Laura Moss

Research & Development Healthcare Computer Scientist,
NHS Greater Glasgow & Clyde

Honorary Senior Lecturer,
School of Medicine, University of Glasgow

Bio

Dr Moss' principal research interest lies in developing innovative health informatics approaches which drive clinical knowledge discovery. She has experience of complex data analysis from routinely collected clinical datasets and has designed tools which help to support clinical reasoning based on these datasets. Dr Moss has interests in understanding, capturing, and representing complex reasoning processes. Specifically, she is interested in the automated generation of explanations, how they can be created from large scale datasets and the use of these techniques to create scrutable, transparent and accountable artificial intelligence systems. This research has encompassed and contributed to areas such as hybrid intelligence, knowledge representation and reasoning, argumentation, provenance, knowledge acquisition and human expertise/computation studies.

As well as contributing to theoretical research, she has also performed empirical and interdisciplinary research. Application of her research has had a significant focus on the healthcare domain and she has been responsible for research outputs in both clinical and computing science fields as well as working in the health informatics field.

Prior to joining NHS Greater Glasgow & Clyde, Dr Moss gained a PhD from the University of Aberdeen for work on an approach to the refinement of semantic web knowledge bases, comprising the automatic generation of explanations for anomalies and analogical reasoning. This work was evaluated in the Critical Care medical field.

Dr Moss is a member of the IDEAS (Intelligent Data Exploration and Analysis) research group, and is currently responsible for leading research into the artificial intelligence fields of knowledge capture & refinement for decision support, hypothesis generation, and intelligent data analysis.

In addition, she is a member of the European BrainIT research group and works in collaboration with several external research groups.

Dr Moss holds an honorary research position in the School of Medicine, University of Glasgow.

Contact Me

Research Themes

Semantic Web Approaches for Explainable and Adaptive Hybrid Intelligence in Healthcare

Domain ontologies provide a formalism with which to represent detailed domain knowledge. I am interested in the role of domain ontologies in automatically generating explanations which are understandable and useful to a human. By building more transparent and explainable systems, clinicians will be better equipped to understand, trust and work with these systems. Additionally, I am interested in the role in which domain ontologies can play in refining the knowledge base of an intelligent system. The ability to refine knowledge in an intelligent healthcare system is important as complex medical environments don’t remain static and as such require working with the human agent to adapt to changing knowledge.

I have investigated the automatic explanation of anomalies as a method of generating refinements to a theory held by a human or computer. Anomalies are interesting as they help to identify the part of the theory requiring refinement. This work involved extensive interviews held with domain experts and the strategies used by the domain experts to provide (appropriate) explanations for the anomalies were identified. A knowledge-based system, EIRA (Explaining, Inferencing, and Reasoning about Anomalies), was developed. EIRA implemented the domain-expert based strategies with domain ontologies and data to automatically generate explanations for an anomaly. To evaluate this approach, EIRA has been applied in the Intensive Care Unit (ICU) domain to investigate the detection and explanation of anomalous patient responses to treatment and ICU clinicians have evaluated the explanations produced by EIRA.

Work on this theme has also explored the application of analogical reasoning in conjunction with domain ontologies to generate refinements to a knowledge base.

Selected papers:

Argumentation Theory, Clinical Reasoning & Explanation Generation

Argumentation theory provides computational models of defeasible reasoning and has been applied in artificial intelligence and multi-agent systems. Argumentation theory is particularly well suited to support clinical decision-making due to its ability to reason with uncertain knowledge and derive defeasible and understandable conclusions. Subsequently, models of argumentation are being increasingly deployed in clinical decision-making systems to facilitate reasoning.

I have explored the use of argumentation theory for the generation of explanations of the conclusions of intelligent systems, enabling greater scrutiny, and as part of an argumentation-based dialogue system where the aim was to achieve systems for medical training that provide human-like mechanisms for computer-clinician interaction, potentially enhancing the acceptance of the system's explanations while changing the clinician's behaviour. Furthermore, providing clinicians with simple mechanisms to discover if the knowledge base used by the explanation system should be updated or corrected, potentially changing the system's behaviour. As part of this work cognitive models of clinical reasoning have been extracted from real deliberation dialogues between ICU clinicians. It is envisioned that this work contributes towards hybrid intelligence research in particular collaborative, explainable and adaptive hybrid intelligence.

Selected papers:

Clinical Knowledge Acquisition & Collaborative Hybrid Intelligence

I am interested in understanding and formalising clinical cognitive reasoning and how, using that knowledge, machines can be used collaboratively to support the role of clinicians. For example, machines can often help prevent common human cognitive biases. We have explored the issue of clinicians’ cognitive biases during the task of knowledge acquisition. To develop knowledge bases for use in knowledge-based (or expert) systems, domain experts are often interviewed or are asked to perform tasks during which knowledge engineers aim to capture their expertise and cognitive reasoning. Although domain experts are highly regarded, they can still display cognitive biases (i.e. errors in judgement, knowledge, and reasoning) which can affect their performance. Consequently, developing accurate knowledge bases for knowledge-based systems is still a challenge for knowledge engineers. In this work, I have worked closely with Prof. Derek Sleeman and contributed to several tools to aid the knowledge acquisition process in the clinical domain, including IS-DELPHI, INSIGHT, PREDICTOR, and Temporal Workbench.

Selected papers:

Interpretable & Explainable Predictive Models in Healthcare

Techniques from the field of machine learning have been applied to patient data to develop models with high levels of predictive accuracy. However, whilst these models appear clinically promising, their interpretability has often not been considered and they are black box models, making it extremely difficult to understand how the model came to its conclusion. By building more transparent or explainable models it may lead to increased trust in medical settings. I am interested in the development of predictive models from healthcare data which use symbolic approaches which often lead to more intrinsically interpretable models. Additionally, I am interested in the development of explanations from machine learning approaches, in particular through the combination of non-symbolic machine learning with domain knowledge represented in ontologies.

Selected papers:

Critical Care Informatics

I have an interest in complex data analysis from routinely collected clinical datasets and tools which help to support clinical reasoning based on these datasets. I am very keen to work on real-world clinical problems and to support analyses which directly impact patient care and healthcare service provision. For example, CHART-ADAPT was a platform developed to enable clinically important physiological models and analyses to be implemented more quickly into clinical practice. It was demonstrated in the neurocritical care unit, and it was capable of extracting high-frequency physiological patient data, analysing it, and then returning the results directly back into clinical practice in real time.

Additionally, I work with clinical researchers and share an interest in the improvement of the acute clinical management of brain injured patients through the novel development and application of advanced computational, artificial intelligence and knowledge discovery methods to high resolution physiological monitoring and clinical management data collected from patients managed within neurocritical care and neurospecialist wards. As part of this work I am involved in the BrainIT group and lead the G-BINARy research group (https://www.gla.ac.uk/schools/medicine/research/g-binary/).

Selected papers:

