Juan Fdez-Olivares

Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks

OBJECTIVE This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To this end, a translation from a time-annotated computer-interpretable guideline (CIG) model of a clinical protocol into a temporal hierarchical task network (HTN) planning domain is presented. MATERIALS AND METHODS The proposed method uses a knowledge-driven reasoning process to translate knowledge previously described in a CIG into a corresponding HTN Planning and Scheduling domain, taking advantage of HTNs known ability to (i) dynamically cope with temporal and resource constraints, and (ii) automatically generate customized plans. The proposed method, focusing on the representation of temporal knowledge and based on the identification of workflow and temporal patterns in a CIG, makes it possible to automatically generate time-annotated and resource-based care pathways tailored to the needs of any possible patient profile. RESULTS The proposed translation is illustrated through a case study based on a 70 pages long clinical protocol to manage Hodgkins disease, developed by the Spanish Society of Pediatric Oncology. We show that an HTN planning domain can be generated from the corresponding specification of the protocol in the Asbru language, providing a running example of this translation. Furthermore, the correctness of the translation is checked and also the management of ten different types of temporal patterns represented in the protocol. By interpreting the automatically generated domain with a state-of-art HTN planner, a time-annotated care pathway is automatically obtained, customized for the patients and institutional needs. The generated care pathway can then be used by clinicians to plan and manage the patients long-term care. CONCLUSION The described methodology makes it possible to automatically generate patient-tailored care pathways, leveraging an incremental knowledge-driven engineering process that starts from the expert knowledge of medical professionals. The presented approach makes the most of the strengths inherent in both CIG languages and HTN planning and scheduling techniques: for the former, knowledge acquisition and representation of the original clinical protocol, and for the latter, knowledge reasoning capabilities and an ability to deal with complex temporal and resource constraints. Moreover, the proposed approach provides immediate access to technologies such as business process management (BPM) tools, which are increasingly being used to support healthcare processes.

Automatic generation of temporal planning domains for e-learning problems

AI Planning & Scheduling techniques are being widely used to adapt learning paths to the special features and needs of students both in distance learning and lifelong learning environments. However, instructors strongly rely on Planning & Scheduling experts to encode and review the domains for the planner/scheduler to work. This paper presents an approach to automatically extract a fully operational HTN planning domain and problem from a learning objects repository without requiring the intervention of any planning expert, and thus enabling an easier adoption of this technology in practice. The results of a real experiment with a small group of students within an e-Learning private company in Spain are also shown.

SUPPORTING CLINICAL PROCESSES AND DECISIONS BY HIERARCHICAL PLANNING AND SCHEDULING

This article is focused on how a general‐purpose hierarchical planning representation, based on the hierarchical task networks (HTN) paradigm, can be used to support the representation of oncology treatment protocols. The planning algorithm used is a temporally extended HTN planning process capable of interpreting such representation and generating oncology treatment plans that have been proven to support clinical decisions in the area of pediatrics oncology.

A Multi-agent Planning Approach for the Generation of Personalized Treatment Plans of Comorbid Patients

This work addresses the generation of a personalized treatment plan from multiple clinical guidelines, for a patient with multiple diseases (comorbid patient), as a multi-agent cooperative planning process that provides support to collaborative medical decision-making. The proposal is based on a multi-agent planning architecture in which each agent is capable of (1) planning a personalized treatment from a temporal Hierarchical Task Network (HTN) representation of a single-disease guideline, and (2) coordinating with other planning agents by both sharing disease specific knowledge, and resolving the eventual conflicts that may arise when conciliating different guidelines by merging single-disease treatment plans. The architecture follows a life cycle that starting from a common specification of the main high-level steps of a treatment for a given comorbid patient, results in a detailed treatment plan without harmful interactions among the single-disease personalized treatments.

Automatic generation of control sequences for manufacturing systems based on partial order planning techniques

This work presents an approach for the application of artificial intelligence planning techniques to the automatic generation of control sequences for manufacturing systems. These systems have some special features that must be considered in the planning process, but there are difficulties when the usual models of action are used to deal with these features. In this work, a specialized interval-based model of action is defined by extending the classic model ofstrips giving it more expressiveness so that it is able to deal with these features. In consequence, a specialized planning algorithm for this model of action, called machine, is defined based on a general partial order planning scheme, and it is able to obtain control sequences for manufacturing systems. These control sequences are actually the control program skeleton and may be easily translated into real control programs expressed as GRAFCET charts. q 2000 Elsevier Science Ltd. All rights reserved.

Towards the Use of XPDL as Planning and Scheduling Modeling Tool: The Workflow Patterns Approach

This paper presents a transformation from a business process model diagram stored in XPDL format, into a hierarchical extension of the PDDL planning language, using the concept of workflow patterns as base of the translation process. The proposed architecture is evaluated within a specific teamwork project management scenario: the allocation of human resources and web services for the cooperative development of on-line courses in an e-learning center.

Task network based modeling, dynamic generation and adaptive execution of patient-tailored treatment plans based on smart process management technologies

In this paper we present a knowledge-based, Clinical Decision Support System (OncoTheraper2.0) that provides support to the full life-cycle of both clinical decisions and clinical processes execution in the field of pediatric oncology treatments. The system builds on a previous proof of concept devoted to demonstrate that Hierarchical Planning and Scheduling is an enabling technology to support clinical decisions. The present work describes new issues about the engineering process carried out in the development and deployment of the system in a hospital environment (supported by a knowledge engineering suite named IActive Knowledge Studio, devoted to the development of intelligent systems based on Smart Process Management technologies). New techniques that support the execution and monitoring of patient-tailored treatment plans, as well as, the adaptive response to exceptions during execution are described.

Mixing expressiveness and efficiency in a manufacturing planner

The application of arti® cial intelligence planning techniques to real world problems is a very dic cult task which usually implies overcoming expressiveness and ec ciency handicaps. Expressiveness in order to cope with the great amount of knowledge involved in these problems and also to obtain results directly, or almost directly, applicable in the ® eld of study. Ec ciency to achieve a good course through the potentially enormous search spaces and, therefore, a satisfactory response capability. This work describes Ma c h i n e , a manufacturing planning architecture which is able to obtain sequential control programs for manufacturing systems, the steps given to overcome these expressiveness and ec ciency handicaps and how it is able to solve some real size problems.

ATHENA: Smart Process Management for Daily Activity Planning for Cognitive Impairment

Smart Process Management is a technology, based on Artificial Intelligence planning and scheduling, able to design timed sequences of activities that solve a problem in a given environment. In the framework of Ambient Assisted Living, it is being used for decision support for the daily care of patients with cognitive impairment, either for the patient themselves or for their care givers or senior care center staff by designing personalized daily activity plans for the specific case of every patient or the resources available at senior care centers.

OncoTheraper: Clinical Decision Support for Oncology Therapy Planning Based on Temporal Hierarchical Tasks Networks

This paper presents the underlying technology of OncoTheraper, a Clinical Decision Support System for oncology therapy planning. The paper is focused on the representation of oncology treatment protocols by a temporally extended, Hierarchical Task Networks (HTN) based knowledge representation as well as their interpretation by a temporal HTN planning process. The planning process allows to obtain temporally annotated therapy plans that support decisions of oncologists in the area of paediatrics oncology. A proof on concept aimed to validate this technology is also described. In addition, a service oriented architecture that supports the decision services of the system is proposed.