Standardization of immunotherapy adverse events in patient information leaflets and development of an interface terminology for outpatients' monitoring

Automatic SNOMED CT coding of Chinese clinical terms via attention-based semantic matching

BACKGROUND

A considerable amount of meaningful information is routinely recorded in Chinese clinical data in text format, referred to as Chinese clinical terms. The lack of coding is a major difficulty hindering the application of clinical terms. SNOMED CT is a widely used and comprehensive clinical health care terminology collection because of its coverage, granularity, clinical orientation, and logical underpinning. It is useful and efficient for automatically assigning SNOMED CT codes to Chinese clinical terms, but it still faces several problems. Current cross-language clinical term matching studies rely on external resources, such as machine translation and rule-based methods. Semantic matching methods have achieved strong performance on text matching, but few studies have been done on cross-language clinical term matching. We present an effective attention-based semantic matching algorithm to automatically cross-language code Chinese clinical terms with SNOMED CT.

METHOD

Firstly, BERT was used to turn the input into word embedding. Then, the word embeddings were encoded through a BiLSTM with self-attention to focus on capturing distant relationships among words with different weights depending on their contribution to semantic matching. Then, decomposable attention was used to make semantic matching trivially parallelizable to speed up calculation. Finally, fully connected layers and a sigmoid were utilized to output matching results.

RESULTS

The 29,960 manually coded Chinese clinical terms, 30,040 unmatched Chinese clinical terms and SNOMED CT codes were collected to evaluate the proposed method. Compared with the existing semantic matching method, the proposed approach achieves state-of-the-art results demonstrating the effectiveness of the method with an accuracy of 0.905, a precision of 0.856, a recall of 0.518, and an F-measure of 0.645. The proposed Chinese-English bilingual term mapping, Chinese character-level and word-level encoder, English word-level encoder, BERT model, and attention mechanism performed better than other methods.

CONCLUSION

The proposed automatic SNOMED CT coding approach of Chinese clinical terms via attention-based semantic matching can improve the performance of automated SNOMED CT code assignment for Chinese clinical terms and improve the efficiency of the code assignment.

Experiences of cancer immunotherapy with immune checkpoint inhibitors (ExCIm)-insights of people affected by cancer and healthcare professionals: a qualitative study protocol

INTRODUCTION

There is a global interest in cancer immunotherapy. Clinical trials have found that one group, immune checkpoint inhibitors (ICIs), has demonstrated clinical benefits across various cancers. However, research focused on the experiences of people affected by cancer who have undergone this treatment using qualitative methodology is currently limited. Moreover, little is known about the experiences and education needs of the healthcare staff supporting the people receiving these immunotherapies. This study therefore seeks to explore the experiences of using ICIs by both the people affected by cancer and the healthcare professionals who support those people, and use the findings to make recommendations for ICI supportive care guidance development, cancer immunotherapy education materials for healthcare professionals, cancer policy and further research.

METHODS AND ANALYSIS

Patient participants (n=up to 30) will be recruited within the UK. The sample will incorporate a range of perspectives, sociodemographic factors, diagnoses and ICI treatments, yet share some common experiences. Healthcare professionals (n=up to 15) involved in supporting people receiving immunotherapy will also be recruited from across the UK. Data will be generated through in-depth, semistructured interviews. Reflexive thematic analysis will be used to obtain thorough understanding of individual's perspectives on, and experiences of, immunotherapy. Study dates are as follows: December 2019-March 2022.

ETHICS AND DISSEMINATION

The research will be performed in accordance with the UK Policy for Health and Social Care Research and Cardiff University's Research Integrity and Governance Code of Practice (2018). The study received ethical approval from the West Midlands and Black Country Research Ethics Committee in October 2019. Health Research Authority and Health and Care Research Wales approvals were confirmed in December 2019. All participants will provide informed consent. Findings will be published in peer-reviewed journals, non-academic platforms, the Macmillan Cancer Support website, disseminated at relevant national and international conferences and presented via a webinar. The study is listed on the National Institute for Health Research (NIHR) Clinical Research Network Central Portfolio.

Different patterns of treatment-related adverse events of programmed cell death-1 and its ligand-1 inhibitors in different cancer types: A meta-analysis and systemic review of clinical trials

Programmed cell death receptor-1 and its ligand-1 (PD-1/PD-L1) inhibitors have been applied to many cancers, but the difference of treatment-related adverse events (AEs) across cancer types remains unknown. We performed a meta-analysis and systemic review to compare the incidences of commonly reported all-grade AEs across cancer types and found that the most frequent AEs were fatigue, rash/pruritus, loss of appetite/nausea and diarrhea. However, each cancer type also had its higher incidences of AEs involving a relevant system, such as melanoma with epidermal AEs (rash, diarrhea and enterocolitis), lung cancer with dyspnea and pneumonitis, digestive system cancers with amylase and lipase elevation; and renal cell and urothelial cancer with kidney injury (creatinine elevation and proteinuria). However, the incidence of hepatitis did not follow the pattern to show a difference. We did another comparison between PD-1 and PD-L1 inhibitors in lung cancer and urothelial cancer respectively, and found that the risk of most AEs did not differ much, except for more hypothyroidism in PD-1 inhibitors, and more kidney injury in PD-L1 inhibitors. Besides possible immunological mechanisms for treatment-related AEs, the influence of previous radiotherapy and the clinical characteristics of the diseases themselves should also be considered and is worth further investigation. With the result of this meta-analysis, clinicians could estimate the risk of certain AE in certain cancer type, to make treatment options and to customize monitor strategies.

Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study

Background

Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles.

Objective

The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration–approved immune checkpoint inhibitors.

Methods

In our framework, we first used the Food and Drug Administration’s Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals.

Results

By our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals.

Conclusions

We demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

BACKGROUND

Mobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the interoperability of sensor data and EHR medical data is a challenge. EHR data collected from distributed systems have different structures, semantics, and coding mechanisms. As a result, building a transparent CDSS that can work as a portable plug-and-play component in any existing EHR ecosystem requires a careful design process. Ontology and medical standards support the construction of semantically intelligent CDSSs.

METHODS

This paper proposes a comprehensive MH framework with an integrated CDSS capability. This cloud-based system monitors and manages type 1 diabetes mellitus. The efficiency of any CDSS depends mainly on the quality of its knowledge and its semantic interoperability with different data sources. To this end, this paper concentrates on constructing a semantic CDSS based on proposed FASTO ontology.

RESULTS

This realistic ontology is able to collect, formalize, integrate, analyze, and manipulate all types of patient data. It provides patients with complete, personalized, and medically intuitive care plans, including insulin regimens, diets, exercises, and education sub-plans. These plans are based on the complete patient profile. In addition, the proposed CDSS provides real-time patient monitoring based on vital signs collected from patients' wireless body area networks. These monitoring include real-time insulin adjustments, mealtime carbohydrate calculations, and exercise recommendations. FASTO integrates the well-known standards of HL7 fast healthcare interoperability resources (FHIR), semantic sensor network (SSN) ontology, basic formal ontology (BFO) 2.0, and clinical practice guidelines. The current version of FASTO includes 9577 classes, 658 object properties, 164 data properties, 460 individuals, and 140 SWRL rules. FASTO is publicly available through the National Center for Biomedical Ontology BioPortal at https://bioportal.bioontology.org/ontologies/FASTO .

CONCLUSIONS

The resulting CDSS system can help physicians to monitor more patients efficiently and accurately. In addition, patients in rural areas can depend on the system to manage their diabetes and emergencies.