본문 바로가기
HOME> 저널/프로시딩 > 저널/프로시딩 검색상세

저널/프로시딩 상세정보

권호별목차 / 소장처보기

H : 소장처정보

T : 목차정보

Journal of biomedical informatics 20건

  1. [해외논문]   Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2   SCI SCIE

    Filannino, Michele (University at Albany, State University of New York, Albany, NY, USA ) , Stubbs, Amber (Simmons College, Boston, MA, USA ) , Uzuner, Ö (University at Albany, State University of New York, Albany, NY, USA) , zlem
    Journal of biomedical informatics v.75 suppl. ,pp. S62 - S70 , 2017 , 1532-0464 ,

    초록

    Abstract The second track of the CEGS N-GRID 2016 natural language processing shared tasks focused on predicting symptom severity from neuropsychiatric clinical records. For the first time, initial psychiatric evaluation records have been collected, de-identified, annotated and shared with the scientific community. One-hundred-ten researchers organized in twenty-four teams participated in this track and submitted sixty-five system runs for evaluation. The top ten teams each achieved an inverse normalized macro-averaged mean absolute error score over 0.80. The top performing system employed an ensemble of six different machine learning-based classifiers to achieve a score 0.86. The task resulted to be generally easy with the exception of two specific classes of records: records with very few but crucial positive valence signals, and records describing patients predominantly affected by negative rather than positive valence. Those cases proved to be very challenging for most of the systems. Further research is required to consider the task solved. Overall, the results of this track demonstrate the effectiveness of data-driven approaches to the task of symptom severity classification. Highlights Results from 110 researchers in 24 teams and 65 submissions. The best system performs comparably to the least experienced annotator. Positive domain symptom severity classification can be tackled automatically. Systems fail when patients show both signs of negative and positive valence. Graphical abstract [DISPLAY OMISSION]

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  2. [해외논문]   Cover 2: Editorial Board   SCI SCIE


    Journal of biomedical informatics v.75 suppl. ,pp. IFC - IFC , 2017 , 1532-0464 ,

    초록

    Abstract The second track of the CEGS N-GRID 2016 natural language processing shared tasks focused on predicting symptom severity from neuropsychiatric clinical records. For the first time, initial psychiatric evaluation records have been collected, de-identified, annotated and shared with the scientific community. One-hundred-ten researchers organized in twenty-four teams participated in this track and submitted sixty-five system runs for evaluation. The top ten teams each achieved an inverse normalized macro-averaged mean absolute error score over 0.80. The top performing system employed an ensemble of six different machine learning-based classifiers to achieve a score 0.86. The task resulted to be generally easy with the exception of two specific classes of records: records with very few but crucial positive valence signals, and records describing patients predominantly affected by negative rather than positive valence. Those cases proved to be very challenging for most of the systems. Further research is required to consider the task solved. Overall, the results of this track demonstrate the effectiveness of data-driven approaches to the task of symptom severity classification. Highlights Results from 110 researchers in 24 teams and 65 submissions. The best system performs comparably to the least experienced annotator. Positive domain symptom severity classification can be tackled automatically. Systems fail when patients show both signs of negative and positive valence. Graphical abstract [DISPLAY OMISSION]

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  3. [해외논문]   Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients   SCI SCIE

    Dai, Hong-Jie (Department of Computer Science and Information Engineering, National Taitung University, Taitung, Taiwan ) , Su, Emily Chia-Yu (Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan ) , Uddin, Mohy (King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Publication Office, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia ) , Jonnagaddala, Jitendra (School of Public Health and Community Medicine, UNSW Sydney, Australia ) , Wu, Chi-Shin (Department of Psychiatry, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan ) , Syed-Abdul, Shabbir (Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan)
    Journal of biomedical informatics v.75 suppl. ,pp. S149 - S159 , 2017 , 1532-0464 ,

