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  • 1
    Online Resource
    Online Resource
    Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach
    JMIR Publications Inc. ; 2020
    In:  JMIR Medical Informatics Vol. 8, No. 7 ( 2020-7-27), p. e17652-
    Details
    In: JMIR Medical Informatics, JMIR Publications Inc., Vol. 8, No. 7 ( 2020-7-27), p. e17652-
    Abstract: Temporal information frequently exists in the representation of the disease progress, prescription, medication, surgery progress, or discharge summary in narrative clinical text. The accurate extraction and normalization of temporal expressions can positively boost the analysis and understanding of narrative clinical texts to promote clinical research and practice. Objective The goal of the study was to propose a novel approach for extracting and normalizing temporal expressions from Chinese narrative clinical text. Methods TNorm, a rule-based and pattern learning-based approach, has been developed for automatic temporal expression extraction and normalization from unstructured Chinese clinical text data. TNorm consists of three stages: extraction, classification, and normalization. It applies a set of heuristic rules and automatically generated patterns for temporal expression identification and extraction of clinical texts. Then, it collects the features of extracted temporal expressions for temporal type prediction and classification by using machine learning algorithms. Finally, the features are combined with the rule-based and a pattern learning-based approach to normalize the extracted temporal expressions. Results The evaluation dataset is a set of narrative clinical texts in Chinese containing 1459 discharge summaries of a domestic Grade A Class 3 hospital. The results show that TNorm, combined with temporal expressions extraction and temporal types prediction, achieves a precision of 0.8491, a recall of 0.8328, and a F1 score of 0.8409 in temporal expressions normalization. Conclusions This study illustrates an automatic approach, TNorm, that extracts and normalizes temporal expression from Chinese narrative clinical texts. TNorm was evaluated on the basis of discharge summary data, and results demonstrate its effectiveness on temporal expression normalization.
    Type of Medium: Online Resource
    ISSN: 2291-9694
    URL: Article
    DOI: 10.2196/17652
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2020
    detail.hit.zdb_id: 2798261-0
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  • 2
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    Online Resource
    Development of an Item Bank to Measure Medication Adherence: Systematic Review
    JMIR Publications Inc. ; 2020
    In:  Journal of Medical Internet Research Vol. 22, No. 10 ( 2020-10-8), p. e19089-
    Details
    In: Journal of Medical Internet Research, JMIR Publications Inc., Vol. 22, No. 10 ( 2020-10-8), p. e19089-
    Abstract: Medication adherence is important in managing the progression of chronic diseases. A promising approach to reduce cognitive burden when measuring medication adherence lies in the use of computer‐adaptive tests (CATs) or in the development of shorter patient-reported outcome measures (PROMs). However, the lack of an item bank currently hampers this progress. Objective We aim to develop an item bank to measure general medication adherence. Methods Using the preferred reporting items for systematic review and meta-analysis (PRISMA), articles published before October 2019 were retrieved from PubMed, Embase, CINAHL, the Cochrane Library, and Web of Science. Items from existing PROMs were classified and selected (“binned” and “winnowed”) according to standards published by the Patient-Reported Outcomes Measurement Information System (PROMIS) Cooperative Group. Results A total of 126 unique PROMs were identified from 213 studies in 48 countries. Items from the literature review (47 PROMs with 579 items for which permission has been obtained) underwent binning and winnowing. This resulted in 421 candidate items (77 extent of adherence and 344 reasons for adherence). Conclusions We developed an item bank for measuring general medication adherence using items from validated PROMs. This will allow researchers to create new PROMs from selected items and provide the foundation to develop CATs.
