Background Due to limited numbers of palliative care specialists and/or resources, accessing palliative care remains limited in many low and middle-income countries. Data science methods, such as rule-based algorithms...
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Background Due to limited numbers of palliative care specialists and/or resources, accessing palliative care remains limited in many low and middle-income countries. Data science methods, such as rule-based algorithms and textmining, have potential to improve palliative care by facilitating analysis of electronic healthcare records. This study aimed to develop and evaluate a rule-based algorithm for identifying cancer patients who may benefit from palliative care based on the Thai version of the Supportive and Palliative Care Indicators for a Low-Income Setting (SPICT-LIS) *** The medical records of 14,363 cancer patients aged 18 years and older, diagnosed between 2016 and 2020 at Songklanagarind Hospital, were analyzed. Two rule-based algorithms, strict and relaxed, were designed to identify key SPICT-LIS indicators in the electronic medical records using tokenization and sentiment analysis. The inter-rater reliability between these two algorithms and palliative care physicians was assessed using percentage agreement and Cohen's kappa coefficient. Additionally, factors associated with patients might be given palliative care as they will benefit from it were *** The strict rule-based algorithm demonstrated a high degree of accuracy, with 95% agreement and Cohen's kappa coefficient of 0.83. In contrast, the relaxed rule-based algorithm demonstrated a lower agreement (71% agreement and Cohen's kappa of 0.16). Advanced-stage cancer with symptoms such as pain, dyspnea, edema, delirium, xerostomia, and anorexia were identified as significant predictors of potentially benefiting from palliative *** The integration of rule-based algorithms with electronic medical records offers a promising method for enhancing the timely and accurate identification of patients with cancer might benefit from palliative care.
Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining techniques have been adapted to reduce noisy information from extracted features but still contains s...
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ISBN:
(纸本)9781479941438
Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining techniques have been adapted to reduce noisy information from extracted features but still contains some noises features. However, the noise features are extracted from the same training documents that good features extracted from. Therefore, the main problem is that some training documents contain large a mount of noises data. If we can reduce the noises data in the training documents that would help to reduce noises in extracted features. Moreover, we believe that remove some of training documents (documents that contains noises data more than useful data) can help to improve the effectiveness of the classifier. Using the advantages of clustering method can help to reduce the affect of noises data. The main problem of clustering is defined to be that of finding groups of similar projects in the data. In this paper we introduce the methodology that using clustering algorithm to group training data before use it. Also we tested our theory that not all training documents are useful to train the classifier.
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