Funding

External Committee and Group Membership

Supervision

PhD

MSc

BSc

Publications

Peer Reviewed Publications

  1. Palmer, J., Manataki, A., Moss, L., Neilson, A., Lo, M. “Toy” Paediatric Critical Care Unit (PCCU): Building a computational simulation model to improve patient flow in a Scottish PCCU. Abstract accepted for publication in Pediatric Critical Care Medicine Journal
  2. Moss, L., Corsar, D., Shaw, M., Piper, I. Hawthorne, C. Demystifying the Black Box: Approaches to Aid Understanding and Interpretability of Predictive Models in Neurocritical Care. Neurocrit Care. 2022 May 6. doi: 10.1007/s12028-022-01504-4. Epub ahead of print. PMID: 35523917.
  3. Tracey, J., Ashmore, J. Moss, L. Training AI using Artificial Data: Rubbish In, Rubbish Out? IPEM Scope Vol 31(4) Winter 2021 36-38.
  4. Cowan, L. Moss, L., Puxty, K., Shaw, M. Developing Interpretable Mortality Prediction Models for Intensive Care. Intensive Care Medicine Experimental 2021, 9(1): 001171
  5. Moss, L., Shaw, M., Piper, I., Kinsella, J., Hawthorne, C. CHART-ADAPT: Enabling actionable analytics at the critical care unit bedside. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 2021, pp. 301-306, doi: 10.1109/CBMS52027.2021.00032.
  6. Hannigan LJ, Phillippo DM, Hanlon P, Moss L, Butterly EW, Hawkins N, Dias S, Welton NJ, McAllister DA. Improving the Estimation of Subgroup Effects for Clinical Trial Participants with Multimorbidity by Incorporating Drug Class-Level Information in Bayesian Hierarchical Models: A Simulation Study. Med Decis Making. 2021 Aug 18:272989X211029556. doi: 10.1177/0272989X211029556. Epub ahead of print. PMID: 34407672.
  7. Young., J. Cayha, E., Moss, L., Hawthorne, C., Piper, I., Kommer, M., O’Kane R., Shaw, M. Influence of Patient Demographics on Optimal Cerebral Perfusion Pressure Following Traumatic Brain Injury. Acta Neurochir Suppl. 2021;131:153-158. doi: 10.1007/978-3-030-59436-7_31. PMID: 33839837.
  8. Kommer, M., Canty, M., Campbell, E., Sangra, M., Amato-Watkins, A., Young, S., Hawthorne, C., Moss, L., Piper, I., Shaw, M., O'Kane, R. The use of direct ICP and brain tissue oxygen monitoring in the perioperative management of patients with Moyamoya disease. Acta Neurochir Suppl. 2021;131:115-117. doi: 10.1007/978-3-030-59436-7_24. PMID: 33839830.
  9. Shaw, M., Hawthorne, C., Moss, L., Piper, I. Time series analysis and prediction of ICP using time varying dynamic linear models. Acta Neurochir Suppl. 2021;131:225-229. doi: 10.1007/978-3-030-59436-7_43. PMID: 33839849.
  10. Stell, A., Moss, L., Hawthorne, C., O’Kane, R., Shaw, M., Piper, I. An evaluation of software for the automated measurement of adherence to the ICP-monitoring threshold guideline. Acta Neurochir Suppl. 2021;131:217-224. doi: 10.1007/978-3-030-59436-7_42. PMID: 33839848.
  11. Kommer, M., Boulton, R., Loi, L., Robinson, S., Hawthorne, C., Shaw, M., Piper, I., Moss, L., Amato-Watkins, A., Campbell, E., Sangra, M., O’Kane, R. Telemetric ICP: A snapshot does not give the full story. Acta Neurochir Suppl. 2021;131:323-324. doi: 10.1007/978-3-030-59436-7_61. PMID: 33839867.
  12. Moss L., Sleeman, D., Kinsella, J. (2020) Development of Persuasive Argumentation Schemes to Represent Clinical Dialogue. In: Grasso F., Green, N.L., Schneider, J., Wells, S. (eds) Computational Models of Natural Argument 2020. Proceedings of the 20th Workshop on Computational Models of Natural Argument, co-located with the 8th International Conference on Computational Models of Argument (COMMA 2020). CEUR Vol 2669, 57-66. ISSN 1613-0073.
  13. Moss, L., Henderson, M., Puxty, A., Shaw, M., Leach, J.P., McPeake, J., Quasim, T. Post-critical care mortality of patients admitted to an Intensive Care Unit with seizures: a population-based study. Anaesthesia. 2020 Mar;75(3):417-418. doi: 10.1111/anae.14977. PMID: 32022911.
  14. Sleeman, D.; Kostadinov, K.; Moss, L. and Sim, M. (2020). Resolving Differences of Opinion between Medical Experts: A Case Study with the IS-DELPHI System. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-398-8, pages 66-76. DOI: 10.5220/0008922000660076
