Knowledge acquisition process for intelligent decision support in critical health care

An efficient triage system is a good way to avoid some future problems and, how much quicker it is, more the patient can benefit. However, a limitation still exists, the triage system are general and not specific to each case. Manchester Triage System is a reliable known system and is focused in the emergency department of a hospital. When applied to specific patients’ conditions, such the pregnancy has several limitations. To overcome those limitations, an alternative triage system, integrated into an intelligent decision support system, was developed. The system classifies patients according to the severity of their clinical condition, establishing clinical priorities and not diagnosis. According to the woman urgency of attendance or problem type, it suggests one of the three possible categories of the triage. This paper presents the overall knowledge acquisition cycle associated to the workflow of patient arrival and the inherent decision making process. Results showed that this new approach enhances the efficiency and the safety through the appropriate use of resources and by assisting the right patient in the right place, reducing the waiting triage time and the number of women in general urgency.Fundação para a Ciência e a Tecnologia (FCT

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions

We describe research towards creating a computational model for recognizing interpersonal trust in social interactions. We found that four negative gestural cues—leaning-backward, face-touching, hand-touching, and crossing-arms—are together predictive of lower levels of trust. Three positive gestural cues—leaning-forward, having arms-in-lap, and open-arms—are predictive of higher levels of trust. We train a probabilistic graphical model using natural social interaction data, a “Trust Hidden Markov Model” that incorporates the occurrence of these seven important gestures throughout the social interaction. This Trust HMM predicts with 69.44% accuracy whether an individual is willing to behave cooperatively or uncooperatively with their novel partner; in comparison, a gesture-ignorant model achieves 63.89% accuracy. We attempt to automate this recognition process by detecting those trust-related behaviors through 3D motion capture technology and gesture recognition algorithms. We aim to eventually create a hierarchical system—with low-level gesture recognition for high-level trust recognition—that is capable of predicting whether an individual finds another to be a trustworthy or untrustworthy partner through their nonverbal expressions

Holistic System Design for Distributed National eHealth Services

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The Social Arena of Mental Health Act Apprehensions: An Examination of Partnership between Police and Hospital Services

Improvement in the collaboration between police and emergency hospital services in responding to citizens in mental health (MH) crisis has been identified as vital by researchers and service organizations alike (Human Services and Justice Coordinating Committee, 2013). Research suggests that collaboration between these two services is inhibited by a lack of clear communication, protection of patient privacy, insufficient training, siloing of services, and safety concerns for patients and staff (Cotton & Coleman, 2010). Consequences of inadequate cooperation between police and hospital services has resulted in lengthy emergency room (ER) wait times for those apprehended by police officers under the Mental Health Act (MHA), poor patient follow-up, and frustration between services (Cotton, 2004). Recently, some police services have begun to implement formal agreements with local hospitals to enhance collaboration in Memorandums of Understanding (MOUs) in caring for those in MH crisis. The purpose of this study was to investigate these emerging agreements to gain insight into how they collectively framed their partnerships and responsibilities, identified their common objectives, and emphasized significant concerns in the context of MHA apprehensions. A Social Arena (Star & Griesemer, 1989) theoretical framework was used to argue that MOUs act as “boundary objects” (p.393). The boundary object comes into play when the diverse worlds, values and ideologies of the police service and the ER service come together to shape their collaboration, negotiate identities and roles, and bridge gaps in cooperating. Implications of such boundary objects are discussed

The Social Arena of Mental Health Act Apprehensions: An Examination of Partnership between Police and Hospital Services

Improvement in the collaboration between police and emergency hospital services in responding to citizens in mental health (MH) crisis has been identified as vital by researchers and service organizations alike (Human Services and Justice Coordinating Committee, 2013). Research suggests that collaboration between these two services is inhibited by a lack of clear communication, protection of patient privacy, insufficient training, siloing of services, and safety concerns for patients and staff (Cotton & Coleman, 2010). Consequences of inadequate cooperation between police and hospital services has resulted in lengthy emergency room (ER) wait times for those apprehended by police officers under the Mental Health Act (MHA), poor patient follow-up, and frustration between services (Cotton, 2004). Recently, some police services have begun to implement formal agreements with local hospitals to enhance collaboration in Memorandums of Understanding (MOUs) in caring for those in MH crisis. The purpose of this study was to investigate these emerging agreements to gain insight into how they collectively framed their partnerships and responsibilities, identified their common objectives, and emphasized significant concerns in the context of MHA apprehensions. A Social Arena (Star & Griesemer, 1989) theoretical framework was used to argue that MOUs act as “boundary objects” (p.393). The boundary object comes into play when the diverse worlds, values and ideologies of the police service and the ER service come together to shape their collaboration, negotiate identities and roles, and bridge gaps in cooperating. Implications of such boundary objects are discussed

Using Case-Based Reasoning for Simulation Modeling in Healthcare

The healthcare system is always defined as a complex system. At its core, it is a system composed of people and processes and requires performance of different tasks and duties. This complexity means that the healthcare system has many stakeholders with different interests, resulting in the emergence of many problems such as increasing healthcare costs, limited resources and low utilization, limited facilities and workforce, and poor quality of services. The use of simulation techniques to aid in solving healthcare problems is not new, but it has increased in recent years. This application faces many challenges, including a lack of real data, complicated healthcare decision making processes, low stakeholder involvement, and the working environment in the healthcare field. The objective of this research is to study the utilization of case-based reasoning in simulation modeling in the healthcare sector. This utilization would increase the involvement of stakeholders in the analysis process of the simulation modeling. This involvement would help in reducing the time needed to build the simulation model and facilitate the implementation of results and recommendations. The use of case-based reasoning will minimize the required efforts by automating the process of finding solutions. This automation uses the knowledge in the previously solved problems to develop new solutions. Thus, people could utilize the simulation modeling with little knowledge about simulation and the working environment in the healthcare field. In this study, a number of simulation cases from the healthcare field have been collected to develop the case-base. After that, an indexing system was created to store these cases in the case-base. This system defined a set of attributes for each simulation case. After that, two retrieval approaches were used as retrieval engines. These approaches are K nearest neighbors and induction tree. The validation procedure started by selecting a case study from the healthcare literature and implementing the proposed method in this study. Finally, healthcare experts were consulted to validate the results of this study