Background Life expectancy is one of the most important factors in end-of-life decision making. Good prognostication for example helps to determine the course of treatment and helps to anticipate the procurement of health care services and facilities, or more broadly: facilitates Advance Care Planning. Advance Care Planning improves the quality of the final phase of life by stimulating doctors to explore the preferences for end-of-life care with their patients, and people close to the patients. Physicians, however, tend to overestimate life expectancy, and miss the window of opportunity to initiate Advance Care Planning. This research tests the potential of using machine learning and natural language processing techniques for predicting life expectancy from electronic medical records. Methods We approached the task of predicting life expectancy as a supervised machine learning task. We trained and tested a long short-term memory recurrent neural network on the medical records of deceased patients. We developed the model with a ten-fold cross-validation procedure, and evaluated its performance on a held-out set of test data. We compared the performance of a model which does not use text features (baseline model) to the performance of a model which uses features extracted from the free texts of the medical records (keyword model), and to doctors’ performance on a similar task as described in scientific literature. Results Both doctors and the baseline model were correct in 20% of the cases, taking a margin of 33% around the actual life expectancy as the target. The keyword model, in comparison, attained an accuracy of 29% with its prognoses. While doctors overestimated life expectancy in 63% of the incorrect prognoses, which harms anticipation to appropriate end-of-life care, the keyword model overestimated life expectancy in only 31% of the incorrect prognoses. Conclusions Prognostication of life expectancy is difficult for humans. Our research shows that machine learning and natural language processing techniques offer a feasible and promising approach to predicting life expectancy. The research has potential for real-life applications, such as supporting timely recognition of the right moment to start Advance Care Planning.
Software systems convert between graphemes and phonemes using lexicon-based, rule-based or data-driven techniques. SHOTGUN combines these techniques in a hybrid system which converts between graphemes and phonemes bi-directionally, adds linguistic and educational information about the relationships between graphemes and phonemes and provides estimates about the likelihood that the generated output is correct. We describe the components from which SHOTGUN is built and determine its accuracy by running tests on two data sources, the BasisSpellingBank and CELEX, comparing the results to Nunn’s (1998) rule-based conversion system. SHOTGUN converts phonemes to graphemes and vice versa with precision of 81% and 86% when tested on the BasisSpellingBank, and 80% and 81% when tested on CELEX. SHOTGUN proves to be a powerful new conversion tool.
Text mining is used to analyze large collections of texts automatically, helping us to aggregate, search, discover, yield, and exploit information in an effective and efficient way. The need for techniques to efficiently process text is growing alongside the increasing availability of large amounts of health data from medical records and social media, for example. This entry gives an overview of text mining from the perspective of health communication, by explaining the goal of text mining, naming much used techniques and resources, providing examples of applications in the health domain, and highlighting some crucial challenges and opportunities.
parameters are returned for follow up providing further opportunities for the ACP team to engage with the practice. Results 87% of general practice teams in Canterbury have supported patients to create an eACPlans.General practice teams create 80% of the Canterbury's eACPlans.Increased multidisciplinary approach to plan creation. Conclusion(s) A cross-system approach to implementation has facilitated the establishment of ACP in general practice.
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