Enriching queries with user preferences in healthcare

Tegegne, Tesfa; Weide, Th.P. van der

doi:10.1016/j.ipm.2014.03.004

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…The adjusted variables in recall and preci-sion are shown in Table 1. Recall is defined as aquation (8) and precision is defined as equation (9). For experiment, we use several queries that shown in Table 2.…”

Section: Results and Analysismentioning

confidence: 99%

“…Many researches implement preference in the proposed method. User preference is useful in recommender system [7,8], query enrichment [9,10], data ranking [11], and others. Chulyadyo et al [7] introduced approach of constructing a personalized recommendation model from Probabilistic Relational Model with the help of users' preferences over their search criteria.…”

Section: Introductionmentioning

confidence: 99%

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Holle¹,

Arifin²,

Purwitasari³

2015

Jurnal Ilmu Komputer dan Informasi

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In document retrieval, besides the suitability of query with search results, there is also a subjective user assessment that is expected to be a deciding factor in document ranking. This preference aspect is referred at the fiqh document searching. People tend to prefer on certain fiqh methodology without rejecting other fiqh methodologies. It is necessary to investigate preference factor in addition to the relevance factor in the document ranking. Therefore, this research proposed a method of term weighting based on preference to rank documents according to user preference. The proposed method is also combined with term weighting based on documents index and books index so it sees relevance and preference aspect. The proposed method is Inverse Preference Frequency with Î± value (IPFÎ±). In this method, we calculate preference value by IPF term weighting. Then, the preference values of terms that is equal with the query are multiplied by Î±. IPFÎ± combined with the existing weighting methods become TF.IDF.IBF.IPFÎ±. Experiment of the proposed method uses dataset of several Arabic fiqh documents. Evaluation uses recall, precision, and f-measure calculations. Proposed term weighting method is obtained to rank the document in the right order according to user preference. It is shown from the result with recall value reach 75%, precision 100%, and f-measure 85.7% respectively.

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Untitled

Holle¹,

Arifin²,

Purwitasari³

2015

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

show abstract

“…Another study enhanced a healthcare information system to provide high-quality information at any time and any place [139]. The second area, that is, tele-expertise between professionals, focused on exchanging experiences between physicians or professionals to share knowledge and formulate proper decisions for diagnosis and treatment [15,[140][141][142][143].…”

Section: Server Based (Tier 3)mentioning

confidence: 99%

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

et al. 2019

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Remotely monitoring a patient's condition is a serious issue and must be addressed. Remote health monitoring systems (RHMS) in telemedicine refers to resources, strategies, methods and installations that enable doctors or other medical professionals to work remotely to consult, diagnose and treat patients. The goal of RHMS is to provide timely medical services at remote areas through telecommunication technologies. Through major advancements in technology, particularly in wireless networking, cloud computing and data storage, RHMS is becoming a feasible aspect of modern medicine. RHMS for the prioritisation of patients with multiple chronic diseases (MCDs) plays an important role in sustainably providing high-quality healthcare services. Further investigations are required to highlight the limitations of the prioritisation of patients with MCDs over a telemedicine environment. This study introduces a comprehensive and inclusive review on the prioritisation of patients with MCDs in telemedicine applications. Furthermore, it presents the challenges and open issues regarding patient prioritisation in telemedicine. The findings of this study are as follows: (1) The limitations and problems of existing patients' prioritisation with MCDs are presented and emphasised. (2) Based on the analysis of the academic literature, an accurate solution for remote prioritisation in a large scale of patients with MCDs was not presented. (3) There is an essential need to produce a new multiple-criteria decisionmaking theory to address the current problems in the prioritisation of patients with MCDs.

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“…The amalgamation of different technologies such as edge computing, IoT, cloud computing, and machine learning has contributed to a rapid proliferation of technological development in many areas such as healthcare and banking [1,2,3]. The increase of cheap pervasive sensing devices has contributed to the rapid growth of IoT, becoming one of the main sources of big data [4].…”

Section: Introductionmentioning

confidence: 99%

Privacy preserving distributed machine learning with federated learning

Chamikara

Bertók

Khalil

et al. 2021

Computer Communications

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Edge computing and distributed machine learning have advanced to a level that can revolutionize a particular organization. Distributed devices such as the Internet of Things (IoT) often produce a large amount of data, eventually resulting in big data that can be vital in uncovering hidden patterns, and other insights in numerous fields such as healthcare, banking, and policing. Data related to areas such

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Enriching queries with user preferences in healthcare

Cited by 9 publications

References 19 publications

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Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

Privacy preserving distributed machine learning with federated learning

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