The ChatGPT Effect: Nursing Education and Generative Artificial Intelligence

This article examines the potential of generative artificial intelligence (AI), such as ChatGPT (Chat Generative Pre-trained Transformer), in nursing education and the associated challenges and recommendations for their use. Generative AI offers potential benefits such as aiding students with assignments, providing realistic patient scenarios for practice, and enabling personalized, interactive learning experiences. However, integrating generative AI in nursing education also presents challenges, including academic integrity issues, the potential for plagiarism and copyright infringements, ethical implications, and the risk of producing misinformation. Clear institutional guidelines, comprehensive student education on generative AI, and tools to detect AI-generated content are recommended to navigate these challenges. The article concludes by urging nurse educators to harness generative AI's potential responsibly, highlighting the rewards of enhanced learning and increased efficiency. The careful navigation of these challenges and strategic implementation of AI is key to realizing the promise of AI in nursing education. [J Nurs Educ. 2024;63(X):XXX-XXX.].

A Conversational Agent for Early Detection of Neurotoxic Effects of Medications through Automated Intensive Observation

We present a fully automated AI-based system for intensive monitoring of cognitive symptoms of neurotoxicity that frequently appear as a result of immunotherapy of hematologic malignancies. Early manifestations of these symptoms are evident in the patient's speech in the form of mild aphasia and confusion and can be detected and effectively treated prior to onset of more serious and potentially life-threatening impairment. We have developed the Automated Neural Nursing Assistant (ANNA) system designed to conduct a brief cognitive assessment several times per day over the telephone for 5-14 days following infusion of the immunotherapy medication. ANNA uses a conversational agent based on a large language model to elicit spontaneous speech in a semi-structured dialogue, followed by a series of brief language-based neurocognitive tests. In this paper we share ANNA's design and implementation, results of a pilot functional evaluation study, and discuss technical and logistic challenges facing the introduction of this type of technology in clinical practice. A large-scale clinical evaluation of ANNA will be conducted in an observational study of patients undergoing immunotherapy at the University of Minnesota Masonic Cancer Center starting in the Fall 2023.

TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments

The evidence is growing that machine and deep learning methods can learn the subtle differences between the language produced by people with various forms of cognitive impairment such as dementia and cognitively healthy individuals. Valuable public data repositories such as TalkBank have made it possible for researchers in the computational community to join forces and learn from each other to make significant advances in this area. However, due to variability in approaches and data selection strategies used by various researchers, results obtained by different groups have been difficult to compare directly. In this paper, we present TRESTLE (Toolkit for Reproducible Execution of Speech Text and Language Experiments), an open source platform that focuses on two datasets from the TalkBank repository with dementia detection as an illustrative domain. Successfully deployed in the hackallenge (Hackathon/Challenge) of the International Workshop on Health Intelligence at AAAI 2022, TRESTLE provides a precise digital blueprint of the data pre-processing and selection strategies that can be reused via TRESTLE by other researchers seeking comparable results with their peers and current state-of-the-art (SOTA) approaches.

Using graph rewriting to operationalize medical knowledge for the revision of concurrently applied clinical practice guidelines

Clinical practice guidelines (CPGs) are patient management tools that synthesize medical knowledge into an actionable format. CPGs are disease specific with limited applicability to the management of complex patients suffering from multimorbidity. For the management of these patients, CPGs need to be augmented with secondary medical knowledge coming from a variety of knowledge repositories. The operationalization of this knowledge is key to increasing CPGs' uptake in clinical practice. In this work, we propose an approach to operationalizing secondary medical knowledge inspired by graph rewriting. We assume that the CPGs can be represented as task network models, and provide an approach for representing and applying codified medical knowledge to a specific patient encounter. We formally define revisions that model and mitigate adverse interactions between CPGs and we use a vocabulary of terms to instantiate these revisions. We demonstrate the application of our approach using synthetic and clinical examples. We conclude by identifying areas for future work with the vision of developing a theory of mitigation that will facilitate the development of comprehensive decision support for the management of multimorbid patients.

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

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).

Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world

This editorial article aims to highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI, and Ethical and Value-based AI in order to cope with future complex socioeconomic and public health issues.

