Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder

Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study

BACKGROUND

Unlike research project-based health data collection (questionnaires and interviews), social media platforms allow patients to freely discuss their health status and obtain peer support. Previous literature has pointed out that both public and private social platforms can serve as data sources for analysis.

OBJECTIVE

This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in patients with uterine fibroids after focused ultrasound ablation surgery.

METHODS

Screenshots taken from clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study. From 408 patients diagnosed with uterine fibroids in Chongqing Haifu Hospital between 2010 and 2020, we searched for symptom burdens in over 900,000 words of WeChat group chats. We first built a corpus of symptoms by manually coding 30% of the WeChat texts and then used regular expressions in Python to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Finally, we analyzed the relationship between the population baseline data and conceptual symptoms; quantitative and qualitative results were examined.

RESULTS

A total of 408 patients with uterine fibroids were included in the study; 190,000 words of free text were obtained after data cleaning. The mean age of the patients was 39.94 (SD 6.81) years, and their mean BMI was 22.18 (SD 2.78) kg/m2. The median reporting times of the 7 major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Logistic regression models identified preoperative menstrual duration (odds ratio [OR] 1.14, 95% CI 5.86-6.37; P=.009), age of menophania (OR -1.02 , 95% CI 11.96-13.47; P=.03), and the number (OR 2.34, 95% CI 1.45-1.83; P=.04) and size of fibroids (OR 0.12, 95% CI 2.43-3.51; P=.04) as significant risk factors for postoperative symptoms.

CONCLUSIONS

Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis. By extracting the conceptual information about patients' health-related quality of life, we can adopt personalized treatment for patients at different stages of recovery to improve their quality of life. Python-based text mining of free-text data can accurately extract symptom burden and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.

Using WeChat clinician-patient group communication data to identify symptom burdens in patients with uterine fibroids under focused ultrasound ablation surgery treatment :Qualitative Study (Preprint).. [DOI: 10.2196/preprints.43995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Unlike research project-based health data collections(questionnaires, interviews), social media platforms, which allow patients to freely discuss their health status and obtain peer support.Previous literature has pointed out that both public and private social platforms can serve as data sources for analysing.

OBJECTIVE

This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in uterine fibroids after focused ultrasound ablation surgery.

METHODS

Screenshots taken from the clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study, which used regular expressions in Python to search for symptom burdens in over 900,000 words of WeChat group-chats associated with 408 patients in Chongqing Haifu Hospital diagnosed with uterine fibroids between 2010 and 2020. We first built a corpus of symptoms by manually coding 30% of the WeChat texts, and then used regular expressions to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Then we analyzed the relationship between the population baseline data and conceptual symptoms, Quantitative and qualitative results were examined.

RESULTS

A total of 190,000 words of uterine fibroids patients' free text were finally obtained after data cleaning. A total of 408 patients were included in the study. The age of the patients was 39.94±6.81 years, and their BMI was 22.18±2.78 (kg/m^2). The median reporting times of the seven major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Results showed that patients with dysmenorrhea were younger(mean 38.26 (SD 7.05), P=.004) and slimmer (mean 22.37 (SD 3.81), P=.04), with lower fertility and parity (P<.05), and tended to stay longer in the hospital (P<.05). Logistic regression models identified preoperative menstrual duration (OR 1.14, 95% CI 5.86-6.37; P= .009), age of menophania (OR -1.02 ,95%CI 11.96-13.47,P=.03), and the number(OR 2.34,95% CI 1.45-1.83,P=.04) and size of fibroids(OR 0.12,95% CI 2.43-3.51,P=.04) as significant risk factors for postoperative symptoms.

CONCLUSIONS

Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis, extracting the conceptual information about patients' HRQol,adopt personalized treatment for patients at different stages of recovery to improve the quality of life of patients. Python-based text mining of free-text data can accurately extract symptom burden administered and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.

CLINICALTRIAL

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A Systematic Approach to Configuring MetaMap for Optimal Performance

BACKGROUND

 MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward.

OBJECTIVE

 To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance.

METHODS

 MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input. During testing, MetaMap was configured with the default option, one behavior option, and two behavior options. For each configuration, cosine and soft cosine similarity scores between identified entities and gold-standard terms were computed for 40 annotated abstracts (422 sentences). The similarity scores were used to calculate and compare the overall percentages of exact matches, similar matches, and missing gold-standard terms among the abstracts for each configuration. The results were manually spot-checked. The precision, recall, and F-measure (β =1) were calculated.

