Using similarity measures for medical event sequences to predict mortality in trauma patients

Merauke is one of seventeen districts selected for strengthening beef cattle breeding activities to provide livestock germs, especially for local livestock, through domestic procurement. The sustainability of domestic beef production supplies must be supported by the availability of quality beef breeds. Superior beef cattle are one of the determining factors and have a strategic value in the effort to develop beef cattle. Criteria for evaluating the quality of cattle breeds, including body size or morphology, health and defects in livestock, are in accordance with Indonesian national standards for good beef cattle breeding guidelines. Selection of cattle breeds is done by the traditional breeders community only by visually paying attention to the size of cattle. This study aims to develop decision support tools for farmers in selecting prospective high-quality beef cattle, to analyze the criteria data for cattle using a combination of two methods; AHP and SMART, that alternative solutions can be obtained that will be recommended to farmers. The local beef cattle of Merauke that are used as research samples are PO cattle, system provides information on bull superior which farmers can use as supporting information in making decisions.

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“…The analysis using the AHP method must be repeated. Calculation of ratio consistency uses the following equation [19].…”

Section: 𝐶𝐼 = (4)mentioning

confidence: 99%

Aplication of Hybrid Method for Superior cattle selection using Decision Support System

2021

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“…In this work, we develop a Decision Support System (DSS) to support the implementation of strategies to reduce no-show rates in this program. It has been argued that in complex problems with multiple variables and fragmented data, information systems have potential to improve resource allocation practices [16] and increase system performance [17]. Hence, the DSS will use routinely collected data and Machine Learning (ML) methods to classify patients in three risk categories (low, medium and high) in terms of their individual no-show probability.…”

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confidence: 99%

Improving healthcare access management by predicting patient no-show behaviour

Barrera

Brailsford

Bravo

et al. 2020

Decision Support Systems

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Low attendance levels in medical appointments have been associated with poor health outcomes and efficiency problems for service providers. To address this problem, healthcare managers could aim at improving attendance levels or minimizing the operational impact of no-shows by adapting resource allocation policies. However, given the uncertainty of patient behaviour, generating relevant information regarding no-show probabilities could support the decision-making process for both approaches. In this context many researchers have used multiple regression models to identify patient and appointment characteristics than can be used as good predictors for no-show probabilities. This work develops a Decision Support System (DSS) to support the implementation of strategies to encourage attendance, for a preventive care program targeted at underserved communities in Bogotá, Colombia. Our contribution to literature is threefold. Firstly, we assess the effectiveness of different machine learning approaches to improve the accuracy of regression models. In particular, Random Forest and Neural Networks are used to model the problem accounting for non-linearity and variable interactions. Secondly, we propose a novel use of Layer-wise Relevance Propagation in order to improve the explainability of neural network predictions and obtain insights from the modelling step. Thirdly, we identify variables explaining no-show probabilities in a developing context and study its policy implications and potential for improving healthcare access. In addition to quantifying relationships reported in previous studies, we find that income and neighbourhood crime statistics affect no-show probabilities. Our results will support patient prioritization in a pilot behavioural intervention and will inform appointment planning decisions.

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A deep recurrent neural network approach to learn sequence similarities for user-identification

Vamosi

Reutterer

Platzer

2022

Decision Support Systems

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Using similarity measures for medical event sequences to predict mortality in trauma patients

Cited by 6 publications

References 23 publications

Aplication of Hybrid Method for Superior cattle selection using Decision Support System

Aplication of Hybrid Method for Superior cattle selection using Decision Support System

Improving healthcare access management by predicting patient no-show behaviour

A deep recurrent neural network approach to learn sequence similarities for user-identification

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