A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies pill-prescription from mobile images. Specifically, we define a so-called pill-prescription matching task, which attempts to match the images of the pills taken with the pills' names in the prescription. We then propose PIMA, a novel approach using Graph Neural Network (GNN) and contrastive learning to address the targeted problem. In particular, GNN is used to learn the spatial correlation between the text boxes in the prescription and thereby highlight the text boxes carrying the pill names. In addition, contrastive learning is employed to facilitate the modeling of cross-modal similarity between textual representations of pill names and visual representations of pill images. We conducted extensive experiments and demonstrated that PIMA outperforms baseline models on a real-world dataset of pill and prescription images that we constructed. Specifically, PIMA improves the accuracy from 19.09% to 46.95% compared to other baselines. We believe our work can open up new opportunities to build new clinical applications and improve medication safety and patient care.Comment: Accepted for publication and presentation at the 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2022

Non-destructive detection of counterfeit and substandard medicines using X-ray diffraction

The prevalence of counterfeit and substandard medicines has been growing rapidly over the past decade, and fast, non-destructive techniques for their detection are urgently needed to counter this trend. In this thesis, both energy-dispersive X-ray diffraction (EDXRD) and pixelated diffraction (“PixD”) combined with chemometric methods were assessed for their effectiveness in detecting poor-quality medicines within their packaging. Firstly, a series of caffeine, paracetamol and cellulose mixtures of known concentrations were pressed into tablets. EDXRD spectra of each tablet were collected both with and without packaging. Principal component analysis (PCA) and partial least-squares regression (PLSR) were used to study the data and construct calibration models for quantitative analysis. The concentration prediction errors for the packaged data were found to be very similar to those obtained in the unpackaged case, and were also on a par with reported values in the literature using higher-resolution angular-dispersive X-ray diffraction (ADXRD). Following this, soft independent modelling by class analogy (SIMCA) classification was used to compare EDXRD spectra from a test set of over-the-counter (OTC) medicines containing various combinations of active pharmaceutical ingredients (APIs) against PCA models constructed using spectra collected for paracetamol and ibuprofen samples. The test samples were selected to emulate different levels of difficulty in authenticating medicines correctly, ranging from completely different APIs (easy) to those with a small quantity of additional API (difficult). This classification study found that the sensitivity and specificity were optimal at data acquisition times on the order of 75~150s, and regardless of whether layers of blister and card packaging surrounded the tablet in question. This experiment was repeated on a novel, compact system incorporating a pixellated detector, which was found to reduce the required data acquisition times for optimal classification by a factor of five

Identification of unique microbial signatures pre- and post-coitus in male-female pairings by massively parallel sequencing and its potential to detect sexual contact

Background: The capture of male DNA, post-assault, is important in sexual assault investigation, particularly where an offender is unknown to the victim. The recovery of DNA often occurs when the female victim undergoes a forensic medical assessment. Analysis regularly results in mixed autosomal DNA profiles. As these results contain both victim and perpetrator DNA, they are often difficult to interpret a searchable male profile. While STR profiling of the male Y-chromosome is often used to overcome this, the successful identification of an individual can be hindered by the paternal inheritance pattern of Y-STRs. An adjunct method of perpetrator identification lies with microbiome analysis using massively parallel sequencing. Aims: This study aimed to identify ASVs that were unique to each participant and compare the bacterial communities found on the genitals pre- and post-coitus. From the sequence data derived, statistical analysis was performed to investigate if bacteria sequences could be used to infer contact between each male-female pairing. Content: Samples were collected from 14 male-female pairings across two recruitment cohorts. Volunteers were asked to self-collect samples pre- and post-coitus. Samples were extracted using PureLink™ Microbiome DNA Purification Kit. Extracted DNA underwent library preparation using primers targeting the V1-V9 hypervariable regions of the bacterial 16S rRNA gene (~1,449 bp). Libraries were sequenced by PacBio® SMRT Sequel II sequencing platform. Unique bacterial signatures were detected in low frequencies (<1%) in male and female participants pre-coitus. The data indicates a disruption to microbial composition post-coitus. Further genomic analysis is needed to confirm species and subspecies classification of bacteria

2018 Annual Research Symposium Abstract Book

2018 annual volume of abstracts for science research projects conducted by students at Trinity College

An Automated Method to Enrich and Expand Consumer Health Vocabularies Using GloVe Word Embeddings

Clear language makes communication easier between any two parties. However, a layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical jargon, which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this dissertation, we present an automatic method to enrich existing concepts in a medical ontology with additional laymen terms and also to expand the number of concepts in the ontology that do not have associated laymen terms. Our work has the benefit of being applicable to vocabularies in any domain. Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. We improve these vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. By performing iterative feedback using GloVe’s candidate terms, we can boost the number of word occurrences in the co-occurrence matrix allowing our approach to work with a smaller training corpus. Our novel algorithms and GloVe were evaluated using two laymen datasets from the National Library of Medicine (NLM), the Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV) and the MedlinePlus Healthcare Vocabulary. For our first goal, enriching concepts, the results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Our best algorithm enhanced the corpus with synonyms from WordNet, outperformed GloVe with an F-score relative improvement of 25%. For our second goal, expanding the number of concepts with related laymen’s terms, our synonym-enhanced GloVe outperformed GloVe with a relative F-score relative improvement of 63%. The results of the system were in general promising and can be applied not only to enrich and expand laymen vocabularies for medicine but any ontology for a domain, given an appropriate corpus for the domain. Our approach is applicable to narrow domains that may not have the huge training corpora typically used with word embedding approaches. In essence, by incorporating an external source of linguistic information, WordNet, and expanding the training corpus, we are getting more out of our training corpus. Our system can help building an application for patients where they can read their physician\u27s letters more understandably and clearly. Moreover, the output of this system can be used to improve the results of healthcare search engines, entity recognition systems, and many others