RESULTS

 The percentages of exact matches and missing gold-standard terms were 0.6-0.79 and 0.09-0.3 for one behavior option, and 0.56-0.8 and 0.09-0.3 for two behavior options, respectively. The percentages of exact matches and missing terms for soft cosine similarity scores exceeded those for cosine similarity scores. The average precision, recall, and F-measure were 0.59, 0.82, and 0.68 for exact matches, and 1.00, 0.53, and 0.69 for missing terms, respectively.

CONCLUSION

 We demonstrated a systematic approach that provides objective and accurate evidence guiding MetaMap configurations for optimizing performance. Combining objective evidence and the current practice of using principles, experience, and intuitions outperforms a single strategy in MetaMap configurations. Our methodology, reference codes, measurements, results, and workflow are valuable references for optimizing and configuring MetaMap.

Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology

Background

Knowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses.

Method

We developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted.

Results

We demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org. Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework.

Conclusion

In an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.

Deciphering Diversity of Mental Models in Neurodevelopmental Disorders: Knowledge Graph Representation of Public Data Using Natural Language Processing (Preprint)

Background

Understanding how individuals think about a topic, known as the mental model, can significantly improve communication, especially in the medical domain where emotions and implications are high. Neurodevelopmental disorders (NDDs) represent a group of diagnoses, affecting up to 18% of the global population, involving differences in the development of cognitive or social functions. In this study, we focus on 2 NDDs, attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), which involve multiple symptoms and interventions requiring interactions between 2 important stakeholders: parents and health professionals. There is a gap in our understanding of differences between mental models for each stakeholder, making communication between stakeholders more difficult than it could be.

Objective

We aim to build knowledge graphs (KGs) from web-based information relevant to each stakeholder as proxies of mental models. These KGs will accelerate the identification of shared and divergent concerns between stakeholders. The developed KGs can help improve knowledge mobilization, communication, and care for individuals with ADHD and ASD.

Methods

We created 2 data sets by collecting the posts from web-based forums and PubMed abstracts related to ADHD and ASD. We utilized the Unified Medical Language System (UMLS) to detect biomedical concepts and applied Positive Pointwise Mutual Information followed by truncated Singular Value Decomposition to obtain corpus-based concept embeddings for each data set. Each data set is represented as a KG using a property graph model. Semantic relatedness between concepts is calculated to rank the relation strength of concepts and stored in the KG as relation weights. UMLS disorder-relevant semantic types are used to provide additional categorical information about each concept’s domain.

Results

The developed KGs contain concepts from both data sets, with node sizes representing the co-occurrence frequency of concepts and edge sizes representing relevance between concepts. ADHD- and ASD-related concepts from different semantic types shows diverse areas of concerns and complex needs of the conditions. KG identifies converging and diverging concepts between health professionals literature (PubMed) and parental concerns (web-based forums), which may correspond to the differences between mental models for each stakeholder.

Conclusions

We show for the first time that generating KGs from web-based data can capture the complex needs of families dealing with ADHD or ASD. Moreover, we showed points of convergence between families and health professionals’ KGs. Natural language processing–based KG provides access to a large sample size, which is often a limiting factor for traditional in-person mental model mapping. Our work offers a high throughput access to mental model maps, which could be used for further in-person validation, knowledge mobilization projects, and basis for communication about potential blind spots from stakeholders in interactions about NDDs. Future research will be needed to identify how concepts could interact together differently for each stakeholder.

Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records

BACKGROUND

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives.

METHODS

To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method.

RESULTS

Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders.

CONCLUSION

Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making.

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

PURPOSE OF REVIEW

The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each.

RECENT FINDINGS

Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks.

SUMMARY

AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

Informatics and machine learning methods for health applications

The 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) provided a multidisciplinary forum for computational scientists and experimental biologists to share recent advances on all aspects of intelligent computing, informatics and data science in biology and medicine. ICIBM 2020 was held as a virtual conference on August 9-10, 2020, including four live sessions with forty-one oral presentations over video conferencing. In this special issue, ten high-quality manuscripts were selected after peer-review from seventy-five submissions to represent the medical informatics and decision making aspect of the conference. In this editorial, we briefly summarize these ten selected manuscripts.