Predictive models for social functioning in healthy young adults: A machine learning study integrating neuroanatomical, cognitive, and behavioral data

Poor social functioning is an emerging public health problem associated with physical and mental health consequences. Developing prognostic tools is critical to identify individuals at risk for poor social functioning and guide interventions. We aimed to inform prediction models of social functioning by evaluating models relying on bio-behavioral data using machine learning. With data from the Human Connectome Project Healthy Young Adult sample (age 22-35, N = 1,101), we built Support Vector Regression models to estimate social functioning from variable sets of brain morphology to behavior with increasing complexity: 1) brain-only model, 2) brain-cognition model, 3) cognition-behavioral model, and 4) combined brain-cognition-behavioral model. Predictive accuracy of each model was assessed and the importance of individual variables for model performance was determined. The combined and cognition-behavioral models significantly predicted social functioning, whereas the brain-only and brain-cognition models did not. Negative affect, psychological wellbeing, extraversion, withdrawal, and cortical thickness of the rostral middle-frontal and superior-temporal regions were the most important predictors in the combined model. Results demonstrate that social functioning can be accurately predicted using machine learning methods. Behavioral markers may be more significant predictors of social functioning than brain measures for healthy young adults and may represent important leverage points for preventative intervention.

Nurses' Utilization of Information Resources for Patient Care Tasks: A Survey of Critical Care Nurses in an Urban Hospital Setting

Critical care nurses manage complex patient care interventions under dynamic, time-sensitive and constrained conditions, yet clinical decision support systems for nurses are limited compared with advanced practice healthcare providers. In this work, we study and analyze nurses' information-seeking behaviors to inform the development of a clinical decision support system that supports nurses. Nurses from an urban midwestern hospital were recruited to complete an online survey containing eight open-ended questions about resource utilization for various nursing tasks and open space for additional insights. Frequencies and percentages were calculated for resource type, bivariate analyses using Pearson's χ2 test were conducted for differences in resources utilization by years of experience, and content analysis of free text was completed. Forty-five nurses (response rate, 19.6%) identified 38 unique resources, which we organized into a resource taxonomy. Institutional applications were the most common type of resource used (35.6% of all responses) but accounted for only 15.4% of respondents' "go-to resources," suggesting potential areas for improvement. Our findings highlight that knowing where to look for information, the existence of comprehensive information, and fast and easy retrieval of information are key resource seeking attributes that must be considered when designing a clinical decision support system.

Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases

Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines offer conflicting advice. A number of research groups are developing computer-interpretable guideline (CIG) modeling formalisms that integrate recommendations from multiple Clinical Practice Guidelines (CPGs) for knowledge-based multimorbidity decision support. In this paper we describe work towards the development of a framework for comparing the different approaches to multimorbidity CIG-based clinical decision support (MGCDS). We present (1) a set of features for MGCDS, which were derived using a literature review and evaluated by physicians using a survey, and (2) a set of benchmarking case studies, which illustrate the clinical application of these features. This work represents the first necessary step in a broader research program aimed at the development of a benchmark framework that allows for standardized and comparable MGCDS evaluations, which will facilitate the assessment of functionalities of MGCDS, as well as highlight important gaps in the state-of-the-art. We also outline our future work on developing the framework, specifically, (3) a standard for reporting MGCDS solutions for the benchmark case studies, and (4) criteria for evaluating these MGCDS solutions. We plan to conduct a large-scale comparison study of existing MGCDS based on the comparative framework.

Fear of Progression in chronic illnesses other than cancer: A systematic review and meta-analysis of a transdiagnostic construct

Fear of cancer recurrence (FCR) is the most common psychosocial issue amongst cancer survivors. However, fear of progression (FoP) has been studied outside of the cancer context. This review aimed to: (1) meta-synthesize qualitative studies of FoP in illnesses other than cancer; and (2) quantify the relationship between FoP and anxiety, depression, and quality of life (QoL) in non-cancer chronic illnesses. We identified 25 qualitative and 11 quantitative studies in a range of chronic illnesses. Participants described fears of progression and recurrence of their illness, including fears of dying, and fears of becoming a burden to family. Fears were often triggered by downward comparison (i.e. seeing people worse off than themselves). Participants coped in different ways, including by accepting the illness or seeking knowledge. Those for whom these fears caused distress reported hypervigilance to physical symptoms and avoidance. Distress, and seeking information, were associated with adherence. In quantitative analyses, FoP was moderately associated with QoL, and strongly associated with anxiety and depression. These results suggest that FoP in illnesses other than cancer is similar to FCR. FoP appears to be an important transdiagnostic construct associated with distress. Evidence-based FCR interventions could be adapted to better manage FoP in other illnesses.

