Recognition of medication information from discharge summaries using ensembles of classifiers

Doan, Son; Collier, Nigel; Xu, Hua; Duy, Pham Hoang; Phương, Từ Minh

doi:10.1186/1472-6947-12-36

Cited by 36 publications

(26 citation statements)

References 22 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Processing of healthcare narrative has been a focus of clinical text mining and natural language processing for over 30 years, with notable results in automated harvesting of important clinical concepts and events in many domains (Abbe et al, ; Doan, Collier, Xu, Duy, & Phuong, ; Friedman, Shagina, Lussier, & Hripcsak, ; Savova et al, ; Sohn, Kocher, Chute, & Savova, ; Spasić, Livsey, Keane, & Nenadić, ). The main challenge is that clinical narrative is often written with a distinct style, seldom conforming to standard grammar, frequently with spelling and typing errors as well as common abbreviations and acronyms, with their meaning being often ambiguous depending on the context (Abbe et al, ; Dehghan, Keane, & Nenadic, ; Ford et al, ).…”

Section: Introductionmentioning

confidence: 99%

Automatic mining of symptom severity from psychiatric evaluation notes

Karystianis

Nevado

Kim

et al. 2017

Int J Methods Psych Res

View full text Add to dashboard Cite

ObjectivesAs electronic mental health records become more widely available, several approaches have been suggested to automatically extract information from free‐text narrative aiming to support epidemiological research and clinical decision‐making. In this paper, we explore extraction of explicit mentions of symptom severity from initial psychiatric evaluation records. We use the data provided by the 2016 CEGS N‐GRID NLP shared task Track 2, which contains 541 records manually annotated for symptom severity according to the Research Domain Criteria.MethodsWe designed and implemented 3 automatic methods: a knowledge‐driven approach relying on local lexicalized rules based on common syntactic patterns in text suggesting positive valence symptoms; a machine learning method using a neural network; and a hybrid approach combining the first 2 methods with a neural network.ResultsThe results on an unseen evaluation set of 216 psychiatric evaluation records showed a performance of 80.1% for the rule‐based method, 73.3% for the machine‐learning approach, and 72.0% for the hybrid one.ConclusionsAlthough more work is needed to improve the accuracy, the results are encouraging and indicate that automated text mining methods can be used to classify mental health symptom severity from free text psychiatric notes to support epidemiological and clinical research.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic mining of symptom severity from psychiatric evaluation notes

Karystianis

Nevado

Kim

et al. 2017

Int J Methods Psych Res

View full text Add to dashboard Cite

show abstract

“…Based on [64], [65], we compared performance across different systems using an approximate randomization approach for testing significance. In order to calculate significance for two different systems (system A and system B) on the Phenominer corpus (with sentences), we performed the following steps:…”

Section: Methodsmentioning

confidence: 99%

Learning to Recognize Phenotype Candidates in the Auto-Immune Literature Using SVM Re-Ranking

Collier

Tran

et al. 2013

PLoS ONE

Self Cite

View full text Add to dashboard Cite

The identification of phenotype descriptions in the scientific literature, case reports and patient records is a rewarding task for bio-medical text mining. Any progress will support knowledge discovery and linkage to other resources. However because of their wide variation a number of challenges still remain in terms of their identification and semantic normalisation before they can be fully exploited for research purposes.This paper presents novel techniques for identifying potential complex phenotype mentions by exploiting a hybrid model based on machine learning, rules and dictionary matching. A systematic study is made of how to combine sequence labels from these modules as well as the merits of various ontological resources. We evaluated our approach on a subset of Medline abstracts cited by the Online Mendelian Inheritance of Man database related to auto-immune diseases.Using partial matching the best micro-averaged F-score for phenotypes and five other entity classes was 79.9%. A best performance of 75.3% was achieved for phenotype candidates using all semantics resources. We observed the advantage of using SVM-based learn-to-rank for sequence label combination over maximum entropy and a priority list approach. The results indicate that the identification of simple entity types such as chemicals and genes are robustly supported by single semantic resources, whereas phenotypes require combinations. Altogether we conclude that our approach coped well with the compositional structure of phenotypes in the auto-immune domain.

show abstract

“…Sequence labeling problem means: given a sequence of input tokens A = (a 1 , ..., a n ), and a predefined set of labels L, determine a sequence of labels B = (b 1 , …, b n ) with the largest joint probability for the sequence of input tokens [18]. (b i L for 1 ≤ i ≤ n) Classification problem means for each input ∈ token x, determine the label with the highest probability of classification among the predefined set of labels L. As for CNER, the labels incorporate two concepts: the type of the clinical entity and the position of the token within the entity.…”

Section: Machine Learning Methods For Cnermentioning

confidence: 99%

Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods (Preprint)

Zhang¹,

Wang²,

Hou³

et al. 2018

Preprint

View full text Add to dashboard Cite

Background: Electronic health records (EHR) is an important data resource for clinical studies and applications. Physicians or clinicians describe patients' disorders or treatment procedures using free texts in EHR. The narrative information play an important role in patient treatment and clinical research. However, it is challenging to made machine understand the clinical narratives. Objective: This study aimed to automatically identify Chinese clinical entities from free texts in EHR, and make machine semantically understand diagnosis, test, body part, symptom, treatment and, etc. Methods: Two machine learning (ML) models, conditional random fields (CRF) method and bidirectional LSTM-CRF, were applied to recognize clinical entities from Chinese EHR data. For training the CRF-based model, we selected features as bag of Chinese characters, part-of-speech (POS) tags, character types and the position of characters. For the bidirectional LSTM-CRF-based model, character embeddings and segmentation information were used as features. In addition, we used a dictionarybased approach as the baseline for performance evaluation purpose. Results: To validate our methods, we used the benchmark data set with human annotated Chinese electronic health records, released by CCKS 2017 CNER challenge task. The result showed that our methods were able to automatically identify types of Chinese clinical entities such as diagnosis, test, symptom body part and treatment in one-round running. The identification overall performance of CRF and bidirectional LSTM-CRF achieved Precision of 0.9203 and 0.9112, Recall of 0.8709 and 0.8974, F1 score of 0.8949 and 0.9043 respectively. The result also indicated that our methods performed well on recognizing each type of clinical entities, in which the "symptom" type achieved the best with F1 score over 0.96. Conclusions: In this study, we developed two computational methods to simultaneously identify types of Chinese clinical entities from free texts in EHRs. Via training, it can effectively identify various types of clinical entities (e.g., symptom and treatment) with high accuracy. This study contributed to translating humanreadable health information into machine-readable one.

show abstract

Recognition of medication information from discharge summaries using ensembles of classifiers

Cited by 36 publications

References 22 publications

Automatic mining of symptom severity from psychiatric evaluation notes

Automatic mining of symptom severity from psychiatric evaluation notes

Learning to Recognize Phenotype Candidates in the Auto-Immune Literature Using SVM Re-Ranking

Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods (Preprint)

Contact Info

Product

Resources

About