areas are proposed. Among them, ... ...

Abstract	COVID-19 is a novel coronavirus affecting all the world since December last year. Up to date, the spread of the outbreak continues to complicate our lives, and therefore, several research efforts from many scientific areas are proposed. Among them, mathematical models are an excellent way to understand and predict the epidemic outbreaks evolution to some extent. Due to the COVID-19 may be modeled as a non-Markovian process that follows power-law scaling features, we present a fractional-order SIRD (Susceptible-Infected-Recovered-Dead) model based on the Caputo derivative for incorporating the memory effects (long and short) in the outbreak progress. Additionally, we analyze the experimental time series of 23 countries using fractal formalism. Like previous works, we identify that the COVID-19 evolution shows various power-law exponents (no just a single one) and share some universality among geographical regions. Hence, we incorporate numerous memory indexes in the proposed model, i.e., distinct fractional-orders defined by a time-dependent function that permits us to set specific memory contributions during the evolution. This allows controlling the memory effects of more early states, e.g., before and after a quarantine decree, which could be less relevant than the contribution of more recent ones on the current state of the SIRD system. We also prove our model with Italy's real data from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.
Language	English
Publishing date	2021-01-10
Publishing country	England
Document type	Journal Article
ZDB-ID	2003919-0
ISSN	1873-2887 ; 0960-0779
ISSN (online)	1873-2887
ISSN	0960-0779
DOI	10.1016/j.chaos.2020.110632
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