Rehabilitation therapy aims to enable disabled patients to maintain optimal functioning upon achieving return-to-work (RTW) approval. The Berg Balance Scale (BBS) is a benchmark for activity accomplishment featured in the International Classification of Functioning, Disability and Health Framework (ICF). The ICF is used by physiotherapist (PT) and occupational therapist (OT) to determine the functional mobility of disabled patient. Conventionally, practitioners measure, record, and analyze assessment results manually which resulting in difficulty in predicting patient progression. The large data volume involved in the RTW process requires synthesize and reasoning for decision making purposes. Thus, there is a need to identify an efficient reasoning technique to facilitate the decision making process. This study highlights the use of Horn Clause in decision tree to assist medical doctor in assessing improvement of RTW approved patient based on the BBS assessment. The Horn Clause is one of the fundamental reasoning techniques applied in various domains including healthcare. The next phase of this study is to test the efficiency of the Horn Clause reasoning based on patient’s stroke recovery and spinal cord injury.

Disability of upper limb parts is common for stroke survivors. Early implementation of well-organized upper limb treatment after stroke may result in fast recovery of upper limb functions. There are many treatments and assessments to improve the ability in upper limb movements. However, the specialists in rehabilitation departments use patient information system (PIS) to store and manage all the patient�s information and assessment records. The information and assessment records of the patients usually are obtained from various categories of assessment but it is inconsistent. This causes difficulties in seeking information, and needs to run all the assessments even those not important for the patients. In this paper, an ontology in the development of PIS will be constructed to overcome the problem. The ontology enables semantic knowledge representation for upper limb stroke rehabilitation. This ontology will be designed based on the Enterprise Ontology, TOronto Virtual Enterprise Ontology, METHONTOLOGY and Ontology Development 101. As a result, the proposed ontology will improve the information management in PIS. � 2018 CEUR-WS. All rights reserved.

A decision support system (DSS) is an intelligent system by which a decision support is provided for decision-making purpose. DSS extensively requires a representation of domain knowledge to apply reasoning methods that help in reaching new knowledge for decision support. Due to the benefits of Ontology compared with database system, it considered as knowledge representation in several DSS. DSSs have been extensively addressed in many research like healthcare, therefore it was named Clinical Decision Support System (CDSS). In this paper, a review on reasoning methods that has been utilized on CDSS has been conducted. The review is achieved through comparing and analysing the benefits and drawbacks of CDSS reasoning methods as well as surveying the current reasoning methods used in ontology based CDSS. After analysis, it has been revealed that the integration of the Case-Based Reasoning (CBR) method and Rule-Based Reasoning (RBR) methods facilitates the operation of CDSS to mimic clinical staff members. This integration is recommended since the CDSS will contain the two sources of knowledge which are domain knowledge and knowledge from historical cases. Furthermore, the review shows that ontology has been used in several CDSS but still not been integrated with CBR and RBR. � 2020 SERSC.

Upper limb complications are common following stroke and may be seriously debilitating. There are many treatments and assessments to improve the ability in upper limb movements. Due to the increasing of stroke survivors, specialists in rehabilitation departments usually use Patients Information System (PIS) to store and manage the patient's information and their assessments records. Designing an ontology for PIS is crucial to help the specialists in seeking the patients' information and managing their assessments. In this paper, an Upper Limb Stroke Ontology (ULSOnt) was developed to enable semantic knowledge representation for PIS at rehabilitation department. ULSOnt consists of tangible objects that are listed by the specialists at rehabilitation department of Hospital Universiti Sains Malaysia (HUSM). ULSOnt was designed on the basis of Enterprise Ontology, TOronto Virtual Enterprise Ontology, METHONTOLOGY and Ontology Development 101. The logical consistency and model completeness of ULSOnt ontology were verified by ontology experts. ULSOnt ontology offers more flexibility in accessing the patients' information, which means that it can be utilize for designing Intelligent Rehabilitation (IntelliRehab) systems. � 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.