    초록

    Abstract Evidence has revealed interesting associations of clinical and social parameters with violent behaviors of patients with psychiatric disorders. Men are more violent preceding and during hospitalization, whereas women are more violent than men throughout the 3days following a hospital admission. It has also been proven that mental disorders may be a consistent risk factor for the occurrence of violence. In order to better understand violent behaviors of patients with psychiatric disorders, it is important to investigate both the clinical symptoms and psychosocial factors that accompany violence in these patients. In this study, we utilized a dataset released by the Partners Healthcare and Neuropsychiatric Genome-scale and RDoC Individualized Domains project of Harvard Medical School to develop a unique text mining pipeline that processes unstructured clinical data in order to recognize clinical and social parameters such asage, gender, history of alcohol use, and violent behaviors, and explored the associations between these parameters and violent behaviors of patients with psychiatric disorders. The aim of our work was to demonstrate the feasibility of mining factors that are strongly associated with violent behaviors among psychiatric patients from unstructured psychiatric evaluation records using clinical text mining. Experiment results showed that stimulants, followed by a family history of violent behavior, suicidal behaviors, and financial stress were strongly associated with violent behaviors. Key aspects explicated in this paper include employing our text mining pipeline to extract clinical and social factors linked with violent behaviors, generating association rules to uncover possible associations between these factors and violent behaviors, and lastly the ranking of top rules associated with violent behaviors using statistical analysis and interpretation. Highlights Text mining can be used to explore parameters associated with violent behaviors in unstructured clinical notes. Mental disorders are a significant risk factor for the violent behavior among the patients. Stimulants and suicidal tendency were also strongly associated with the patients’ violent behavior. Graphical abstract [DISPLAY OMISSION]

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  4. [해외논문]   De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1   SCI SCIE

    Stubbs, Amber (Simmons College, School of Library and Information Science, 300 The Fenway, Boston, MA 02115, United States ) , Filannino, Michele (University at Albany, United States ) , Uzuner, Ö (University at Albany, United States) , zlem
    Journal of biomedical informatics v.75 suppl. ,pp. S4 - S18 , 2017 , 1532-0464 ,

    초록

    Abstract The 2016 CEGS N-GRID shared tasks for clinical records contained three tracks. Track 1 focused on de-identification of a new corpus of 1000 psychiatric intake records. This track tackled de-identification in two sub-tracks: Track 1.A was a “sight unseen” task, where nine teams ran existing de-identification systems, without any modifications or training, on 600 new records in order to gauge how well systems generalize to new data. The best-performing system for this track scored an F1 of 0.799. Track 1.B was a traditional Natural Language Processing (NLP) shared task on de-identification, where 15 teams had two months to train their systems on the new data, then test it on an unannotated test set. The best-performing system from this track scored an F1 of 0.914. The scores for Track 1.A show that unmodified existing systems do not generalize well to new data without the benefit of training data. The scores for Track 1.B are slightly lower than the 2014 de-identification shared task (which was almost identical to 2016 Track 1.B), indicating that these new psychiatric records pose a more difficult challenge to NLP systems. Overall, de-identification is still not a solved problem, though it is important to the future of clinical NLP. Highlights NLP shared task with new set of 1000 de-identified psychiatric records. “Sight-unseen” task: top F1 of 0.799 using out-of-the-box system on new data. “Standard task: top F1 of 0.914 on test data after 2months of development. Hybrid systems most effective, but often missed PHI requiring world knowledge or context. Graphical abstract [DISPLAY OMISSION]

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  5. [해외논문]   Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records   SCI SCIE

    Posada, Jose D. (Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd., Pittsburgh, PA 15206, United States ) , Barda, Amie J. (Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd., Pittsburgh, PA 15206, United States ) , Shi, Lingyun (Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd., Pittsburgh, PA 15206, United States ) , Xue, Diyang (Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd., Pittsburgh, PA 15206, United States ) , Ruiz, Victor (Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd., Pittsburgh, PA 15206, United States ) , Kuan, Pei-Han (Institute of Manufacturing Information and System, National Cheng-Kung University, Tainan, Taiwan ) , Ryan, Neal D. (Department of Psychiatry, University of Pittsburgh, 3811 O'Hara St., Pittsburgh, PA 15213, United States ) , Tsui, Fuchiang (Rich) (Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd., Pittsburgh, PA 15206, United States)
    Journal of biomedical informatics v.75 suppl. ,pp. S94 - S104 , 2017 , 1532-0464 ,

