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Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients. A Hybrid Method of Decision Tree and Neural Network.

Methods of information in medicine
BACKGROUND: Generally, traumatic brain injury (TBI) patients do not have a stable condition, particularly after the first week of TBI. Hence, indicating the attributes in prognosis through a prediction model is of utmost importance since it helps car...

Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining.

Assessment
Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural la...

Ensembles of randomized trees using diverse distributed representations of clinical events.

BMC medical informatics and decision making
BACKGROUND: Learning deep representations of clinical events based on their distributions in electronic health records has been shown to allow for subsequent training of higher-performing predictive models compared to the use of shallow, count-based ...

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

Health informatics journal
This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set...

Machine learning for medical images analysis.

Medical image analysis
This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorit...

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

Physics in medicine and biology
Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on...

A study of the effectiveness of machine learning methods for classification of clinical interview fragments into a large number of categories.

Journal of biomedical informatics
This study examines the effectiveness of state-of-the-art supervised machine learning methods in conjunction with different feature types for the task of automatic annotation of fragments of clinical text based on codebooks with a large number of cat...

Clustering Single-Cell Expression Data Using Random Forest Graphs.

IEEE journal of biomedical and health informatics
Complex tissues such as brain and bone marrow are made up of multiple cell types. As the study of biological tissue structure progresses, the role of cell-type-specific research becomes increasingly important. Novel sequencing technology such as sing...

Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy.

International journal of computer assisted radiology and surgery
PURPOSE: Computer assistance is increasingly common in surgery. However, the amount of information is bound to overload processing abilities of surgeons. We propose methods to recognize the current phase of a surgery for context-aware information fil...

Improving the utility of MeSH® terms using the TopicalMeSH representation.

Journal of biomedical informatics
OBJECTIVE: To evaluate whether vector representations encoding latent topic proportions that capture similarities to MeSH terms can improve performance on biomedical document retrieval and classification tasks, compared to using MeSH terms.