Samina Abidi

@article{nokey,

title = {Plausible Reasoning over Large Health Datasets: A Novel Approach to Data Analytics Leveraging Semantics},

author = {Hossein Mohammadhassanzadeh and Samina Raza Abidi and Syed Sibte Raza Abidi},

doi = {10.1016/j.knosys.2024.111493},

year  = {2024},

date = {2024-02-11},

urldate = {2024-02-11},

journal = {Knowledge-Based Systems},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians},

author = {Kyle W. Eastwood and Ronald May and Pantelis Andreou and Samina Abidi and Syed Sibte Raza Abidi and Osama M. Loubani},

doi = {https://doi.org/10.1186/s12913-023-09740-w},

issn = {1472-6963},

year  = {2023},

date = {2023-07-25},

urldate = {2023-07-25},

journal = {BMC Health Services Research},

volume = {23},

number = {798},

abstract = {Background

Artificial Intelligence (AI) is recognized by emergency physicians (EPs) as an important technology that will affect clinical practice. Several AI-tools have already been developed to aid care delivery in emergency medicine (EM). However, many EM tools appear to have been developed without a cross-disciplinary needs assessment, making it difficult to understand their broader importance to general-practice. Clinician surveys about AI tools have been conducted within other medical specialties to help guide future design. This study aims to understand the needs of Canadian EPs for the apt use of AI-based tools.



Methods

A national cross-sectional, two-stage, mixed-method electronic survey of Canadian EPs was conducted from January-May 2022. The survey includes demographic and physician practice-pattern data, clinicians’ current use and perceptions of AI, and individual rankings of which EM work-activities most benefit from AI.



Results

The primary outcome is a ranked list of high-priority AI-tools for EM that physicians want translated into general use within the next 10 years. When ranking specific AI examples, ‘automated charting/report generation’, ‘clinical prediction rules’ and ‘monitoring vitals with early-warning detection’ were the top items. When ranking by physician work-activities, ‘AI-tools for documentation’, ‘AI-tools for computer use’ and ‘AI-tools for triaging patients’ were the top items. For secondary outcomes, EPs indicated AI was ‘likely’ (43.1%) or ‘extremely likely’ (43.7%) to be able to complete the task of ‘documentation’ and indicated either ‘a-great-deal’ (32.8%) or ‘quite-a-bit’ (39.7%) of potential for AI in EM. Further, EPs were either ‘strongly’ (48.5%) or ‘somewhat’ (39.8%) interested in AI for EM.



Conclusions

Physician input on the design of AI is essential to ensure the uptake of this technology. Translation of AI-tools to facilitate documentation is considered a high-priority, and respondents had high confidence that AI could facilitate this task. This study will guide future directions regarding the use of AI for EM and help direct efforts to address prevailing technology-translation barriers such as access to high-quality application-specific data and developing reporting guidelines for specific AI-applications. With a prioritized list of high-need AI applications, decision-makers can develop focused strategies to address these larger obstacles.},

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@article{nokey,

title = {A Community-of-Practice-based Evaluation Methodology for Knowledge Intensive Computational Methods and its Application to Multimorbidity Decision Support},

author = {William Van Woensel and Samson W. Tu and Wojtek Michalowski and Syed Sibte Raza Abidi and Samina Abidi and Jose-Ramon Alonso and Alessio Bottrighi and Marc Carrier and Ruth Edry and Irit Hochberg and Malvika Rao and Stephen Kingwell and Alexandra Kogan and Mar Marcos and Begoña Martínez Salvador and Martin Michalowski and Luca Piovesan and David Riaño and Paolo Terenziani and Szymon Wilk and Mor Peleg},

url = {https://www.sciencedirect.com/science/article/pii/S1532046423001168},

doi = {https://doi.org/10.1016/j.jbi.2023.104395},

issn = {1532-0464},

year  = {2023},

date = {2023-06-01},

urldate = {2023-06-01},

journal = {Journal of Biomedical Informatics},

volume = {142},

number = {104395},

abstract = {Objective



The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans.



Materials and Methods



Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods—research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods.



Results



Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support.Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods.



Discussion



The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS.



Conclusion



We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).