    초록

    Abstract In response to the challenges set forth by the CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing , we describe a framework to automatically classify initial psychiatric evaluation records to one of four positive valence system severities: absent, mild, moderate, or severe. We used a dataset provided by the event organizers to develop a framework comprised of natural language processing (NLP) modules and 3 predictive models (two decision tree models and one Bayesian network model) used in the competition. We also developed two additional predictive models for comparison purpose. To evaluate our framework, we employed a blind test dataset provided by the 2016 CEGS N-GRID. The predictive scores, measured by the macro averaged-inverse normalized mean absolute error score, from the two decision trees and NaIve Bayes models were 82.56%, 82.18%, and 80.56%, respectively. The proposed framework in this paper can potentially be applied to other predictive tasks for processing initial psychiatric evaluation records, such as predicting 30-day psychiatric readmissions. Highlights Proposed a method to automatically classify symptom severity in psychiatric reports. Question-answers from reports are the most important source of information. Best predictive models automatically selected features prevalent in literature. Graphical abstract [DISPLAY OMISSION]

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  6. [해외논문]   Automatic classification of RDoC positive valence severity with a neural network   SCI SCIE

    Clark, Cheryl (Corresponding author at: The MITRE Corporation, 202 Burlington Rd., Bedford, MA 01730, USA.) , Wellner, Ben , Davis, Rachel , Aberdeen, John , Hirschman, Lynette
    Journal of biomedical informatics v.75 suppl. ,pp. S120 - S128 , 2017 , 1532-0464 ,

    초록

    Abstract Objective Our objective was to develop a machine learning-based system to determine the severity of Positive Valance symptoms for a patient, based on information included in their initial psychiatric evaluation. Severity was rated on an ordinal scale of 0–3 as follows: 0 ( absent =no sy no symptoms), 1 ( mild =mod modest significance), 2 ( moderate =require requires treatment), 3 ( severe =cause causes substantial impairment) by experts. Materials and methods We treated the task of assigning Positive Valence severity as a text classification problem. During development, we experimented with regularized multinomial logistic regression classifiers, gradient boosted trees, and feedforward, fully-connected neural networks. We found both regularization and feature selection via mutual information to be very important in preventing models from overfitting the data. Our best configuration was a neural network with three fully connected hidden layers with rectified linear unit activations. Results Our best performing system achieved a score of 77.86%. The evaluation metric is an inverse normalization of the Mean Absolute Error presented as a percentage number between 0 and 100, where 100 means the highest performance. Error analysis showed that 90% of the system errors involved neighboring severity categories. Conclusion Machine learning text classification techniques with feature selection can be trained to recognize broad differences in Positive Valence symptom severity with a modest amount of training data (in this case 600 documents, 167 of which were unannotated). An increase in the amount of annotated data can increase accuracy of symptom severity classification by several percentage points. Additional features and/or a larger training corpus may further improve accuracy. Highlights We trained a machine learning-based system to determine psychiatric symptom severity. Regularization and feature selection via mutual information reduced overfitting. Increasing the amount of annotated data increased accuracy by several percent. Graphical abstract [DISPLAY OMISSION]

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  7. [해외논문]   Automatic recognition of symptom severity from psychiatric evaluation records   SCI SCIE

    Goodwin, Travis R. (Corresponding author.) , Maldonado, Ramon , Harabagiu, Sanda M.
    Journal of biomedical informatics v.75 suppl. ,pp. S71 - S84 , 2017 , 1532-0464 ,

