160,586 research outputs found

    Users’ Continuance Participation in the Online Peer-to-peer Healthcare Community: A Text Mining Approach

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    The online peer-to-peer healthcare communities are known as the platform where dispersed groups of patients and their families query information, seek and offer support, and connect with others. The success of such communities relies on users’ ongoing involvement to generate benefits for both individuals and the communities. This study attempts to understand users’ continuance participation in online peer-to-peer healthcare community by classifying users’ goals of participation based on the user-generated text contents. We proposed a rule-based classification framework to categorize users’ goals of posting contents into four categories: information seeking, experience sharing, information sharing, and social interaction. We formalize and test the relationship between users’ continuance participation and all four posting goals, and find that the first three goals have significant impact on users’ continuance participation. Our findings can help researchers and practitioners better understand users’ behavior in the online peer-to-peer healthcare community

    Clinical text classification in Cancer Real-World Data in Spanish

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    Healthcare systems currently store a large amount of clinical data, mostly unstructured textual information, such as electronic health records (EHRs). Manually extracting valuable information from these documents is costly for healthcare professionals. For example, when a patient first arrives at an oncology clinical analysis unit, clinical staff must extract information about the type of neoplasm in order to assign the appropriate clinical specialist. Automating this task is equivalent to text classification in natural language processing (NLP). In this study, we have attempted to extract the neoplasm type by processing Spanish clinical documents. A private corpus of 23, 704 real clinical cases has been processed to extract the three most common types of neoplasms in the Spanish territory: breast, lung and colorectal neoplasms. We have developed methodologies based on state-of-the-art text classification task, strategies based on machine learning and bag-of-words, based on embedding models in a supervised task, and based on bidirectional recurrent neural networks with convolutional layers (C-BiRNN). The results obtained show that the application of NLP methods is extremely helpful in performing the task of neoplasm type extraction. In particular, the 2-BiGRU model with convolutional layer and pre-trained fastText embedding obtained the best performance, with a macro-average, more representative than the micro-average due to the unbalanced data, of 0.981 for precision, 0.984 for recall and 0.982 for F1-score.The authors acknowledge the support from the Ministerio de Ciencia e InnovaciĂłn (MICINN) under project PID2020-116898RB-I00, from Universidad de MĂĄlaga and Junta de AndalucĂ­a through grants UMA20-FEDERJA-045 and PYC20-046-UMA (all including FEDER funds), and from the Malaga-Pfizer consortium for AI research in Cancer - MAPIC. Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂ­a Tech

    Determining patient outcomes from patient letters: A comparison of text analysis approaches

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    This paper presents a case study comparing text analysis approaches used to classify the current status of a patient to inform scheduling. It aims to help one of the UKs largest healthcare providers systematically capture patient outcome information following a clinic attendance, ensuring records are closed when a patient is discharged and follow-up appointments can be scheduled to occur within the time-scale required for safe, effective care. Analysing patient letters allows systematic extraction of discharge or follow-up information to automatically update a patient record. This clarifies the demand placed on the system, and whether current capacity is a barrier to timely access. Three approaches for systematic information capture are compared: phrase identification (using lexicons), word frequency analysis and supervised text mining. Approaches are evaluated according to their precision and stakeholder acceptability. Methodological lessons are presented to encourage project objectives to be considered alongside text classification methods for decision support tools

    "There are too many, but never enough": qualitative case study investigating routine coding of clinical information in depression.

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    We sought to understand how clinical information relating to the management of depression is routinely coded in different clinical settings and the perspectives of and implications for different stakeholders with a view to understanding how these may be aligned

    Health information systems

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    Healthcare is an information intensive industry in which quality and timely information is a critical resource. There are a wide range of information systems in health that perform different functions but all are involved in the management of data and information. This chapter provides an overview of Health Information Systems and their use in supporting healthcare
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