  15. Depreitere B. et al. (2018) Cerebral Perfusion Pressure Variability Between Patients and Between Centres. In: Heldt T. (eds) Intracranial Pressure & Neuromonitoring XVI. Acta Neurochirurgica Supplement, vol 126. Springer, Cham. doi:10.1007/978-3-319-65798-1_1. PMID:29492521
  16. Hawthorne C., Shaw M., Piper I., Moss L., Kinsella J. (2018) Transcranial Bioimpedance Measurement as a Non-invasive Estimate of Intracranial Pressure. In: Heldt T. (eds) Intracranial Pressure & Neuromonitoring XVI. Acta Neurochirurgica Supplement, vol 126. Springer, Cham. doi:10.1007/978-3-319-65798-1_19 PMID: 29492539
  17. Flechet M. et al. (2018) Visualizing Cerebrovascular Autoregulation Insults and Their Association with Outcome in Adult and Paediatric Traumatic Brain Injury. In: Heldt T. (eds) Intracranial Pressure & Neuromonitoring XVI. Acta Neurochirurgica Supplement, vol 126. Springer, Cham. doi:10.1007/978-3-319-65798-1_57. PMID: 29492577
  18. Shaw M., Moss L., Hawthorne C., Kinsella J., Piper I. (2018) Investigation of the Relationship Between the Burden of Raised ICP and the Length of Stay in a Neuro-Intensive Care Unit. In: Heldt T. (eds) Intracranial Pressure & Neuromonitoring XVI. Acta Neurochirurgica Supplement, vol 126. Springer, Cham. doi: 10.1007/978-3-319-65798-1_42. PMID: 29492562
  19. Piper I., Shaw M., Hawthorne C., Kinsella J, Moss L. (2018) Medical Waveform Format Encoding Rules Representation of Neurointensive Care Waveform Data. In: Heldt T. (eds) Intracranial Pressure & Neuromonitoring XVI. Acta Neurochirurgica Supplement, vol 126. Springer, Cham. doi: 10.1007/978-3-319-65798-1_38. PMID: 29492558
  20. Stell A., Piper I. and Moss L. (2018). Automated Measurement of Adherence to Traumatic Brain Injury (TBI) Guidelines using Neurological ICU Data. In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-281-3, pages 135-146. doi: 10.5220/0006583801350146
  21. Dewanti, A., Papapanagiotou, P., Gilhooly, C., Fleuriot, J., Manataki, A., Moss, L.. Development of Workflow-Based Guidelines for the Care of Burns in Scotland. Proceedings of the International Conference e-Health 2017, ISBN:978-989-8533-65-4, pages 155-158.
  22. Guiza, F., Meyfroit, G., Piper, I., Citerio, G., Chambers, I., Enblad, P., Nillson, P., Feyen, B., Jorens, P., Maas, A., Schuhmann, M.U., Donald, R., Moss, L., Van den Berghe, G., Depreitere, B. (2017) Cerebral Perfusion Pressure Insults and Associations with Outcome in Adult Traumatic Brain Injury. J Neurotrauma Aug 15; 34(16): 2425-2431. doi:10.1089/neu.2016.4807). PMID:28385097
  23. Moss, L., Shaw, M., Piper, I., Hawthorne, C., Kinsella, J. (2017) Sharing of Big Data in Healthcare: Public Opinion, Trust, and Privacy Considerations for Health Informatics Researchers. In: Proc. of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017). ISBN 978-989-758-213-4 pages 463-468, doi:10.5220/0006251504630468
  24. Moss, L., Shaw, M., Piper, I., Hawthorne, C., Kinsella, J., Aridhia, Philips Healthcare. (2016) Apache Spark for the Analysis of High Frequency Neurointensive Care Unit Data: Preliminary Comparison of Scala vs. R. In: Proc. of American Medical Informatics Association 2016 Annual Symposium (AMIA 2016) pages 1523.
  25. Moss, L., Shaw, M., Piper, I., Arvind, D.K., Hawthorne, C. (2016) In: Ang BT. (eds) Intracranial Pressure and Brain Monitoring XV. Acta Neurochirurgica Supplement, vol 122. Springer, Chamdoi: doi:10.1007/978-3-319-22533-3_52.
  26. Sim, M., Moss, L., Sleeman, D., Kinsella, J. (2015) Knowledge Capture Techniques to Design a Score of Overall Physiological State in Critical Illness. Critical Care Medicine 43(12):1-3, December. doi:10.1097/01.ccm.0000473972.12234.b0
  27. Bonner, S., A.S. McGough,., Kureshi, I., Brennan, J., Theodoropoulos, G., Moss, L., Corsar, D., Antoniou, G. (2015) Data Quality Assessment and Anomaly Detection Via Map / Reduce and Linked Data: A Case Study in the Medical Domain. 2015 IEEE International Conference on Big Data (Big Data), pp. 737-746. doi: 10.1109/BigData.2015.7363818
  28. Guiza, F., Depreitere, B., Piper, I., Citerio, G., Chambers, I., Jones, P,A., Lo, T.Y., Enblad, P., Nillson, P., Feyen, B., Jorens, P., Maas, A., Schuhmann, M.U., Donald, R., Moss, L., Van den Berghe, G., Meyfroidt, G. (2015) Visualizing the Pressure and Time Burden of Intracranial Hypertension in Adult and Paediatric Traumatic Brain Injury. Intensive Care Med, Jun;41(6):1067-76. doi: 10.1007/s00134-015-3806-1. PMID:25894624
  29. Moss. L., Corsar, D., Hawthorne, C., Piper, I., Shaw, M., Kinsella, J. (2014) Data Quality in Neurointensive Care Datasets. Critical Care Medicine, Dec 2014, Vol 42 (12) pA1496. doi: 10.1097/01.ccm.0000458064.02118.7b
  30. Bonner, S., Antoniou., G., Moss, L., Kureshi, I., Corsar, D., Tachmazidis, I. (2014) Using Hadoop to Implement a Semantic Method of Assessing The Quality of Research Medical Datasets. BigDataScience '14: Proceedings of the 2014 International Conference on Big Data Science and Computing. ACM Press, 2014. doi: 10.1145/2640087.2644163
  31. Stell, A., Moss, L., Piper, I. (2014) Building an Empirical Treatment Protocol from High-Resolution Traumatic Brain Injury Data. In: Warren J., Gray K. (eds) HIKM '14 Proceedings of the Seventh Australasian Workshop on Health Informatics and Knowledge Management, Vol 153, Austrailian Computer Society. ISBN: 978-1-921770-35-7 pages 79-88.
  32. Tober, K., Moss, L., Runcie, A., Willox, L., Talwar, D., Kinsella, J. (2013) Asymmetric Dimethylarginine, Homoarginine Levels and Atrial Fibrillation in Oesophagectomy Patients, Critical Care Medicine, Dec 2013, Vol 41(12)
  33. Moss, L., Corsar, D., Piper, I., Kinsella, J. (2013) A Semantic Web Approach to Assessing the Quality of Medical Data. In: Proceedings of BCS Health Informatics Scotland Conference 2013 (Edinburgh, UK)
  34. Moss, L., Corsar, D., Piper, I., Kinsella, J. (2013) Trusting Intensive Care Unit (ICU) Medical Data: A Semantic Web Approach. In: Peek N., Marin Morales R., Peleg M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science, vol 7885. Springer, Berlin, Heidelberg. ISBN: 978-3-642-38325-0 doi.org/10.1007/978-3-642-38326-7_10
  35. Grando, A., Moss, L., Sleeman, D., Kinsella, J. (2013) Argumentation-Logic for Creating and Explaining Medical Hypotheses. Artif Intell Med, May;58(1):1-13. doi:10.1016/j.artmed.2013.02.003. PMID: 23522940
  36. Kearns, R.J., Moss, L., Kinsella, J. (2013) A Comparison of Clinical Practice Guidelines for Proximal Femoral Fracture. Anaesthesia, 2013 Feb;68(2):159-66. doi: 10.1111/anae.12076. PMID:23121498.
  37. Grando, A., Moss, L., Bel-Enguix G, Jimenez-Lopez, M.D., Kinsella, J. (2013) Argumentation-Based Dialogue Systems for Medical Training. In: Neustein A., Markowitz J. (eds) Where Humans Meet Machines. Springer, New York, NY. ISBN: 978-1-4614-6933-9. doi:10.1007/978-1-4614-6934-6_10
  38. Docking, R., Moss, L., Sim, M., Sleeman, D., Kinsella, J. Investigation into Haemodynamic Stability During Intermittent Haemodialysis in the Critically Ill. Crit Care (2012) 16(Suppl 1): P371. https://doi.org/10.1186/cc10978
  39. Docking, R., Moss, L., Sim, M, Sleeman, D., Kinsella, Investigation into the Effects of Commencing Haemodialysis in the Critically Ill. J. Crit Care (2012) 16(Suppl 1): P359. https://doi.org/10.1186/cc10966.
  40. Sleeman, D., Moss, L., Aitken, A., Hughes, M., Sim, M., Kinsella, J. Detecting and Resolving Inconsistencies between Domain Experts' Different Perspectives on (Classification) Tasks. Artificial Intelligence in Medicine, 2012 Jun;55(2):71-86. doi: 10.1016/j.artmed.2012.03.001. PMID:22483422
  41. Moss, L., Corsar, D., Piper, I. (2012) A Linked Data Approach to Assessing Medical Data. In: Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2012), pages 529-532, IEEE. doi: 10.1109/CBMS.2012.6266391
  42. Stell, A., Moss, L., Piper, I. (2012) Knowledge-Driven Inference of Medical Interventions. In: Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2012), pages 521-524, IEEE. doi: 10.1109/CBMS.2012.6266389
  43. Moss, L, Sleeman, D., Quasim, T., Sim, M., Booth, M., Puxty, A., Kinsella, J. Identifying Myocardial Damage from Routinely Recorded Data in the Intensive Care Unit (ICU). Intensive Care Medicine, 37(Suppl 1): 1. https://doi.org/10.1007/s00134-011-2322-1
  44. Grando, A., Moss, L., Glasspool, D., Sleeman, D., Sim, M., Kinsella, J. (2011) Argumentation-Logic for Explaining Anomalous Patient Responses to Treatments. In: Peleg M., Lavrac N., Combi C. (eds) Artificial Intelligence in Medicine. AIME 2011. Lecture Notes in Computer Science, vol 6747. Springer, Berlin, Heidelberg. ISBN:978-3-642-22217-7 doi:10.1007/978-3-642-22218-4_5.
  45. Sleeman, D., Moss, L., Sim, M., Kinsella, J. (2011) Predicting Adverse Events: Detecting Myocardial Damage in Intensive Care Unit (ICU) Patients. In: Proc. of The Sixth International Conference on Knowledge Capture ACM Press, New York,NY,USA 73-80. ISBN: 978-1-4503-0396-5. doi: 10.1145/1999676.1999690
  46. Sleeman, D., Moss, L., Gyftodimos, E., Nicolson, M., Devereux G. (2010) A Comparison between Clinical Decisions made about Lung Cancer Patients and those inherent in the corresponding Scottish Intercollegiate Guideline Network (SIGN) Guideline, Health Informatics J. 2010 Dec;16(4):260-73. doi: 10.1177/1460458210380520. PMID:21216806
  47. Moss, L., Sleeman, D., Sim, M. (2010) Reasoning by Analogy in the Generation of Domain Acceptable Ontology Refinements. In: Cimiano P., Pinto H.S. (eds) Knowledge Engineering and Management by the Masses. EKAW 2010. Lecture Notes in Computer Science, vol 6317. Springer, Berlin, Heidelberg. ISBN:978-3-642-16437-8. doi: 10.1007/978-3-642-16438-5_43
  48. Sleeman, D., Aitken, A., Moss, L., Kinsella, J., Sim, M. (2010) A System to Detect Inconsistencies between a Domain Expert's Different Perspectives on (Classification) Tasks. In: Koronacki J., Ra? Z.W., Wierzcho? S.T., Kacprzyk J. (eds) Advances in Machine Learning II. Studies in Computational Intelligence, vol 263. Springer, Berlin, Heidelberg. ISBN:978-3-642-05178-4. doi:10.1007/978-3-642-05179-1_14.
  49. Moss, L., Sleeman, D., Sim, M., Booth, M., Daniel, M., Donaldson, L., Gilhooly, C., Hughes, M., Kinsella, J. (2010) Ontology-Driven Hypothesis Generation to Explain Anomalous Patient Responses to Treatment. Knowledge Based Systems, Volume 23, Issue 4, May 2010, pages 309-315. doi: doi.org/10.1016/j.knosys.2009.11.009.
  50. Sim, M., Aitken, A., Moss, L., Sleeman, D., Kinsella, J. Confusion Matrices To Refine A Novel Scoring System For Cardiovascular Instability In Intensive Care. Scottish Medical Journal, Volume 54, Issue 2, pg 56, May 2009
  51. Moss, L., Sleeman, D., Sim, M., Booth, M., Daniel, M., Donaldson, L., Gilhooly, C., Hughes, M., Kinsella, J. Ontology-Driven Hypothesis Generation to Explain Anomalous Patient Responses to Treatment. (2010) Ontology-Driven Hypothesis Generation to Explain Anomalous Patient Responses to Treatment. In: Bramer M., Ellis R., Petridis M. (eds) Research and Development in Intelligent Systems XXVI. Springer, London pages 63-76. ISBN:978-1-84882-982-4. doi: 10.1007/978-1-84882-983-1_5 *Best Student Paper*
  52. Corsar, D., Moss, L., Sleeman, D., Sim, M.(2009) Supporting the Development of Medical Ontologies. Frontiers in Artificial Intelligence and Applications: Formal Ontologies Meet Industry, pages 114-125. IOS Press. ISBN:978-1-60750-047-6.
  53. Moss, L., Sleeman, D., Booth, M., Daniel, M., Donaldson, L., Gilhooly, C., Hughes, M., Sim, M., Kinsella, J. (2009) Explaining Anomalous Responses to Treatment in the Intensive Care Unit. In: Combi C., Shahar Y., Abu-Hanna A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science, vol 5651. Springer, Berlin, Heidelberg. ISBN:978-3-642-02975-2. doi:10.1007/978-3-642-02976-9_36.
  54. Moss, L., Sleeman, D., Kinsella, J., Sim, M. (2008) ACHE: an Architecture for Clinical Hypothesis Examination. In: Puurone S., Pechenizkiy M., Tsymbal A.,Jye Lee D (eds) Proceedings of the 21st IEEE Symposium on Computer-Based Medical Systems (CBMS 2008) pages 158-160, IEEE. ISBN:978-0-7695-3165-6. doi:10.1109/CBMS.2008.100.
  55. Gyftodimos, E., Moss, L., Sleeman, D., Welch, A. (2008) Analysing PET Scans Data for Predicting Response to Chemotherapy in Breast Cancer Patients. In: Ellis R., Allen T., Petridis M. (eds) Applications and Innovations in Intelligent Systems XV. SGAI 2007. Springer, London. ISBN:978-1-84800-085-8. doi:10.1007/978-1-84800-086-5_5.
  56. Sleeman, D., Fluck, N., Gyftodimos, E., Moss, L., Christie, G. (2007) An Intelligent Aide for Interpreting a Patient's Dialysis Data Set. In: Bellazzi R., Abu-Hanna A., Hunter J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science, vol 4594. Springer, Berlin, Heidelberg. ISBN:978-3-540-73598-4. doi:10.1007/978-3-540-73599-1_7.

