1
|
Miranda O, Kiehl SM, Qi X, Brannock MD, Kosten T, Ryan ND, Kirisci L, Wang Y, Wang L. Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records. BMC Med Inform Decis Mak 2024; 24:154. [PMID: 38835009 PMCID: PMC11151516 DOI: 10.1186/s12911-024-02554-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities. METHODS In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories. RESULTS The sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption. CONCLUSIONS The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.
Collapse
Affiliation(s)
- Oshin Miranda
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | | | - Xiguang Qi
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | | | - Thomas Kosten
- Menninger Department of Psychiatry, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Neal David Ryan
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Levent Kirisci
- University of Pittsburgh School of Pharmacy, Pittsburgh, PA, 15213, USA
| | - Yanshan Wang
- University of Pittsburgh School of Health and Rehabilitation Sciences, Pittsburgh, PA, 15213, USA
| | - LiRong Wang
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
| |
Collapse
|
2
|
The mediating role of knowledge management processes in the effective use of artificial intelligence in manufacturing firms. INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT 2022. [DOI: 10.1108/ijopm-05-2022-0282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
PurposeThis paper aims to provide and empirically test a conceptual model in which artificial intelligence (AI), knowledge management processes (KMPs) and supply chain resilience (SCR) are simultaneously considered in terms of their reciprocal relationships and impact on manufacturing firm performance (MFP).Design/methodology/approachIn the study, six hypotheses have been developed and tested through an empirical survey administered to 120 senior executives of Italian manufacturing firms. The data analysis has been carried out via the partial least squares structural equation modelling approach, using the Advanced Analysis for Composites 2.0 variance-based software program.FindingsUsing a conceptual model validated using an empirical survey, the study sheds light on the relationships between AI, KMPs and SCR, as well as their impacts on MFP. In particular, the authors show the positive effects of the adoption of AI on KMPs, as well as the influence of KMPs on SCR and MFP. Finally, the authors demonstrate that KMPs act as a mediator through which AI affects SCR and MFP.Practical implicationsThis study highlights the critical role of KMPs for manufacturing firms that can deploy AI to stimulate KMPs and through attaining a high level of the latter might succeed in enhancing both their SCR and MFP.Originality/valueThis study demonstrates that manufacturing firms interested in properly applying AI to ameliorate their performance and resilience must carefully consider KMPs as a mediator mechanism.
Collapse
|
3
|
Luo Y, Cuneo KC, Lawrence TS, Matuszak MM, Dawson LA, Niraula D, Ten Haken RK, El Naqa I. A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy. Front Oncol 2022; 12:1061024. [PMID: 36568208 PMCID: PMC9782976 DOI: 10.3389/fonc.2022.1061024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/01/2022] [Indexed: 12/13/2022] Open
Abstract
Background Imbalanced outcome is one of common characteristics of oncology datasets. Current machine learning approaches have limitation in learning from such datasets. Here, we propose to resolve this problem by utilizing a human-in-the-loop (HITL) approach, which we hypothesize will also lead to more accurate and explainable outcome prediction models. Methods A total of 119 HCC patients with 163 tumors were used in the study. 81 patients with 104 tumors from the University of Michigan Hospital treated with SBRT were considered as a discovery dataset for radiation outcomes model building. The external testing dataset included 59 tumors from 38 patients with SBRT from Princess Margaret Hospital. In the discovery dataset, 100 tumors from 77 patients had local control (LC) (96% of 104 tumors) and 23 patients had at least one grade increment of ALBI (I-ALBI) during six-month follow up (28% of 81 patients). Each patient had a total of 110 features, where 15 or 20 features were identified by physicians as expert knowledge features (EKFs) for LC or I-ALBI prediction. We proposed a HITL based Bayesian network (HITL-BN) approach to enhance the capability of selecting important features from imbalanced data in terms of accuracy and explainability through humans' participation by integrating feature importance ranking and Markov blanket algorithms. A pure data-driven Bayesian network (PD-BN) method was applied to the same discovery dataset of HCC patients as a benchmark. Results In the training and testing phases, the areas under receiver operating characteristic curves of the HITL-BN models for LC or I-ALBI prediction during SBRT are 0.85 (95% confidence interval: 0.75-0.95) or 0.89 (0.81-0.95) and 0.77 or 0.78, respectively. They significantly outperformed the during-treatment PD-BN model in predicting LC or I-ALBI based on the discovery cross-validation and testing datasets from the Delong tests. Conclusion By allowing the human expert to be part of the model building process, the HITL-BN approach yielded significantly improved accuracy as well as better explainability when dealing with imbalanced outcomes in the prediction of post-SBRT treatment response of HCC patients when compared to the PD-BN method.