Keywords: Evaluation Study; Benchmarking; Multimorbidity; Decision Support Systems, Clinical; Computer-interpretable Clinical Guidelines},

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@article{davis2022,

title = {Semantic Knowledge Modeling and Evaluation of Argument Theory to Develop Dialogue based Patient Education Systems for Chronic Disease Self-Management},

author = {Benjamin Rose-Davis and William Van Woensel and Samina Raza Abidi and Elizabeth Stringer and Syed Sibte Raza Abidi},

url = {https://www.sciencedirect.com/science/article/abs/pii/S1386505622000077},

year  = {2022},

date = {2022-01-19},

urldate = {2022-01-18},

journal = {International Journal of Medical Informatics},

keywords = {Argument Theory, Knowledge Graphs, Patient Education, Semantic Web},

pubstate = {published},

tppubtype = {article}

}

@article{VANWOENSEL2021102127,

title = {Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning},

author = {William Van Woensel and Syed Sibte Raza Abidi and Samina Raza Abidi},

url = {https://www.sciencedirect.com/science/article/pii/S0933365721001202},

doi = {https://doi.org/10.1016/j.artmed.2021.102127},

issn = {0933-3657},

year  = {2021},

date = {2021-08-01},

journal = {Artificial Intelligence in Medicine},

volume = {118},

pages = {102127},

abstract = {In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks—these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.},

keywords = {Clinical guidelines, Comorbidity, Decision Support Systems},

pubstate = {published},

tppubtype = {article}

}

@article{article,

title = {Fall Prevention and Management App Prototype for the Elderly and Their Caregivers: Design, Implementation, and Evaluation},

author = {Eseohen Imoukhome and Lori Weeks and Samina Abidi},

doi = {10.4018/IJEACH.2020010104},

year  = {2020},

date = {2020-01-01},

journal = {International Journal of Extreme Automation and Connectivity in Healthcare},

volume = {2},

pages = {48-67},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{VANWOENSEL2020101931,

title = {Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods},

author = {William Van Woensel and Patrice C Roy and Syed Sibte Raza Abidi and Samina Raza Abidi},

url = {http://www.sciencedirect.com/science/article/pii/S0933365720301275

https://authors.elsevier.com/a/1bTwR3KEGaD3xR},

doi = {https://doi.org/10.1016/j.artmed.2020.101931},

issn = {0933-3657},

year  = {2020},

date = {2020-01-01},

journal = {Artificial Intelligence in Medicine},

volume = {108},

pages = {101931},

abstract = {In a digitally enabled healthcare setting, we posit that an individual’s current location is pivotal for supporting many virtual care services—such as tailoring educational content towards an individual’s current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual’s indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual’s discrete semantic location; i.e., it is the concrete type of an individual’s location (e.g., exam vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of activities. We utilized Machine Learning methods to accurately identify an individual’s discrete location, together with knowledge-based models and tools to supply the associated semantics of identified locations. We considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application of our approach in two compelling healthcare use cases, and empirically validated our localization approach at the emergency unit of a large Canadian pediatric hospital.},

keywords = {Activities of daily living, Ambient assisted living, Ambient Intelligence, Ambient sensors, Chronic disease self-management, Data fusion, eHealth Platform, Indoor Localization, Machine Learning, Self-Management, Semantic Web, Virtual care},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1177/0840470419846134,

title = {Intelligent health data analytics: A convergence of artificial intelligence and big data},

author = {Syed Sibte Raza Abidi and Samina Raza Abidi},

url = {https://doi.org/10.1177/0840470419846134},

doi = {10.1177/0840470419846134},

year  = {2019},

date = {2019-05-22},

journal = {Healthcare Management Forum},

volume = {32},

number = {4},

pages = {178--182},

abstract = {Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand “how big” is health data. Next, we explain the working of artificial intelligence–based data analytics methods and discuss “what insights” can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.},

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pubstate = {published},

tppubtype = {article}

}

@article{JAFARPOUR2019,

title = {Execution-Time Integration of Clinical Practice Guidelines To Provide Decision Support for Comorbid Conditions},

author = {Borna Jafarpour and Samina Raza Abidi and William Van Woensel and Syed Sibte Raza Abidi},

url = {https://authors.elsevier.com/a/1Yf0D3KEGa1e9B},

doi = {10.1016/j.artmed.2019.02.003},

issn = {0933-3657},

year  = {2019},

date = {2019-01-01},

journal = {Artificial Intelligence in Medicine},

volume = {94},

pages = {117-137},

abstract = {Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen – such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions. To ensure the clinical safety and effectiveness of integrating multiple comorbid CIG, these execution-time difficulties must be considered. Further, when dealing with comorbid conditions, we remark that clinical practitioners typically consider multiple complex solutions, depending on the patient’s health profile. Hence, execution-time flexibility, based on dynamic health parameters, is needed to effectively and safely cope with comorbid conditions. In this work, we introduce a flexible, knowledge-driven and execution-time approach to comorbid CIG integration, based on an OWL ontology with clearly defined integration semantics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/14993,

title = {Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study},

author = {Hani Nabeel Mufti and Gregory Marshal Hirsch and Samina Raza Abidi and Syed Sibte Raza Abidi},

url = {http://www.ncbi.nlm.nih.gov/pubmed/31558433},

doi = {10.2196/14993},

issn = {2291-9694},

year  = {2019},

date = {2019-01-01},

journal = {JMIR Med Inform},

volume = {7},

number = {4},

pages = {e14993},

abstract = {Background: Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. Objective: This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. Methods: We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), na"ive Bayesian, random forest, and decision trees. Results: Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm's performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03). Conclusions: Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model's performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients' outcomes.},

keywords = {delirium; cardiac surgery; machine learning; predictive modeling},

pubstate = {published},

tppubtype = {article}

}

@article{WHEELER20188,

title = {Feasibility and usability of an ontology-based mobile intervention for patients with hypertension},

author = {Tyler S Wheeler and Michael T Vallis and Nicholas B Giacomantonio and Samina R Abidi},

url = {http://www.sciencedirect.com/science/article/pii/S1386505618301710},

doi = {10.1016/j.ijmedinf.2018.08.002},

issn = {1386-5056},

year  = {2018},

date = {2018-11-01},

journal = {International Journal of Medical Informatics},

volume = {119},

pages = {8 - 16},

keywords = {Behaviour change, Chronic disease self-management, Hypertension, Mobile Health, Ontology},

pubstate = {published},

tppubtype = {article}

}

@article{COX2018149c,

title = {Optimizing Primary Care Management of Atrial Fibrillation: The Rationale and Methods of the Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) Study},

author = {Jafna L Cox and Ratika Parkash and Syed SR Abidi and Lehana Thabane and Feng Xie and James MacKillop and Samina R Abidi and Antonio Ciaccia and Shurjeel H Choudhri and A Abusharekh and Joanna Nemis-White},

url = {http://www.sciencedirect.com/science/article/pii/S0002870318301170},

doi = {10.1016/j.ahj.2018.04.008},

issn = {0002-8703},

year  = {2018},

date = {2018-01-01},

volume = {201},

pages = {149 - 157},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articleb,

title = {Preoperative Education System to Assist Patients Undergoing TAVI Surgery: A Digital Health Solution},

author = {Ali Naserian Mojadam and Najaf Nadeem and Hussein Beydoun and Samina Abidi and Ali Rizvi and Syed Abidi},

doi = {10.4172/2157-7420.1000313},

year  = {2018},

date = {2018-01-01},

journal = {Journal of Health & Medical Informatics},

volume = {09},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/jms/Abidi17,

title = {A Knowledge-Modeling Approach to Integrate Multiple Clinical Practice Guidelines to Provide Evidence-Based Clinical Decision Support for Managing Comorbid Conditions},

author = {Samina Abidi},

url = {https://doi.org/10.1007/s10916-017-0841-1},

doi = {10.1007/s10916-017-0841-1},

year  = {2017},

date = {2017-10-16},

journal = {J. Medical Systems},

volume = {41},

number = {12},

pages = {193:1 - 193:19},

keywords = {Clinical Decision Support Systems, Clinical Practice Guidelines, Comorbidities, Knowledge Modelling, Knowledge Translation},

pubstate = {published},

tppubtype = {article}

}

@article{Maghsoud-Lou2017,

title = {Protocol-Driven Decision Support within e-Referral Systems to Streamline Patient Consultation, Triaging and Referrals from Primary Care to Specialist Clinics},

author = {Ehsan Maghsoud-Lou and Sean Christie and Samina Raza Abidi and Syed Sibte Raza Abidi},

url = {https://doi.org/10.1007/s10916-017-0791-7},

doi = {10.1007/s10916-017-0791-7},

issn = {1573-689X},

year  = {2017},

date = {2017-08-01},

journal = {Journal of Medical Systems},

volume = {41},

number = {9},

pages = {139},

abstract = {Patient referral is a protocol where the referring primary care physician refers the patient to a specialist for further treatment. The paper-based current referral process at times lead to communication and operational issues, resulting in either an unfulfilled referral request or an unnecessary referral request. Despite the availability of standardized referral protocols they are not readily applied because they are tedious and time-consuming, thus resulting in suboptimal referral requests. We present a semantic-web based Referral Knowledge Modeling and Execution Framework to computerize referral protocols, clinical guidelines and assessment tools in order to develop a computerized e-Referral system that offers protocol-based decision support to streamline and standardize the referral process. We have developed a Spinal Problem E-Referral (SPER) system that computerizes the Spinal Condition Consultation Protocol (SCCP) mandated by the Halifax Infirmary Division of Neurosurgery (Halifax, Canada) for referrals for spine related conditions (such as back pain). The SPER system executes the ontologically modeled SCCP to determine (i) patient's triaging option as per severity assessments stipulated by SCCP; and (b) clinical recommendations as per the clinical guidelines incorporated within SCCP. In operation, the SPER system identifies the critical cases and triages them for specialist referral, whereas for non-critical cases SPER system provides clinical guideline based recommendations to help the primary care physician effectively manage the patient. The SPER system has undergone a pilot usability study and was deemed to be easy to use by physicians with potential to improve the referral process within the Division of Neurosurgery at QEII Health Science Center, Halifax, Canada.},

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pubstate = {published},

tppubtype = {article}

}

@article{Roy2017b,

title = {Possibilistic Activity Recognition with Uncertain Observations to Support Medication Adherence in an Assisted Ambient Living Setting},

author = {Patrice C. Roy and Samina R. Abidi and Syed S.R. Abidi},

url = {http://www.sciencedirect.com/science/article/pii/S0950705117303246},

doi = {10.1016/j.knosys.2017.07.008},

issn = {09507051},

year  = {2017},

date = {2017-07-06},

journal = {Knowledge-Based Systems},

volume = {133},

pages = {156-173},

abstract = {A recent trend in healthcare is to motivate patients to self-manage their health conditions in home-based settings. Self-management programs guide and motivate patients to achieve self-efficacy in the self-management of their disease through a regime of educational and behavioural modification strategies. To improve self-management programs effectiveness and efficacy, we must consider Ambient Assisted Living (AAL) technologies (smart environments, activity recognition, aid acts planning), since they alleviate issues related to unreliable self-reported data by monitoring self-management activities. To improve self-management programs in smart environments, it is necessary to recognize the occupant behaviour from observed data. Observed data/attributes generated from various sources (sensors, questionnaires, low-level activity recognition) are certain to uncertain (imprecise, incomplete, missing), where several values are plausible instead of only one. Thus, activity recognition must consider heterogeneous observations (sources' types) and uncertainty in the activity recognition inputs (observations). To address this challenge, we propose an activity recognition approach based on possibilistic network classifiers with uncertain observations. We believe that this is the first work to consider possibilistic network classifiers for the recognition of activities in smart environments using uncertain observations. We have validated the approach on 780 synthetic scenarios illustrating behaviours related to medication adherence. The activity classifiers, based on knowledge and beliefs about the activities related to medication adherence, can correctly recognize 79% of an activity current state, which is comparable with approaches based on data-driven naïve Bayesian classifiers. Furthermore, the classification performance only decreases when we have highly partial to complete ignorance about the observations values. Hence, the validations results show the interest of activity recognition based on possibilistic network classifiers for handling uncertain observations.},

keywords = {Activity Recognition, Ambient Intelligence, medication adherence},

pubstate = {published},

tppubtype = {article}

}

@article{Mohammadhassanzadeh2017,

title = {Semantics-based Plausible Reasoning to Extend the Knowledge Coverage of Medical Knowledge Bases for Improved Clinical Decision Support},

author = {Hossein Mohammadhassanzadeh and William Van Woensel and Samina Raza Abidi and Syed Sibte Raza Abidi},

url = {http://rdcu.be/paPY},

doi = {10.1186/s13040-017-0123-y},

year  = {2017},

date = {2017-02-10},

journal = {Journal of BioData Mining},

volume = {10},

number = {7},

abstract = {Background

Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.



Results

We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.



Conclusions

We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.},

keywords = {Analogical reasoning, Inductive generalization, Medical knowledge bases, Plausible reasoning, Semantic Web reasoning},

pubstate = {published},

tppubtype = {article}

}