Evaluation of emerging screening technologies for the on-site detection and identification of methamphetamine and its precursors in simulated clandestine lab operations

Stimulant drugs comprise one of the top drug categories abused in the United States. Due to its accessibility, low price, and manufacturing simplicity, methamphetamine is frequently placed within the top 10 seized drugs in the country. As of March 2023, methamphetamine is the most seized controlled substance in the United States, with 34,291 kg. In 2022, the United States seized over 79,000 kg of methamphetamine. One reason for the proliferation of methamphetamine is related to the production itself, which does not require large warehouses but can be manufactured in houses using relatively accessible materials and small containers. When a clandestine laboratory is investigated, law enforcement and CSIs must be able to identify what drug is being produced and what hazards are associated with the production method being utilized by the clandestine laboratory. Due to shifting manufacturing routes by underground chemists, it has become difficult for forensic scientists to identify illicit substances and their respective precursors reliably. Indeed, rapid analytical tools that facilitate the identification of legal and scheduled drugs are highly desirable for first responders, health personnel, and forensic chemists. This thesis addresses this deficit by investigating Raman Spectroscopy and Direct Analysis in Real-Time Mass Spectrometry (DART-MS) to determine ways to improve mixture identification. This research focuses on methamphetamine and its precursors, ephedrine, and pseudoephedrine. However, the scope was expanded to include several other drugs and cutting agents of concern in the United States. This research compared three Portable Raman instrumentations for detecting methamphetamine and its precursors in binary mixtures. From a practical perspective, the TacticID GP from B&W Tek (Newark, DE) and the Mira XTR DS from Metrohm USA (Riverview, FL) were determined to be suitable for on-site detection due to their simple operation and color-coded results that provide immediate safety information for the results, in case the user is not familiar with the compound. The mixture analysis function allowed for better identification of the controlled substance due to the controlled substance being the minor component in most cases. The iRaman Prime from B&W Tek (Newark, DE) had limitations for the mixtures. Software used to compare the collected spectrum to the library does not include the mixture analysis function to help identify complex samples. There are other software present; however, the software requires an additional understanding of statistical analysis that first responders may not be equipped with. This research also sought to improve Raman’s detection of mixtures using machine learning, specifically convolutional neural networks or CNN. The iRaman Prime from B&W Tek (Newark, DE) was used for this purpose, which had the most difficulty identifying mixtures due to the built-in software available. Using CNN, the ability to identify both components in the mixtures improved to 94.0 % compared to 71.5 % using cosine similarity. However, the algorithm had difficulty identifying the drugs and adulterants in the authentic samples. The difficulty is because the authentic samples consisted of more complex samples, with more than two compounds present. Further research can be done to train the algorithm with more complex samples or include the class of compounds to give an overall result for compounds not in the training set. Lastly, the utility of DART-MS was investigated for methamphetamines. The Data Interpretation Tool (DIT) v. 2 from NIJ/NIST was used to see how well DART-MS could identify multiple components in mixtures. Authentic samples from the Maryland State Police Forensic Sciences Division were used as more complex samples to compare these instruments with more realistic ones. The DIT and DART-MS identified 82.5 % of the binary mixtures. The DIT also successfully identified at least one controlled substance in the samples containing controlled substances. This thesis demonstrates that the combination of Raman Spectroscopy with CNN and DART-MS with DIT improves their respective instrument\u27s ability to detect mixtures, making them better equipped for use in clandestine operations and regular forensic casework

Exploring the Accessibility, Affordability, and Equitability of Telecontraception Platforms and Their Implications for Reproductive Health Care

Telemedicine has skyrocketed to national attention with the COVID-19 crisis, raising questions about how to best use virtual tools to support public health. One emerging sector of telemedicine is the rise of telecontraception platforms, such as Nurx, Pill Club, and Planned Parenthood Direct. Known as “the Uber for birth control”, these platforms represent a growing market and innovative approach that aim to address barriers to obtaining birth control such as geography, cost, time, and gatekeeping by providing contraception and other sexual and reproductive healthcare services directly to consumers (Sundstrom et al. 2019; Grindlay and Grossman 2016; Chuck 2017; Stormo et al. 2011). Contraception historically was and currently is riddled with red tape for women trying to access critical care they need to make decisions about their own bodies and lives. Telecontraception represents an important potential solution to these long-standing issues, yet its impact on women and health care has not yet been studied in depth. What are telecontraception platforms adding to the current landscape of reproductive health care? What problems are they solving and where are they falling short? Using mixed methods, this research aims to address this gap by exploring the accessibility, affordability, and equitability of these growing platforms. Findings illustrate telecontraception alleviates many existing access barriers. Yet there are mixed findings regarding affordability and equitability. Cost, insurance, and state availability limit the scope of telecontraception and mirror existing systemic challenges women face on the ground. This carries important implications because this research also found that the majority of women across the United States expressed strong pregnancy avoidance attitudes regardless of subgroup. Having a large presence of women legislators alongside other state conditions was linked to telecontraception availability in Republican and Democrat politically controlled states, suggesting that gender and having women in positions of power, in combination with other political, social, and economic state-level factors, is another growing and important factor to consider in advocating for issues related to women such as reproductive rights and policy. Overall, this project identifies areas of progress and opportunities for improvement not only for telecontraception but for health apps and telemedicine more broadly

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject