Machine Learning-based Analysis of Publications Funded by the National Institutes of Health's Initial COVID-19 Pandemic Response.

Background: The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis. Methods: NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning-based model built to identify common topics/themes within the publications. Results and Conclusions: We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning-based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase.

Analysis of National Institutes of Health Funding for the COVID-19 Pandemic.

Background: Evaluating the National Institute's Health's (NIH's) response to the coronavirus disease 2019 (COVID-19) pandemic via grants and clinical trials is crucial to determining the impact they had on aiding US citizens. We determined how the NIH's funding for COVID-19 research was disbursed and used by various institutions across the United States. Methods: We queried NIH RePORTER and isolated COVID-19-related grants from January 2020 to December 2021. We analyzed grant type, geographical location, and awardee institution. Manuscripts published from these grants were quantitatively analyzed. COVID-19 clinical trials were mapped and distances from counties to clinical trial sites were calculated using ArcGis. Results: A total of 2401 COVID-19 NIH grants resulted in 14 654 manuscripts from $4.2 billion and generated more than 150 000 citations. R01s make up 32% of grants (763/2401) and 8% of funding ($329 million). UM1 grants account for the majority of funding (30.8%; $1.3 Billion). Five states received 50.6% of funding: North Carolina, Washington, New York, California, and Massachusetts. Finally, of the 1806 clinical trials across 1266 sites in the United States, the majority were in metropolitan areas in close proximity to areas of high COVID-19 disease burden. Conclusions and Relevance: Evaluating the outcome of the NIH's response to the COVID-19 pandemic is of interest to the general public. The present study finds that the NIH disbursed more than $4 billion in funding to large consortiums and clinical trials to develop diagnostics, therapeutics, and vaccines. Approximately 8% of funding was used for R01 grants. Clinical trial sites were generally located in areas of high COVID-19 burden.

Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review.

Purpose: There is great promise in use of machine learning (ML) for the diagnosis, prognosis, and treatment of various medical conditions in ophthalmology and beyond. Applications of ML for ocular neoplasms are in early development and this review synthesizes the current state of ML in ocular oncology. Methods: We queried PubMed and Web of Science and evaluated 804 publications, excluding nonhuman studies. Metrics on ML algorithm performance were collected and the Prediction model study Risk Of Bias ASsessment Tool was used to evaluate bias. We report the results of 63 unique studies. Results: Research regarding ML applications to intraocular cancers has leveraged multiple algorithms and data sources. Convolutional neural networks (CNNs) were one of the most commonly used ML algorithms and most work has focused on uveal melanoma and retinoblastoma. The majority of ML models discussed here were developed for diagnosis and prognosis. Algorithms for diagnosis primarily leveraged imaging (e.g., optical coherence tomography) as inputs, whereas those for prognosis leveraged combinations of gene expression, tumor characteristics, and patient demographics. Conclusions: ML has the potential to improve the management of intraocular cancers. Published ML models perform well, but were occasionally limited by small sample sizes owing to the low prevalence of intraocular cancers. This could be overcome with synthetic data enhancement and low-shot ML techniques. CNNs can be integrated into existing diagnostic workflows, while non-neural networks perform well in determining prognosis.

Landscape and future directions of machine learning applications in closed-loop brain stimulation.

Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson's, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are "open-loop" and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of "closed-loop" systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict / identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson's, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease.