Demo Publications

@article{soton479497b,

title = {Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer},

author = {Navamayooran Thavanesan and Indu Bodala and Zoe Walters and Sarvapali Ramchurn and Timothy J. Underwood and Ganesh Vigneswaran},

url = {https://eprints.soton.ac.uk/479497/},

year  = {2023},

date = {2023-11-01},

journal = {European Journal of Surgical Oncology},

volume = {49},

number = {11},

abstract = {ensuremath<pensuremath>Background: Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to address inconsistency and standardize care. The aim of this experimental pilot study was to develop ML models able to predict curative OC MDT treatment decisions and determine the relative importance of underlying decision-critical variables. Methods: Retrospective complete-case analysis of oesophagectomy patients $±$ neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) between 2010 and 2020. Established ML algorithms (Multinomial Logistic regression (MLR), Random Forests (RF), Extreme Gradient Boosting (XGB)) and Decision Tree (DT) were used to train models predicting OC MDT treatment decisions: surgery (S), NACT + S or NACRT + S. Performance metrics included Area Under the Curve (AUC), Accuracy, Kappa, LogLoss, F1 and Precision -Recall AUC. Variable importance was calculated for each model. Results: We identified 399 cases with a male-to-female ratio of 3.6:1 and median age of 66.1yrs (range 32?83). MLR outperformed RF, XGB and DT across performance metrics (mean AUC of 0.793 [$±$0.045] vs 0.757 [$±$0.068], 0.740 [$±$0.042], and 0.709 [$±$0.021] respectively). Variable importance analysis identified age as a major factor in the decision to offer surgery alone or NACT + S across models (p < 0.05). Conclusions: ML techniques can use limited feature-sets to predict curative UGI MDT treatment decisions. Explainable Artificial Intelligence methods provide insight into decision-critical variables, highlighting underlying subconscious biases in cancer care decision-making. Such models may allow prioritization of caseload, improve efficiency, and offer data-driven decision-assistance to MDTs in the future.ensuremath</pensuremath>},

note = {Publisher Copyright: 

© 2023 The Author(s)},

keywords = {Artificial Intelligence, machine learning, Oesophageal cancer multidisciplinary team},

pubstate = {published},

tppubtype = {article}

}

@article{soton479497,

title = {Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer},

author = {Navamayooran Thavanesan and Indu Bodala and Zoe Walters and Sarvapali Ramchurn and Timothy J Underwood and Ganesh Vigneswaran},

url = {https://eprints.soton.ac.uk/479497/},

year  = {2023},

date = {2023-07-01},

journal = {European Journal of Surgical Oncology},

abstract = {ensuremath<pensuremath>Background: Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to address inconsistency and standardize care. The aim of this experimental pilot study was to develop ML models able to predict curative OC MDT treatment decisions and determine the relative importance of underlying decision-critical variables. Methods: Retrospective complete-case analysis of oesophagectomy patients $pm$ neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) between 2010 and 2020. Established ML algorithms (Multinomial Logistic regression (MLR), Random Forests (RF), Extreme Gradient Boosting (XGB)) and Decision Tree (DT) were used to train models predicting OC MDT treatment decisions: surgery (S), NACT + S or NACRT + S. Performance metrics included Area Under the Curve (AUC), Accuracy, Kappa, LogLoss, F1 and Precision -Recall AUC. Variable importance was calculated for each model. Results: We identified 399 cases with a male-to-female ratio of 3.6:1 and median age of 66.1yrs (range 32?83). MLR outperformed RF, XGB and DT across performance metrics (mean AUC of 0.793 [$pm$0.045] vs 0.757 [$pm$0.068], 0.740 [$pm$0.042], and 0.709 [$pm$0.021] respectively). Variable importance analysis identified age as a major factor in the decision to offer surgery alone or NACT + S across models (p < 0.05). Conclusions: ML techniques can use limited feature-sets to predict curative UGI MDT treatment decisions. Explainable Artificial Intelligence methods provide insight into decision-critical variables, highlighting underlying subconscious biases in cancer care decision-making. Such models may allow prioritization of caseload, improve efficiency, and offer data-driven decision-assistance to MDTs in the future.ensuremath</pensuremath>},

note = {Publisher Copyright: 

copyright 2023 The Author(s)},

keywords = {Artificial Intelligence, machine learning, Oesophageal cancer multidisciplinary team},

pubstate = {published},

tppubtype = {article}

}

@article{soton471971,

title = {Reasoning About Responsibility in Autonomous Systems: Challenges and Opportunities},

author = {Vahid Yazdanpanah and Enrico Gerding and Sebastian Stein and Mehdi Dastani and Catholijn M Jonker and Timothy Norman and Sarvapali Ramchurn},

url = {https://eprints.soton.ac.uk/471971/},

year  = {2022},

date = {2022-11-01},

journal = {AI & Society},

abstract = {Ensuring the trustworthiness of autonomous systems and artificial intelligenceensuremath<br/ensuremath>is an important interdisciplinary endeavour. In this position paper, we argue thatensuremath<br/ensuremath>this endeavour will benefit from technical advancements in capturing various forms of responsibility, and we present a comprehensive research agenda to achieve this. In particular, we argue that ensuring the reliability of autonomous system can take advantage of technical approaches for quantifying degrees of responsibility and for coordinating tasks based on that. Moreover, we deem that, in certifying the legality of an AI system, formal and computationally implementable notions of responsibility, blame, accountability, and liability are applicable for addressing potential responsibility gaps (i.e., situations in which a group is responsible, but individuals? responsibility may be unclear). This is a call to enable AI systems themselves, as well as those involved in the design, monitoring, and governance of AI systems, to represent and reason about who can be seen as responsible in prospect (e.g., for completing a task in future) and who can be seen as responsible retrospectively (e.g., for a failure that has already occurred). To that end, in this work, we show that across all stages of the design, development, and deployment of Trustworthy Autonomous Systems (TAS), responsibility reasoning should play a key role. This position paper is the first step towards establishing a road-map and research agenda on how the notion of responsibility can provide novel solution concepts for ensuring the reliability and legality of TAS and, as a result, enables an effective embedding of AI technologies into society.},

keywords = {Artificial Intelligence, Citizen-Centric AI Systems, human-agent collectives, Human-Centred AI, Multiagent Responsibility Reasoning, Multiagent Systems, Trustworthy Autonomous Systems},

pubstate = {published},

tppubtype = {article}

}

@article{soton467975,

title = {From Intelligent Agents to Trustworthy Human-Centred Multiagent Systems},

author = {Mohammad Divband Soorati and Enrico Gerding and Enrico Marchioni and Pavel Naumov and Timothy Norman and Sarvapali Ramchurn and Baharak Rastegari and Adam Sobey and Sebastian Stein and Danesh Tarapore and Vahid Yazdanpanah and Jie Zhang},

url = {https://eprints.soton.ac.uk/467975/},

year  = {2022},

date = {2022-07-01},

journal = {AI Communications},

abstract = {The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS). We have made substantial scientific contributions across learning in MAS, game-theoretic techniques for coordinating agent systems, and formal methods for representation and reasoning. We highlight key results achieved by the group and elaborate on recent work and open research challenges in developing trustworthy autonomous systems and deploying human-centred AI systems that aim to support societal good.},

keywords = {Artificial Intelligence, Distributed Artificial Intelligence, Intelligent Agents, Multiagent Systems, Trustworthy Autonomous Systems},

pubstate = {published},

tppubtype = {article}

}

@article{soton450597,

title = {Trustworthy human-AI partnerships},

author = {Sarvapali Ramchurn and Sebastian Stein and Nicholas R Jennings},

url = {https://eprints.soton.ac.uk/450597/},

year  = {2021},

date = {2021-08-01},

journal = {iScience},

volume = {24},

number = {8},

abstract = {In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organisations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry.},

keywords = {Artificial Intelligence, Human-Computer Interaction, Sociology},

pubstate = {published},

tppubtype = {article}

}

@article{soton479497b,

title = {Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer},

author = {Navamayooran Thavanesan and Indu Bodala and Zoe Walters and Sarvapali Ramchurn and Timothy J. Underwood and Ganesh Vigneswaran},

url = {https://eprints.soton.ac.uk/479497/},

year  = {2023},

date = {2023-11-01},

journal = {European Journal of Surgical Oncology},

volume = {49},

number = {11},

abstract = {ensuremath<pensuremath>Background: Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to address inconsistency and standardize care. The aim of this experimental pilot study was to develop ML models able to predict curative OC MDT treatment decisions and determine the relative importance of underlying decision-critical variables. Methods: Retrospective complete-case analysis of oesophagectomy patients $±$ neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) between 2010 and 2020. Established ML algorithms (Multinomial Logistic regression (MLR), Random Forests (RF), Extreme Gradient Boosting (XGB)) and Decision Tree (DT) were used to train models predicting OC MDT treatment decisions: surgery (S), NACT + S or NACRT + S. Performance metrics included Area Under the Curve (AUC), Accuracy, Kappa, LogLoss, F1 and Precision -Recall AUC. Variable importance was calculated for each model. Results: We identified 399 cases with a male-to-female ratio of 3.6:1 and median age of 66.1yrs (range 32?83). MLR outperformed RF, XGB and DT across performance metrics (mean AUC of 0.793 [$±$0.045] vs 0.757 [$±$0.068], 0.740 [$±$0.042], and 0.709 [$±$0.021] respectively). Variable importance analysis identified age as a major factor in the decision to offer surgery alone or NACT + S across models (p < 0.05). Conclusions: ML techniques can use limited feature-sets to predict curative UGI MDT treatment decisions. Explainable Artificial Intelligence methods provide insight into decision-critical variables, highlighting underlying subconscious biases in cancer care decision-making. Such models may allow prioritization of caseload, improve efficiency, and offer data-driven decision-assistance to MDTs in the future.ensuremath</pensuremath>},

note = {Publisher Copyright: 