A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment

We propose a methodological framework to support the development of personalized courses that improve patients' understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes-condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific course content according to Bloom's taxonomy. Customizing educational materials along these learning axes stimulates and sustains patients' attention when learning about their conditions or treatment options. Our proposed framework creates a personalized course that prepares patients for their meetings with specialists and educates them about their prescribed treatment. We posit that the improvement in patients' understanding of prescribed care will result in better outcomes and we validate that the constructs of our framework are appropriate for representing content and deriving personalized courses for two use cases: anticoagulation treatment of an atrial fibrillation patient and lower back pain management to treat a lumbar degenerative disc condition. We conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the framework among the target patient population and medical practitioners.

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative

Aim

To develop a consensus paper on the central points of an international invitational think‐tank on nursing and artificial intelligence (AI).

Methods

We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3‐day invitational think tank in autumn 2019. Activities included a pre‐event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities.

Implications for nursing

Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice.

Conclusion

There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems.

Impact

We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs.

MitPlan: A planning approach to mitigating concurrently applied clinical practice guidelines

As the population ages, patients' complexity and the scope of their care is increasing. Over 60% of the population is 65 years of age or older and suffers from multi-morbidity, which is associated with two times as many patient-physician encounters. Yet clinical practice guidelines (CPGs) are developed to treat a single disease. To reconcile these two competing issues, previously we developed a framework for mitigation, i.e., identifying and addressing adverse interactions in multi-morbid patients managed according to multiple CPGs. That framework relies on first-order logic (FOL) to represent CPGs and secondary medical knowledge and FOL theorem proving to establish valid patient management plans. In the work presented here, we leverage our earlier research and simplify the mitigation process by representing it as a planning problem using the Planning Domain Definition Language (PDDL). This new framework, called MitPlan, identifies and addresses adverse interactions using durative planning actions that embody clinical actions (including medication administration and patient testing), supports a physician-defined length of planning horizons, and optimizes plans based on patient preferences and action costs. It supports a variety of criteria when developing management plans, including the total cost of prescribed treatment and the cost of the revisions to be introduced. The solution to MitPlan's planning problem is a sequence of timed actions that are easy to interpret when creating a management plan. We demonstrate MitPlan's capabilities using illustrative and clinical case studies.

How Do Spinal Surgeons Perceive The Impact of Factors Used in Post-Surgical Complication Risk Scores?

When deciding about surgical treatment options, an important aspect of the decision-making process is the potential risk of complications. A risk assessment performed by a spinal surgeon is based on their knowledge of the best available evidence and on their own clinical experience. The objective of this work is to demonstrate the differences in the way spine surgeons perceive the importance of attributes used to calculate risk of post-operative and quantify the differences by building individual formal models of risk perceptions. We employ a preference-learning method - ROR-UTADIS - to build surgeon-specific additive value functions for risk of complications. Comparing these functions enables the identification and discussion of differences among personal perceptions of risk factors. Our results show there exist differences in surgeons' perceived factors including primary diagnosis, type of surgery, patient's age, body mass index, or presence of comorbidities.

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

Abstract WP382: Using Natural Language Processing Algorithms to Identify Stroke Cases and Stroke Subtypes From Neuroimaging Reports

Background/Objective: The long-term goal of our research is to develop automated, accurate methods for conducting stroke outcome surveillance across large populations. To facilitate this, we developed natural language processing (NLP) based machine learning algorithms to classify stroke cases and sub-type strokes using neuroimaging reports. We report on the performance of our algorithms.

Methods: Our population of interest included patients with stroke symptoms presenting to the emergency room of our large academic healthcare system. We randomly sampled 332 probable stroke cases. A trained neurologist validated stroke diagnoses using neuroimaging reports. Data preprocessing included cleaning and normalizing the reports into a standardized format. We trained and tested machine learning algorithms using the formatted reports. The NLP-based algorithms predicted a stroke diagnosis (binary classification) and a stroke type diagnosis (multiclass classification) using n-grams of length 1 (i.e., ‘stroke’, ‘hemorrhage’) through 3 (i.e., ‘no mass effect’), term-frequency weighting, and feature dimensionality reduction via truncated singular value decomposition (SVD). We report algorithm performance using the area under the receiver operating characteristic curve (AUC-ROC). Classification methods we tested included Multinomial Naïve Bayes, Logistic Regression, Random Forest Classifier, and Support Vector Machine (SVM).

Results: The highest performing algorithm for both stroke and stroke sub-type classification contained 1 to 2 n-grams, no term-frequency, and 200 SVD components. For the stroke case detection, SVM achieved the best AUC-ROC of 95.6%. For stroke sub-type detection, SVM also yielded the highest AUC-ROC of 93.5% for no stroke, 92.3% for ischemic stroke, 91.9% for intraparenchymal hemorrhage, and 94.8% for subarachnoid hemorrhage.

Conclusions: We report very promising results in our pilot study using machine learning algorithms to classify stroke cases from neuroimaging reports. Feature selection on this pilot data revealed a subset of words that are highly effective in categorizing stroke. Future work will focus on algorithm improvement, finer grained stroke sub-type stratification, and multi-label phenotyping.

Reports of the Workshops of the 32nd AAAI Conference on Artificial Intelligence

The AAAI-18 workshop program included 15 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 2–7, 2018, at the Hilton New Orleans Riverside in New Orleans, Louisiana, USA. This report contains summaries of the Affective Content Analysis workshop; the Artificial Intelligence Applied to Assistive Technologies and Smart Environments; the AI and Marketing Science workshop; the Artificial Intelligence for Cyber Security workshop; the AI for Imperfect-Information Games; the Declarative Learning Based Programming workshop; the Engineering Dependable and Secure Machine Learning Systems workshop; the Health Intelligence workshop; the Knowledge Extraction from Games workshop; the Plan, Activity, and Intent Recognition workshop; the Planning and Inference workshop; the Preference Handling workshop; the Reasoning and Learning for Human-Machine Dialogues workshop; and the the AI Enhanced Internet of Things Data Processing for Intelligent Applications workshop.

Learning Neuroscience with Technology: A Scaffolded, Active Learning Approach

Mobile applications (apps) for learning technical scientific content are becoming increasingly popular in educational settings. Neuroscience is often considered complex and challenging for most students to understand conceptually. iNeuron is a recently developed iOS app that teaches basic neuroscience in the context of a series of scaffolded challenges to create neural circuits and increase understanding of nervous system structure and function. In this study, four different ways to implement the app within a classroom setting were explored. The goal of the study was to determine the app's effectiveness under conditions closely approximating real-world use, and to evaluate whether collaborative play and student-driven navigational features contributed to its effectiveness. Students used the app either individually or in small groups, and used a version with either a fixed or variable learning sequence. Student performance on a pre- and post- neuroscience content assessment was analyzed and compared between students who used the app and a control group receiving standard instruction, and logged app data were analyzed. Significantly greater learning gains were found for all students who used the app compared to control. All four implementation modes were effective in producing student learning gains relative to controls, but did not differ in their effectiveness to one another. In addition, students demonstrated transfer of information learned in one context to another within the app. These results suggest that teacher-led neuroscience instruction can be effectively supported by a scaffolded, technology-based curriculum which can be implemented in multiple ways to enhance student learning.

Health intelligence: how artificial intelligence transforms population and personalized health

Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering innovations in medical devices have prompted demands for more comprehensive and coherent strategies to address the most fundamental questions in health care and medicine. Theory, methods, and models from artificial intelligence (AI) are changing the health care landscape in clinical and community settings and have already shown promising results in multiple applications in healthcare including, integrated health information systems, patient education, geocoding health data, social media analytics, epidemic and syndromic surveillance, predictive modeling and decision support, mobile health, and medical imaging (e.g. radiology and retinal image analyses). Health intelligence uses tools and methods from artificial intelligence and data science to provide better insights, reduce waste and wait time, and increase speed, service efficiencies, level of accuracy, and productivity in health care and medicine.

Reports of the Workshops of the Thirty-First AAAI Conference on Artificial Intelligence

The AAAI-17 workshop program included 17 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 4-5, 2017 at the Hilton San Francisco Union Square in San Francisco, California, USA. This report contains summaries of 12 of the workshops, and brief abstracts of the remaining 5

Comprehensive mitigation framework for concurrent application of multiple clinical practice guidelines

In this work we propose a comprehensive framework based on first-order logic (FOL) for mitigating (identifying and addressing) interactions between multiple clinical practice guidelines (CPGs) applied to a multi-morbid patient while also considering patient preferences related to the prescribed treatment. With this framework we respond to two fundamental challenges associated with clinical decision support: (1) concurrent application of multiple CPGs and (2) incorporation of patient preferences into the decision making process. We significantly expand our earlier research by (1) proposing a revised and improved mitigation-oriented representation of CPGs and secondary medical knowledge for addressing adverse interactions and incorporating patient preferences and (2) introducing a new mitigation algorithm. Specifically, actionable graphs representing CPGs allow for parallel and temporal activities (decisions and actions). Revision operators representing secondary medical knowledge support temporal interactions and complex revisions across multiple actionable graphs. The mitigation algorithm uses the actionable graphs, revision operators and available (and possibly incomplete) patient information represented in FOL. It relies on a depth-first search strategy to find a valid sequence of revisions and uses theorem proving and model finding techniques to identify applicable revision operators and to establish a management scenario for a given patient if one exists. The management scenario defines a safe (interaction-free) and preferred set of activities together with possible patient states. We illustrate the use of our framework with a clinical case study describing two patients who suffer from chronic kidney disease, hypertension, and atrial fibrillation, and who are managed according to CPGs for these diseases. While in this paper we are primarily concerned with the methodological aspects of mitigation, we also briefly discuss a high-level proof of concept implementation of the proposed framework in the form of a clinical decision support system (CDSS). The proposed mitigation CDSS "insulates" clinicians from the complexities of the FOL representations and provides semantically meaningful summaries of mitigation results. Ultimately we plan to implement the mitigation CDSS within our MET (Mobile Emergency Triage) decision support environment.

Reports of the 2016 AAAI Workshop Program

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) was held at the beginning of the conference, February 12-13, 2016. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers and users on topics of current interest. To foster interaction and exchange of ideas, the workshops were kept small, with 25-65 participants. Attendance was sometimes limited to active participants only, but most workshops also allowed general registration by other interested individuals. The AAAI-16 Workshops were an excellent forum for exploring emerging approaches and task areas, for bridging the gaps between AI and other fields or between subfields of AI, for elucidating the results of exploratory research, or for critiquing existing approaches. The fifteen workshops held at AAAI-16 were Artificial Intelligence Applied to Assistive Technologies and Smart Environments (WS-16-01), AI, Ethics, and Society (WS-16-02), Artificial Intelligence for Cyber Security (WS-16-03), Artificial Intelligence for Smart Grids and Smart Buildings (WS-16-04), Beyond NP (WS-16-05), Computer Poker and Imperfect Information Games (WS-16-06), Declarative Learning Based Programming (WS-16-07), Expanding the Boundaries of Health Informatics Using AI (WS-16-08), Incentives and Trust in Electronic Communities (WS-16-09), Knowledge Extraction from Text (WS-16-10), Multiagent Interaction without Prior Coordination (WS-16-11), Planning for Hybrid Systems (WS-16-12), Scholarly Big Data: AI Perspectives, Challenges, and Ideas (WS-16-13), Symbiotic Cognitive Systems (WS-16-14), and World Wide Web and Population Health Intelligence (WS-16-15).

Expanding a First-Order Logic Mitigation Framework to Handle Multimorbid Patient Preferences

The increasing prevalence of multimorbidity is a challenge for physicians who have to manage a constantly growing number of patients with simultaneous diseases. Adding to this challenge is the need to incorporate patient preferences as key components of the care process, thanks in part to the emergence of personalized and participatory medicine. In our previous work we proposed a framework employing first order logic to represent clinical practice guidelines (CPGs) and to mitigate possible adverse interactions when concurrently applying multiple CPGs to a multimorbid patient. In this paper, we describe extensions to our methodological framework that (1) broaden our definition of revision operators to support required and desired types of revisions defined in secondary knowledge sources, and (2) expand the mitigation algorithm to apply revisions based on their type. We illustrate the capabilities of the expanded framework using a clinical case study of a multimorbid patient with stable cardiac artery disease who suffers a sudden onset of deep vein thrombosis.

Importance of early absolute lymphocyte count after allogeneic stem cell transplantation: a retrospective study

INTRODUCTION

Early lymphocyte recovery after allogeneic hematopoietic stem cell transplantation (HSCT) is related to the prevention of serious infections and the clearing of residual tumor cells.

METHODS

We analyzed the absolute lymphocyte count at 20 (D+20) and 30 (D+30) days after HSCT in 100 patients with malignant hematologic diseases and correlated with the risk of transplant-related mortality, overall survival (OS), disease-free survival (DFS), nonrelapsed mortality (NRM), and risk of infection.

RESULTS

Patients presenting with lymphocyte counts of <300 × 103/μL on D+30 have a 3.76 times greater risk of death in <100 days. Over a medium follow-up of 20 months OS, DFS, and NRM were similar between the groups.

CONCLUSION

In our group of patients delayed lymphocyte recovery after HSCT was a predictor of early death post-HSCT.

Reports on the 2014 AAAI Fall Symposium Series

The AAAI 2014 Fall Symposium Series was held Thursday through Saturday, November 13–15, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the seven symposia were Artificial Intelligence for Human-Robot Interaction, Energy Market Prediction, Expanding the Boundaries of Health Informatics Using AI, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences, Natural Language Access to Big Data, and The Nature of Humans and Machines: A Multidisciplinary Discourse. The highlights of each symposium are presented in this report.

First-order logic theory for manipulating clinical practice guidelines applied to comorbid patients: a case study

Clinical practice guidelines (CPGs) implement evidence-based medicine designed to help generate a therapy for a patient suffering from a single disease. When applied to a comorbid patient, the concurrent combination of treatment steps from multiple CPGs is susceptible to adverse interactions in the resulting combined therapy (i.e., a therapy established according to all considered CPGs). This inability to concurrently apply CPGs has been shown to be one of the key shortcomings of CPG uptake in a clinical setting1. Several research efforts are underway to address this issue such as the K4CARE2 and GuideLine INteraction Detection Assistant (GLINDA)3 projects and our previous research on applying constraint logic programming to developing a consistent combined therapy for a comorbid patient4. However, there is no generalized framework for mitigation that effectively captures general characteristics of the problem while handling nuances such as time and ordering requirements imposed by specific CPGs. In this paper we propose a first-order logic-based (FOL) approach for developing a generalized framework of mitigation. This approach uses a meta-algorithm and entailment properties to mitigate (i.e., identify and address) adverse interactions introduced by concurrently applied CPGs. We use an illustrative case study of a patient suffering from type 2 diabetes being treated for an onset of severe rheumatoid arthritis to show the expressiveness and robustness of our proposed FOL-based approach, and we discuss its appropriateness as the basis for the generalized theory.

The AAAI-13 Conference Workshops

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14–15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statistical Relational Artificial Intelligence (WS-13-16).

Clinical practice guidelines and comorbid diseases: a MiniZinc representation of guideline models for mitigating adverse interactions

Managing a patient with comorbid diseases according to multiple clinical practice guidelines (CPGs) may result in adverse interactions that need to be mitigated (identified and addressed) so a safe therapy can be devised. However, mitigation poses both clinical and methodological challenges. It requires extensive domain knowledge and calls for advanced CPG models and efficient algorithms to process them. We respond to the above challenges by describing our algorithm that mitigates interactions between pairs of CPGs. The algorithm creates logical models of analyzed CPGs and uses constraint logic programming (CLP) together with domain knowledge, codified as interaction and revision operators, to process them. Logical CPG models are transformed into CLP-CPG models that are solved to find a safe therapy. We represent these CLP-CPG models using MiniZinc, a standard language for CLP models. As motivation and illustration of our mitigation algorithm we use a clinical case study describing a patient managed for hypertension and deep vein thrombosis according to two individual CPGs. We apply the algorithm to this scenario and present MiniZinc representations of the constructed CLP-CPG models.

Reconciling pairs of concurrently used clinical practice guidelines using Constraint Logic Programming

This paper describes a new methodological approach to reconciling adverse and contradictory activities (called points of contention) occurring when a patient is managed according to two or more concurrently used clinical practice guidelines (CPGs). The need to address these inconsistencies occurs when a patient with more than one disease, each of which is a comorbid condition, has to be managed according to different treatment regimens. We propose an automatic procedure that constructs a mathematical guideline model using the Constraint Logic Programming (CLP) methodology, uses this model to identify and mitigate encountered points of contention, and revises the considered CPGs accordingly. The proposed procedure is used as an alerting mechanism and coupled with a guideline execution engine warns the physician about potential problems with the concurrent application of two or more guidelines. We illustrate the operation of our procedure in a clinical scenario describing simultaneous use of CPGs for duodenal ulcer and transient ischemic attack.