    초록

    Abstract This paper presents a novel method for automatically recognizing symptom severity by using natural language processing of psychiatric evaluation records to extract features that are processed by machine learning techniques to assign a severity score to each record evaluated in the 2016 RDoC for Psychiatry Challenge from CEGS/N-GRID. The natural language processing techniques focused on (a) discerning the discourse information expressed in questions and answers; (b) identifying medical concepts that relate to mental disorders; and (c) accounting for the role of negation. The machine learning techniques rely on the assumptions that (1) the severity of a patient’s positive valence symptoms exists on a latent continuous spectrum and (2) all the patient’s answers and narratives documented in the psychological evaluation records are informed by the patient’s latent severity score along this spectrum. These assumptions motivated our two-step machine learning framework for automatically recognizing psychological symptom severity. In the first step, the latent continuous severity score is inferred from each record; in the second step, the severity score is mapped to one of the four discrete severity levels used in the CEGS/N-GRID challenge. We evaluated three methods for inferring the latent severity score associated with each record: (i) pointwise ridge regression; (ii) pairwise comparison-based classification; and (iii) a hybrid approach combining pointwise regression and the pairwise classifier. The second step was implemented using a tree of cascading support vector machine (SVM) classifiers. While the official evaluation results indicate that all three methods are promising, the hybrid approach not only outperformed the pairwise and pointwise methods, but also produced the second highest performance of all submissions to the CEGS/N-GRID challenge with a normalized MAE score of 84.093 % (where higher numbers indicate better performance). These evaluation results enabled us to observe that, for this task, considering pairwise information can produce more accurate severity scores than pointwise regression – an approach widely used in other systems for assigning severity scores. Moreover, our analysis indicates that using a cascading SVM tree outperforms traditional SVM classification methods for the purpose of determining discrete severity levels. Highlights A two-step framework for recognizing psychological symptom severity is presented. Semantic, discourse, and medical information is modelled. A latent continuous severity score is inferred using machine learning. Pairwise and pointwise machine learning approaches are evaluated. Experiments demonstrate the promise of combining pairwise and pointwise information. Graphical abstract [DISPLAY OMISSION]

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  8. [해외논문]   Ordinal convolutional neural networks for predicting RDoC positive valence psychiatric symptom severity scores   SCI SCIE

    Rios, Anthony (Department of Computer Science, University of Kentucky, 329 Rose Street, Lexington, KY 40506, USA ) , Kavuluru, Ramakanth (Department of Computer Science, University of Kentucky, 329 Rose Street, Lexington, KY 40506, USA)
    Journal of biomedical informatics v.75 suppl. ,pp. S85 - S93 , 2017 , 1532-0464 ,

    초록

    Abstract Background The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task. Objective Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are on the ordinal scale. Specifically, we present our entries (methods and results) in the N-GRID shared task in predicting research domain criteria (RDoC) positive valence ordinal symptom severity scores ( absent , mild , moderate , and severe ) from psychiatric notes. Methods We propose a novel convolutional neural network (CNN) model designed to handle ordinal regression tasks on psychiatric notes. Broadly speaking, our model combines an ordinal loss function, a CNN, and conventional feature engineering (wide features) into a single model which is learned end-to-end. Given interpretability is an important concern with nonlinear models, we apply a recent approach called locally interpretable model-agnostic explanation (LIME) to identify important words that lead to instance specific predictions. Results Our best model entered into the shared task placed third among 24 teams and scored a macro mean absolute error (MMAE) based normalized score ( 100 · ( 1 - MMAE ) ) of 83.86. Since the competition, we improved our score (using basic ensembling) to 85.55, comparable with the winning shared task entry. Applying LIME to model predictions, we demonstrate the feasibility of instance specific prediction interpretation by identifying words that led to a particular decision. Conclusion In this paper, we present a method that successfully uses wide features and an ordinal loss function applied to convolutional neural networks for ordinal text classification specifically in predicting psychiatric symptom severity scores. Our approach leads to excellent performance on the N-GRID shared task and is also amenable to interpretability using existing model-agnostic approaches. Highlights RDoC positive valence symptom severity scores are predicted from psychiatric notes. Ordinal convolutional neural networks outperform other supervised models in this task. Ensemble approaches achieve a normalized macro mean absolute score of 85.55. Our performance is within 1% of best score reported on the CEGS NGRID 2016 dataset. Deep nets with ordinal loss and wide features are suitable for ordinal text classification. Graphical abstract [DISPLAY OMISSION]

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  9. [해외논문]   fmi-ii: Table of Contents   SCI SCIE