Conference Abstracts and Posters

  1. Moss, L., Hawthorne, C., Shaw, M., Piper, I., Aridhia, Philips Healthcare, Kinsella, J. Is There an Age Divide in Opinions about the Sharing of Critical Care Data with Private Companies? Society of Critical Care Medicine (SCCM) 47th Critical Care Congress, San Antonio, Texas, USA, February 2018.
  2. Kinsella, J., Hawthorne, C., Shaw, M., Piper, I., Philips Healthcare, Aridhia, Moss, L. Public Perception of the Collection and Use of Critical Care Patient Data Beyond Treatment: a Pilot Study. Society of Critical Care Medicine (SCCM) 46th Critical Care Congress, Honolulu, Hawaii, USA, January 2017.
  3. Shaw, M., Hawthorne, C., Moss, L., Piper, I., Kinsella, J., Philips Healthcare, Aridhia. Investigation of an Improved Optimal Cerebral Perfusion Pressure Calculation Methodology. Society of Critical Care Medicine (SCCM) 46th Critical Care Congress, Honolulu, Hawaii, USA, January 2017.
  4. Moss, L., Shaw, M., Hawthorne, C., Piper, I., McPeake, J., Quasim, T., Kinsella, J. Outcome in the Year Following Admission to an Intensive Care Unit in Scotland with Traumatic Brain Injury. Scottish Intensive Care Society Annual Scientific Meeting 2017, St. Andrew's, January 2017.
  5. Papapanagiotou, P., Dewanti, A., Manataki, A., Fleuriot, J., Gilhooly, C., Moss, L.,. Workflow Modelling of Burns Care Protocols. Scottish Intensive Care Society Annual Scientific Meeting 2017, St. Andrew's, January 2017.
  6. Moss, L., Shaw, M., Piper, I., Hawthorne, C., Kinsella, J., Aridhia, Philips Healthcare. Enabling Analysis of High Frequency Clinical Data at the Bedside: Update on the CHART-ADAPT Project. NHS Research Scotland Annual Conference 2016, Glasgow, UK, October 2016.
  7. Moss, L., Kinsella, J., Shaw, M., Piper, I., Hawthorne, C., Aridhia, Philips Healthcare. A Platform for the Analysis of Critical Care Data: Update on the CHART-ADAPT Project. BCS Health Informatics Scotland Conference 2016, Glasgow, UK, October 2016.
  8. Moss, L., Shaw, M., Piper, I., Hawthorne, C., Kinsella, J., Philips Healthcare., Aridhia. Pilot Evaluation of a De-Identification Tool for Neurointensive Care Unit Data. 16th International Sympoisum on Intracranial Pressure and Neuromonitoring (ICP 2016), Cambridge, Boston, USA, June 2016.
  9. Klein, S., Piper, I., Gregson, B., Enblad, P., Ragauskas, A., Citerio, G., Chambers, I., Neumann, J-O., Sahuquillo, J., Kiening, K., Moss, L., Nilsson, P., Donald, R., Howells, T., Depreitere, B. Timing of GCS Assessment and the Relationship with Long Term Outcome after Acute Traumatic Brain Injury. 16th International Sympoisum on Intracranial Pressure and Neuromonitoring (ICP 2016), Cambridge, Boston, USA, June 2016.
  10. Shaw, M., Moss, L., Piper,I., Hawthorne, C., Kinsella, J., Aridhia, Philips Healthcare. Improving the Performance Time of the Pressure Reactivity Index (PRx) Model using R and Scala. British Neurosurgical Research Group Meeting 2016, Cambridge, UK, March 2016.
  11. Shaw, M., Moss, L., Piper,I., Hawthorne, C., Kinsella, J., Aridhia, Philips Healthcare. Comparison of Hypotension Burden Models in the Neuro-intensive Care Unit. British Neurosurgical Research Group Meeting 2016, Cambridge, UK, March 2016.
  12. Moss, L.,, Shaw, M., Piper,I., Hawthorne, C., Kinsella, J., Aridhia, Philips Healthcare. Enabling Big Data Analysis in the Neurointensive Care Unit. British Neurosurgical Research Group Meeting 2016, Cambridge, UK, March 2016.
  13. Moss, L.,, Shaw, M., Hawthorne, C., Piper, I., Kinsella, J. Connecting Healthcare and Research Through A Data Provisioning Technology (CHART-ADAPT). Scottish Intensive Care Society 25th Annual Scientific Meeting. St. Andrew's, UK, January 2016.
  14. Shaw, M., Moss, L., O'Donnell, A., Judson, A., Piper, I. Exploring the Application of High Performance Computing to Enable the Analysis of Physiological Brain Injury Data. NRS (NHS Research Scotland) Conference 2015. Glasgow, UK, October 2015.
  15. Sleeman, D., Moss, L., Kinsella, J. Studies where experts provide feedback on patterns produced by a temporal discovery workbench. 1st International Workshop on Capturing Scientific Knowledge, Palisades, NY, USA, October 2015.
  16. Moss, L., Shaw, M., Piper, I, and Aridhia. Assessing the Impact of Data Modelling in Real-Time Traumatic Brain Injury Monitoring To Improve Patient Outcomes. Farr Institute International Conference 2015: Data Health Intensive Research and Care, St.Andrews, UK, August 2015.
  17. Henderson, W., Shaw, M., McLennan, F., Piper, I., Moss, L., Hawthorne, C. Cluster Analysis of the BrainIT Database. British Neurosurgical Research Group Meeting 2015, Cardiff, UK, March 2015.
  18. Shaw, M., O'Donnell, A., Piper, I., Moss, L. Down-Sampling Traumatic Brain Injury Physiological Data Using High Performance Computing. British Neurosurgical Research Group Meeting 2015, Cardiff, UK, March 2015.
  19. Canty, M., O'Kane, R., Turner, C., Shaw, M., Hawthorne, C., Moss, L., Piper, I. Comparison of 24 Vs 48 Hour ICP Recording for the Detection of metrics for raised ICP in Patients Investigated for CSF circulation abnormalities. British Neurosurgical Research Group Meeting 2015, Cardiff, UK, March 2015.
  20. Shaw, M., Hawthorne, C., Moss, L., Piper, I. Improvements to the Optimal Cerebral Perfusion Pressure Calculation. British Neurosurgical Research Group Meeting 2015, Cardiff, UK, March 2015.