© 2023 The Author(s)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{soton479497,

title = {Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer},

author = {Navamayooran Thavanesan and Indu Bodala and Zoe Walters and Sarvapali Ramchurn and Timothy J Underwood and Ganesh Vigneswaran},

url = {https://eprints.soton.ac.uk/479497/},

year  = {2023},

date = {2023-07-01},

journal = {European Journal of Surgical Oncology},

abstract = {ensuremath<pensuremath>Background: Rising workflow pressures within the oesophageal cancer (OC) multidisciplinary team (MDT) can lead to variability in decision-making, and health inequality. Machine learning (ML) offers a potential automated data-driven approach to address inconsistency and standardize care. The aim of this experimental pilot study was to develop ML models able to predict curative OC MDT treatment decisions and determine the relative importance of underlying decision-critical variables. Methods: Retrospective complete-case analysis of oesophagectomy patients $pm$ neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) between 2010 and 2020. Established ML algorithms (Multinomial Logistic regression (MLR), Random Forests (RF), Extreme Gradient Boosting (XGB)) and Decision Tree (DT) were used to train models predicting OC MDT treatment decisions: surgery (S), NACT + S or NACRT + S. Performance metrics included Area Under the Curve (AUC), Accuracy, Kappa, LogLoss, F1 and Precision -Recall AUC. Variable importance was calculated for each model. Results: We identified 399 cases with a male-to-female ratio of 3.6:1 and median age of 66.1yrs (range 32?83). MLR outperformed RF, XGB and DT across performance metrics (mean AUC of 0.793 [$pm$0.045] vs 0.757 [$pm$0.068], 0.740 [$pm$0.042], and 0.709 [$pm$0.021] respectively). Variable importance analysis identified age as a major factor in the decision to offer surgery alone or NACT + S across models (p < 0.05). Conclusions: ML techniques can use limited feature-sets to predict curative UGI MDT treatment decisions. Explainable Artificial Intelligence methods provide insight into decision-critical variables, highlighting underlying subconscious biases in cancer care decision-making. Such models may allow prioritization of caseload, improve efficiency, and offer data-driven decision-assistance to MDTs in the future.ensuremath</pensuremath>},

note = {Publisher Copyright: 

copyright 2023 The Author(s)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{soton471971,

title = {Reasoning About Responsibility in Autonomous Systems: Challenges and Opportunities},

author = {Vahid Yazdanpanah and Enrico Gerding and Sebastian Stein and Mehdi Dastani and Catholijn M Jonker and Timothy Norman and Sarvapali Ramchurn},

url = {https://eprints.soton.ac.uk/471971/},

year  = {2022},

date = {2022-11-01},

journal = {AI & Society},

abstract = {Ensuring the trustworthiness of autonomous systems and artificial intelligenceensuremath<br/ensuremath>is an important interdisciplinary endeavour. In this position paper, we argue thatensuremath<br/ensuremath>this endeavour will benefit from technical advancements in capturing various forms of responsibility, and we present a comprehensive research agenda to achieve this. In particular, we argue that ensuring the reliability of autonomous system can take advantage of technical approaches for quantifying degrees of responsibility and for coordinating tasks based on that. Moreover, we deem that, in certifying the legality of an AI system, formal and computationally implementable notions of responsibility, blame, accountability, and liability are applicable for addressing potential responsibility gaps (i.e., situations in which a group is responsible, but individuals? responsibility may be unclear). This is a call to enable AI systems themselves, as well as those involved in the design, monitoring, and governance of AI systems, to represent and reason about who can be seen as responsible in prospect (e.g., for completing a task in future) and who can be seen as responsible retrospectively (e.g., for a failure that has already occurred). To that end, in this work, we show that across all stages of the design, development, and deployment of Trustworthy Autonomous Systems (TAS), responsibility reasoning should play a key role. This position paper is the first step towards establishing a road-map and research agenda on how the notion of responsibility can provide novel solution concepts for ensuring the reliability and legality of TAS and, as a result, enables an effective embedding of AI technologies into society.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{soton467975,

title = {From Intelligent Agents to Trustworthy Human-Centred Multiagent Systems},

author = {Mohammad Divband Soorati and Enrico Gerding and Enrico Marchioni and Pavel Naumov and Timothy Norman and Sarvapali Ramchurn and Baharak Rastegari and Adam Sobey and Sebastian Stein and Danesh Tarapore and Vahid Yazdanpanah and Jie Zhang},

url = {https://eprints.soton.ac.uk/467975/},

year  = {2022},

date = {2022-07-01},

journal = {AI Communications},

abstract = {The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS). We have made substantial scientific contributions across learning in MAS, game-theoretic techniques for coordinating agent systems, and formal methods for representation and reasoning. We highlight key results achieved by the group and elaborate on recent work and open research challenges in developing trustworthy autonomous systems and deploying human-centred AI systems that aim to support societal good.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{soton450597,

title = {Trustworthy human-AI partnerships},

author = {Sarvapali Ramchurn and Sebastian Stein and Nicholas R Jennings},

url = {https://eprints.soton.ac.uk/450597/},

year  = {2021},

date = {2021-08-01},

journal = {iScience},

volume = {24},

number = {8},

abstract = {In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organisations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}