Collapse
Affiliation(s)
- Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States,*Correspondence: Yi Luo,
| | - Kyle C. Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Theodore S. Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Martha M. Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Laura A. Dawson
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Dipesh Niraula
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States
| | - Randall K. Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States
| |
Collapse
|
4
|
Akhtar P, Ghouri AM, Khan HUR, Amin ul Haq M, Awan U, Zahoor N, Khan Z, Ashraf A. Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions. ANNALS OF OPERATIONS RESEARCH 2022; 327:1-25. [PMID: 36338350 PMCID: PMC9628472 DOI: 10.1007/s10479-022-05015-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.
Collapse
Affiliation(s)
- Pervaiz Akhtar
- University of Aberdeen Business School, University of Aberdeen, King’s College, AB24 5UA Aberdeen, UK
- Imperial College London, SW7 2BU London, UK
| | - Arsalan Mujahid Ghouri
- Faculty of Management and Economics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia
| | - Haseeb Ur Rehman Khan
- Faculty of Art, Computing, and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia
| | - Mirza Amin ul Haq
- Department of Business Administration, Iqra University, Karachi, Pakistan
| | - Usama Awan
- Department of Business Administration, Inland School of Business and Social Sciences, Inland Norway University of Applied Sciences, Hamar, Norway
| | - Nadia Zahoor
- School of Business and Management, Queen Mary University of London, London, UK
| | - Zaheer Khan
- University of Aberdeen Business School, University of Aberdeen, King’s College, AB24 5UA Aberdeen, UK
- Innolab, University of Vaasa, Vaasa, Finland
| | - Aniqa Ashraf
- CAS-Key Laboratory of Crust-Mantle Materials and the Environments, School of Earth and Space Sciences, University of Science and Technology of China, 230026 Hefei, PR China
| |
Collapse
|
5
|
Sharma K, Dwivedi YK, Metri B. Incorporating causality in energy consumption forecasting using deep neural networks. ANNALS OF OPERATIONS RESEARCH 2022; 339:1-36. [PMID: 35967838 PMCID: PMC9362444 DOI: 10.1007/s10479-022-04857-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems.
Collapse
Affiliation(s)
- Kshitij Sharma
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yogesh K. Dwivedi
- Emerging Markets Research Centre (EMaRC), School of Management, Swansea University, Room #323, Bay Campus, Fabian Bay, Swansea, SA1 8EN Wales, UK
- Department of Management, Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed University), Pune, Maharashtra India
| | | |
Collapse
|
6
|
Lu X, Wijayaratna K, Huang Y, Qiu A. AI-Enabled Opportunities and Transformation Challenges for SMEs in the Post-pandemic Era: A Review and Research Agenda. Front Public Health 2022; 10:885067. [PMID: 35570947 PMCID: PMC9098932 DOI: 10.3389/fpubh.2022.885067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
The negative impact of COVID-19 pandemic has seen SME's struggling around the world. With many quickly adopting digital technologies, such as AI, in their manufacturing or services operations to achieve sustainable development. This study aims to develop a framework that informs AI-enabled sustainable development for SMEs by integrating the relevant research in the field. In this framework, we identify the opportunities that the deployment of AI technology can do to alleviate the plights of SMEs in the post-pandemic era, including the impacts on work, organizations, and performance. We further explore the challenges that SMEs face in AI transformation and recommend strategies to take on those challenges. Finally we propose an agenda for future research based on technological challenges and environmental threats.
Collapse
Affiliation(s)
- Xiaoqian Lu
- School of Business Administration, Jimei University, Xiamen, China
| | - Kumud Wijayaratna
- Newcastle Business School, Northumbria University, Newcastle Upon Tyne, United Kingdom
| | - Yufei Huang
- University of York Management School, University of York, York, United Kingdom
| | - Aimei Qiu
- School of Business, Guangdong University of Foreign Studies, Guangzhou, China
| |
Collapse
|
7
|
Chatterjee S, Chaudhuri R, Vrontis D, Papadopoulos T. Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support. ANNALS OF OPERATIONS RESEARCH 2022:1-21. [PMID: 35125588 PMCID: PMC8800827 DOI: 10.1007/s10479-021-04505-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/15/2021] [Indexed: 06/14/2023]
Abstract
Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms' productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm's predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.
Collapse
Affiliation(s)
- Sheshadri Chatterjee
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal India
| | - Ranjan Chaudhuri
- Department of Marketing, National Institute of Industrial Engineering (NITIE), Mumbai, India
| | - Demetris Vrontis
- Faculty and Research, Strategic Management, School of Business, University of Nicosia, Nicosia, Cyprus
| | | |
Collapse
|