    Journal of biomedical informatics v.75 suppl. ,pp. i - ii , 2017 , 1532-0464 ,

    초록

    Abstract Background The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task. Objective Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are on the ordinal scale. Specifically, we present our entries (methods and results) in the N-GRID shared task in predicting research domain criteria (RDoC) positive valence ordinal symptom severity scores ( absent , mild , moderate , and severe ) from psychiatric notes. Methods We propose a novel convolutional neural network (CNN) model designed to handle ordinal regression tasks on psychiatric notes. Broadly speaking, our model combines an ordinal loss function, a CNN, and conventional feature engineering (wide features) into a single model which is learned end-to-end. Given interpretability is an important concern with nonlinear models, we apply a recent approach called locally interpretable model-agnostic explanation (LIME) to identify important words that lead to instance specific predictions. Results Our best model entered into the shared task placed third among 24 teams and scored a macro mean absolute error (MMAE) based normalized score ( 100 · ( 1 - MMAE ) ) of 83.86. Since the competition, we improved our score (using basic ensembling) to 85.55, comparable with the winning shared task entry. Applying LIME to model predictions, we demonstrate the feasibility of instance specific prediction interpretation by identifying words that led to a particular decision. Conclusion In this paper, we present a method that successfully uses wide features and an ordinal loss function applied to convolutional neural networks for ordinal text classification specifically in predicting psychiatric symptom severity scores. Our approach leads to excellent performance on the N-GRID shared task and is also amenable to interpretability using existing model-agnostic approaches. Highlights RDoC positive valence symptom severity scores are predicted from psychiatric notes. Ordinal convolutional neural networks outperform other supervised models in this task. Ensemble approaches achieve a normalized macro mean absolute score of 85.55. Our performance is within 1% of best score reported on the CEGS NGRID 2016 dataset. Deep nets with ordinal loss and wide features are suitable for ordinal text classification. Graphical abstract [DISPLAY OMISSION]

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  10. [해외논문]   Symptom severity classification with gradient tree boosting   SCI SCIE

    Liu, Yang (Med Data Quest, Inc., 505 Coast Blvd S Ste 300, La Jolla, CA 92037, United States ) , Gu, Yu (Department of Electrical and Computer Engineering, UCSD, 9500 Gilman Drive, La Jolla, CA 92093, United States ) , Nguyen, John Chu (Med Data Quest, Inc., 505 Coast Blvd S Ste 300, La Jolla, CA 92037, United States ) , Li, Haodan (Med Data Quest, Inc., 505 Coast Blvd S Ste 300, La Jolla, CA 92037, United States ) , Zhang, Jiawei (Med Data Quest, Inc., 505 Coast Blvd S Ste 300, La Jolla, CA 92037, United States ) , Gao, Yuan (Med Data Quest, Inc., 505 Coast Blvd S Ste 300, La Jolla, CA 92037, United States ) , Huang, Yang (Med Data Quest, Inc., 505 Coast Blvd S Ste 300, La Jolla, CA 92037, United States)
    Journal of biomedical informatics v.75 suppl. ,pp. S105 - S111 , 2017 , 1532-0464 ,

    초록

    Abstract In this paper, we present our system as submitted in the CEGS N-GRID 2016 task 2 RDoC classification competition. The task was to determine symptom severity (0–3) in a domain for a patient based on the text provided in his/her initial psychiatric evaluation. We first preprocessed the psychiatry notes into a semi-structured questionnaire and transformed the short answers into either numerical, binary, or categorical features. We further trained weak Support Vector Regressors (SVR) for each verbose answer and combined regressors’ output with other features to feed into the final gradient tree boosting classifier with resampling of individual notes. Our best submission achieved a macro-averaged Mean Absolute Error of 0.439, which translates to a normalized score of 81.75%. Highlights A better representation of unstructured psychiatric notes as question-answer pairs. Training SVRs to handle verbose answers and combining the models with stacking. Predicting symptom severity using gradient tree boosting with resampling. Graphical abstract [DISPLAY OMISSION]

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지

논문관련 이미지