    Type of Medium: Online Resource
    ISSN: 1438-8871
    URL: Article
    DOI: 10.2196/19089
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2020
    detail.hit.zdb_id: 2028830-X
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  • 3
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    Online Resource
    Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study
    JMIR Publications Inc. ; 2022
    In:  JMIR Medical Informatics Vol. 10, No. 9 ( 2022-9-2), p. e38414-
    Details
    In: JMIR Medical Informatics, JMIR Publications Inc., Vol. 10, No. 9 ( 2022-9-2), p. e38414-
    Abstract: Knowledge discovery from treatment data records from Chinese physicians is a dramatic challenge in the application of artificial intelligence (AI) models to the research of traditional Chinese medicine (TCM). Objective This paper aims to construct a TCM knowledge graph (KG) from Chinese physicians and apply it to the decision-making related to diagnosis and treatment in TCM. Methods A new framework leveraging a representation learning method for TCM KG construction and application was designed. A transformer-based Contextualized Knowledge Graph Embedding (CoKE) model was applied to KG representation learning and knowledge distillation. Automatic identification and expansion of multihop relations were integrated with the CoKE model as a pipeline. Based on the framework, a TCM KG containing 59,882 entities (eg, diseases, symptoms, examinations, drugs), 17 relations, and 604,700 triples was constructed. The framework was validated through a link predication task. Results Experiments showed that the framework outperforms a set of baseline models in the link prediction task using the standard metrics mean reciprocal rank (MRR) and Hits@N. The knowledge graph embedding (KGE) multitagged TCM discriminative diagnosis metrics also indicated the improvement of our framework compared with the baseline models. Conclusions Experiments showed that the clinical KG representation learning and application framework is effective for knowledge discovery and decision-making assistance in diagnosis and treatment. Our framework shows superiority of application prospects in tasks such as KG-fused multimodal information diagnosis, KGE-based text classification, and knowledge inference–based medical question answering.
    Type of Medium: Online Resource
    ISSN: 2291-9694
    URL: Article
    DOI: 10.2196/38414
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2022
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  • 4
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    Online Resource
    Measurement Properties of Existing Patient-Reported Outcome Measures on Medication Adherence: Systematic Review
    JMIR Publications Inc. ; 2020
    In:  Journal of Medical Internet Research Vol. 22, No. 10 ( 2020-10-9), p. e19179-
    Details
    In: Journal of Medical Internet Research, JMIR Publications Inc., Vol. 22, No. 10 ( 2020-10-9), p. e19179-
    Abstract: Medication adherence is essential for improving the health outcomes of patients. Various patient-reported outcome measures (PROMs) have been developed to measure medication adherence in patients. However, no study has summarized the psychometric properties of these PROMs to guide selection for use in clinical practice or research. Objective This study aims to evaluate the quality of the PROMs used to measure medication adherence. Methods This study was guided by the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines. Relevant articles were retrieved from the EMBASE, PubMed, Cochrane Library, Web of Science, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) databases. The PROMs were then evaluated based on the COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) guidelines. Results A total of 121 unique medication adherence PROMs from 214 studies were identified. Hypotheses testing for construct validity and internal consistency were the most frequently assessed measurement properties. PROMs with at least a moderate level of evidence for ≥5 measurement properties include the Adherence Starts with Knowledge 20, Compliance Questionnaire-Rheumatology, General Medication Adherence Scale, Hill-Bone Scale, Immunosuppressant Therapy Barrier Scale, Medication Adherence Reasons Scale (MAR-Scale) revised, 5-item Medication Adherence Rating Scale (MARS-5), 9-item MARS (MARS-9), 4-item Morisky Medication Adherence Scale (MMAS-4), 8-item MMAS (MMAS-8), Self-efficacy for Appropriate Medication Adherence Scale, Satisfaction with Iron Chelation Therapy, Test of Adherence to Inhalers, and questionnaire by Voils. The MAR-Scale revised, MMAS-4, and MMAS-8 have been administered electronically. Conclusions This study identified 121 PROMs for medication adherence and provided synthesized evidence for the measurement properties of these PROMs. The findings from this study may assist clinicians and researchers in selecting suitable PROMs to assess medication adherence.
    Type of Medium: Online Resource
    ISSN: 1438-8871
    URL: Article
    DOI: 10.2196/19179
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2020
    detail.hit.zdb_id: 2028830-X
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  • 5
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    Online Resource
    Health Natural Language Processing: Methodology Development and Applications
    JMIR Publications Inc. ; 2021
    In:  JMIR Medical Informatics Vol. 9, No. 10 ( 2021-10-21), p. e23898-
    Details
    In: JMIR Medical Informatics, JMIR Publications Inc., Vol. 9, No. 10 ( 2021-10-21), p. e23898-
    Abstract: With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and eligibility criteria of clinical trials. Health natural language processing, as an interdisciplinary field of natural language processing and health care, plays a substantial role in a wide scope of both methodology development and applications. This editorial shares the most recent methodology innovations of health natural language processing and applications in the medical domain published in this JMIR Medical Informatics special theme issue entitled "Health Natural Language Processing: Methodology Development and Applications".
    Type of Medium: Online Resource
    ISSN: 2291-9694
    URL: Article
    DOI: 10.2196/23898
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2021
    detail.hit.zdb_id: 2798261-0
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