  21. Sleeman, D., Moss. L., Kinsella, J. Temporal Discovery Workbench: a Case Study with ICU Patient Datasets. BCS Health Informatics Scotland Conference 2014, Glasgow, UK, September 2014.
  22. Moss, L., Shaw, M., Piper, I., Arvind, D.K, Automatic Calculation of Hydrostatic Pressure Gradient in Head Injured Patients: A Pilot Study. 15th International Symposium on Intracranial Pressure and Brain Monitoring (ICP 2013), Singapore, November 2013.
  23. Moss, L., Corsar, I., Piper, I., Hawthorne, C. An Approach for Establishing the Quality of Traumatic Brain Injury Data. 15th International Symposium on Intracranial Pressure and Brain Monitoring (ICP 2013), Singapore, November 2013.
  24. Stell, A., Piper, I., Moss, L. Multi-Centre ICP Treatment Patterns: a Study of the Brain-IT Database. 15th International Symposium on Intracranial Pressure and Brain Monitoring (ICP 2013), Singapore, November 2013.
  25. Moss, L., Piper, I., Shaw, M. Ontologies, Provenance, and Speckled Computing - Research Update. Brain IT 2012, Leuven, Belgium, Dec 2012
  26. Corsar, D., Moss, L., Piper, I. Data Quality Assessment Using Linked Data: A Case Study in the Medical Domain. 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW), Galway, Ireland, October 2012. *Best Poster Prize*
    27. Moss, L., Sleeman, D., Kinsella, J. Differences in the Identification of Anomalies from Computerized Physiological Data. 1st International Workshop on Capturing and Refining Knowledge in the Medical Domain (KMED 2012), Galway, Ireland, October 2012.
  27. Moss, L., Sleeman, D., Sim, M., Kinsella, J. Using Cardiovascular Derangements to Predict Raised Troponin Levels. 1st International Workshop on Capturing and Refining Knowledge in the Medical Domain (KMED 2012), Galway, Ireland, October 2012.
  28. Sleeman, D., Rogers, S., Moss, L., Aiken, A., Kinsella, J. INSIGHT: Helping Domain Experts make their Knowledge more Consistent. 1st International Workshop on Capturing and Refining Knowledge in the Medical Domain (KMED 2012), Galway, Ireland, October 2012.
  29. Sim, M., Moss, L., Sleeman, D., Kinsella, J. A novel system for detecting myocardial damage in the critically ill patient. Society of Critical Care Medicine (SCCM) Annual Congress, Houston, Texas, USA, February 2012.
  30. Moss, L., Sleeman, D., Kinsella, J. Clinicians' perspective on the connection between myocardial damage, troponin, and cardiovascular abnormality. Scottish Intensive Care Society Annual Meeting, St.Andrews, UK, January 2012.
  31. Sleeman, D., Muniesa, M., Moss, L., Sim, M., Docking, R., Kinsella, J. Correlation between mean score of cardiovascular instability and patient outcome. Scottish Intensive Care Society Annual Meeting, St.Andrews, UK, January 2012.
  32. McLeod, C., Kearns, R., Moss, L., Kinsella, J. SIGN guideline 122 - adherence in an intensive care unit. Scottish Intensive Care Society Annual Meeting, St.Andrews, UK, January 2012.
  33. Moss, L., Sleeman, D., Quasim, T., Sim, M., Booth, M., Puxty, A., Kinsella, J. Identifying Myocardial Damage from Routinely Recorded Data in the Intensive Care Unit (ICU). European Society of Intensive Care Medicine (ESICM) LIVES Annual Congress, Berlin, Germany, October 2011.
  34. Achenyo Ogbobi, B., Kearns, R., Moss, L., Wright, F., Kinsella, J. Dysphagia following a stroke: can every patient be given a swallow test on the day of admission? Scottish Society of Physicians Annual Meeting, Dumfries, UK, September 2011.
  35. Moss, L., Piper, I., Shaw, M. Precise Pressure (PP) - Infrared Based Head Tracking in the Automatic Correction of Cerebral Perfusion Pressure Measurement. Brain IT meeting, Uppsala, Sweden, May 2011.
  36. Moss, L., Grando, M.A., Sleeman, D., Sim, M., Gilhooly, C., Kinsella, J. Formalizing and Understanding Collaborative Decision Making in the Intensive Care Unit (ICU). Scottish Intensive Care Society Annual Meeting, St.Andrews, UK, January 2011.
  37. Moss, L., Sleeman, D., Sim, M., Booth, M., Donaldson, L., Gilhooly, C., Hughes, M., Kinsella, J. Development of EIRA, a knowledge-based system to explain anomalous patient responses to treatment. BCS Health Scotland Conference, Glasgow, UK, September 2010.
  38. Moss, L., Sleeman, D., Sim, M., Booth, M., Donaldson, L., Gilhooly, C., Hughes, M., Kinsella, J. Transforming Clinical Anomalies into Clinical Insights: Developing a knowledge-based system which explains a patient's unexpected reaction to treatment. Scottish Intensive Care Society Annual Meeting, St.Andrews, January 2010
  39. Sim, M., Moss, L., Aitken, A., Kinsella, J., Sleeman, D. Intermittent Haemodialysis may be Associated with Increased Haemodynamic Stability. Scottish Intensive Care Society Annual Meeting, St.Andrews, January 2010.
  40. Gyftodimos, E., Moss, L., Sleeman, D. Welch, A. Predicting Response to Chemotherapy in Breast Cancer Patients using Machine Learning Techniques. Current Perspectives in Healthcare Computing 2007, Proceedings of the Healthcare Computing 2007 Conference (HC2007) (Harrogate, UK). The British Computer Society Health Informatics Forum
  41. Sim, M., Moss, L., Kinsella, J., Sleeman, D. Assessing Cardiovascular Status in the ICU Advances in Anaesthesia & Intensive Care Symposium, Glasgow, September 2007