Precision information extraction for rare disease epidemiology at scale

Abstract

Background The United Nations recently made a call to address the challenges of an estimated 300 million persons worldwide living with a rare disease through the collection, analysis, and dissemination of disaggregated data. Epidemiologic Information (EI) regarding prevalence and incidence data of rare diseases is sparse and current paradigms of identifying, extracting, and curating EI rely upon time-intensive, error-prone manual processes. With these limitations, a clear understanding of the variation in epidemiology and outcomes for rare disease patients is hampered. This challenges the public health of rare diseases patients through a lack of information necessary to prioritize research, policy decisions, therapeutic development, and health system allocations. Methods In this study, we developed a newly curated epidemiology corpus for Named Entity Recognition (NER), a deep learning framework, and a novel rare disease epidemiologic information pipeline named EpiPipeline4RD consisting of a web interface and Restful API. For the corpus creation, we programmatically gathered a representative sample of rare disease epidemiologic abstracts, utilized weakly-supervised machine learning techniques to label the dataset, and manually validated the labeled dataset. For the deep learning framework development, we fine-tuned our dataset and adapted the BioBERT model for NER. We measured the performance of our BioBERT model for epidemiology entity recognition quantitatively with precision, recall, and F1 and qualitatively through a comparison with Orphanet. We demonstrated the ability for our pipeline to gather, identify, and extract epidemiology information from rare disease abstracts through three case studies. Results We developed a deep learning model to extract EI with overall F1 scores of 0.817 and 0.878, evaluated at the entity-level and token-level respectively, and which achieved comparable qualitative results to Orphanet’s collection paradigm. Additionally, case studies of the rare diseases Classic homocystinuria, GRACILE syndrome, Phenylketonuria demonstrated the adequate recall of abstracts with epidemiology information, high precision of epidemiology information extraction through our deep learning model, and the increased efficiency of EpiPipeline4RD compared to a manual curation paradigm. Conclusions EpiPipeline4RD demonstrated high performance of EI extraction from rare disease literature to augment manual curation processes. This automated information curation paradigm will not only effectively empower development of the NIH Genetic and Rare Diseases Information Center (GARD), but also support the public health of the rare disease community.

Classifying the risk for myasthenic crisis using data-driven explainable machine learning with informative feature design and variance control – a pilot study

“…Although epidemiological assessment 22 , diagnosis 23 , classification of disease subtypes 24 , and therapeutic discovery 25 for rare diseases are thought to be aided by various machine learning approaches, studies employing machine learning to monitor disease progression in MG are scarce. Further, even though disease progression models to describe disease course over time are now frequently used in drug development 26 , clinical use of disease progression modeling, e.g., to aid clinical decision making, is uncommon, particularly in rare diseases including MG.…”

The division of rare diseases research innovation at the national center for advancing translational sciences, NIH: mission, history, and current research activities

“…Collectively, rare diseases impact approximately 30 million people in the U.S. [1,2] . In addition, multiple economic analyses [3] , including work from DRDRI [4,5] , have estimated that, in the US, the direct medical costs of rare diseases are around $400 billion/year. Note that these are only direct costs, and do not include indirect/non-medical costs, estimated to be nearly $550 billion [3] in the US.…”

Unraveling Huntington's Disease: Concepts, Treatments and Health Policy Challenges

“…The constant updating of databases is a crucial factor in collecting information and developing effective public policies to address these pathologies. (KARIAMPUZHA et al, 2023;ZHANG et al, 2023) Verifying these reports poses significant challenges due to the complexity of the required tests, such as metabolite analysis, pathological examinations, and genetic testing. Consequently, databases often have information gaps and delays.…”

“…Therefore, it is imperative to classify these conditions to guide targeted and more effective research. (KARIAMPUZHA et al, 2023;ZHANG et al, 2023) Epidemiological monitoring is essential to evaluate the effectiveness of pharmacological, physiotherapeutic, speech therapy, and psychiatric treatments for Huntington's Disease. It can be challenging to implement, but it is necessary.…”