Two neglected categories of immigrants to Canada: temporary immigrants and returning Canadians

Results are presented from an attempt to improve Canadian statistics on international migration so as to comply with U.N. guidelines by including data on long-term residents with temporary status and Canadian citizens and permanent residents returning from abroad. "The estimation procedures involve extensive operations on three Canadian administrative data systems: the Visitors Immigration Data System of Employment and Immigration Canada; the Family Allowances Files of Health and Welfare Canada; and the Customs and Excise Files of Revenue Canada. These data are used to produce the number of immigrants in both of the neglected categories, as well as to calculate the geographic (origin and destination) and demographic (sex, age, marital status) structures of these groups. Results of the analysis of estimates for the period 1982-1988 show that, due to their size and characteristics, both of these neglected categories of immigrants constitute a significant part of immigration to Canada, and their importance has and will continue to increase over time."

Emotional and behavioral symptoms in children with acute leukemia

BACKGROUND AND OBJECTIVES

The diagnosis of leukemia is probably one of the most severe stressors that children can experience and may be associated with emotional and behavioral symptoms indicating comorbidity with mental health disorders. This study aims to evaluate the presence of emotional and behavioral symptoms in children with acute leukemia exposed to chemotherapy from outpatient services at two university hospitals in Brazil.

DESIGN AND METHODS

In this cross-sectional study, emotional and behavioral symptoms were assessed using the Children Behavior Checklist (CBCL) in three groups of children aged 5-14 years: a) children with acute leukemia (n = 21); b) children with blood dyscrasias (n = 21); c) children evaluated or treated in a pediatric outpatient service (n = 33).

RESULTS

Children with blood dyscrasias had significantly few symptoms of externalization (delinquent and aggressive behavior) than pediatric controls (p< 0.05). Children with leukemia did not differ from the two other groups regarding symptoms of externalization. No significant difference on the scores of the CBCL internalization dimension (anxiety, depression, somatic symptoms and withdrawn) was found among the three groups.

INTERPRETATION AND CONCLUSIONS

These findings seem to indicate that children with acute leukemia do not have more emotional or behavioral symptoms than children with benign hematologic or physical diseases suggesting that comorbidity with mental disorders is not higher in children with acute leukemia than in children in the other two groups.

A contribution of the Asian female immigrants into the Canadian population

"In Canada, the proportion of women among immigrants fluctuates around 50 percent, with a slight increase in recent years. Another important characteristic of immigration...is a radical change in the composition of origin of flows in the past three decades--European-dominated streams have been replaced by those originating mostly in Asia. This paper focuses on female Asian immigrants in Canada.... Major Asian source countries of female immigrants (Hong Kong, Philippines, India, China, Sri Lanka, Taiwan, Lebanon and Iran) give evidence to the growing importance of political push factors and sending countries' policies-facilitation factors as crucial determinants of international migration."

The elderly and international migration in Canada: 1971-1986

"This paper addresses issues of elderly immigrants in Canada within two areas: their characteristics as immigrants and their contribution to the social phenomenon of the aging of the Canadian population. The patterns of net immigration of persons aged 60 years and over are identified according to sex, age and place of origin of immigrants.... Emigration of this group is studied separately. The results of the analysis demonstrate that the elderly immigrant population displays migration patterns significantly different from that of the total immigrant population. Older immigrants participate even more extensively than their younger counterparts in the process of remigration or return migration. As a consequence, an increase of the proportion of older immigrants...does not necessarily accelerate the aging of the Canadian population." (SUMMARY IN ITA)

Foreign-born Canadian emigrants and their characteristics (1981-1986)

"This article provides estimates of levels and structures of recent return migration from Canada. Estimates are distinguished according to sex, period of immigration and place of birth of foreign-born emigrants. Special attention is paid to propensity to return. The impact of return migration on change of foreign-born populations is also evaluated." This is a revised version of a paper originally presented at the 1989 Annual Meeting of the Population Association of America (see Population Index, Vol. 55, No. 3, Fall 1989, p. 426).