Artificial Intelligence in Multiphoton Tomography: Atopic Dermatitis Diagnosis

The diagnostic possibilities of multiphoton tomography (MPT) in dermatology have already been demonstrated. Nevertheless, the analysis of MPT data is still time-consuming and operator dependent. We propose a fully automatic approach based on convolutional neural networks (CNNs) to fully realize the potential of MPT. In total, 3,663 MPT images combining both morphological and metabolic information were acquired from atopic dermatitis (AD) patients and healthy volunteers. These were used to train and tune CNNs to detect the presence of living cells, and if so, to diagnose AD, independently of imaged layer or position. The proposed algorithm correctly diagnosed AD in 97.0 ± 0.2% of all images presenting living cells. The diagnosis was obtained with a sensitivity of 0.966 ± 0.003, specificity of 0.977 ± 0.003 and F-score of 0.964 ± 0.002. Relevance propagation by deep Taylor decomposition was used to enhance the algorithm’s interpretability. Obtained heatmaps show what aspects of the images are important for a given classification. We showed that MPT imaging can be combined with artificial intelligence to successfully diagnose AD. The proposed approach serves as a framework for the automatic diagnosis of skin disorders using MPT

Asthma in electronic health records: validity and phenotyping

This PhD thesis explores the validation of asthma in electronic health records (EHR) and the characteristics of asthma phenotypes in the UK using CPRD GOLD, HES and ONS data. The absence of a universal case definition, the overlap with other diseases and the incomplete recording of diagnostic markers makes the identification of asthma patients in EHR challenging. Furthermore, asthma phenotypes have previously been established based on cluster analysis in small populations, but their prevalence, treatment and outcomes in the general population have not been investigated. Firstly, I conducted a systematic review to understand how past epidemiological studies have validated asthma recording in EHR, including a critical appraisal and list of test measure values for the selected studies. Secondly, I validated algorithms to reliably ascertain the asthma status of patients in CPRD GOLD. This validation study identified multiple algorithms with PPV greater than 80%. The most practical algorithm (presence of a specific asthma diagnostic code) had a PPV of 86.4 (95% CI:77.4-95.4). Thirdly, I quantified the concomitant occurrence of asthma in COPD patients and vice versa in CPRD GOLD. After detailed case review, concomitant asthma and COPD was concluded in 14.8% of validated asthma patients and in 14.5% of validated COPD patients. However, asthma diagnoses may be unreliable in COPD patients, as over 50% of COPD patients had received an asthma code. Finally, I examined the prevalence, treatment, outcomes and characteristics of established asthma phenotypes in CPRD GOLD. Only a minority of patients (37.3%) were classified into these phenotypes using stringent inclusion criteria. Exacerbation rates/1000PY were lowest for benign asthma (106.8 [95% CI:101.2-112.3]), and highest for obese non-eosinophilic asthma (469.0 [95% CI:451.7-486.2]). In conclusion, this thesis provides information on the validation of asthma diagnoses in EHR and the prevalence, treatment, outcomes of predefined asthma phenotypes in the UK primary care population

The landscape of the methodology in drug repurposing using human genomic data:a systematic review

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record (EHR) data, public availability of various databases containing biological and clinical information, and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1st May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies, and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.This work is written on behalf of the Women’s Brain Project (WBP) (www.womensbrainproject.com/), an international organization advocating for women’s brain and mental health through scientific research, debate and public engagement. The authors would like to gratefully acknowledge Maria Teresa Ferretti and Nicoletta Iacobacci (WBP) for the scientific advice and insightful discussions; Roberto Confalonieri (Alpha Health) for reviewing the manuscript; the Bioinfo4Women programme of Barcelona Supercomputing Center (BSC) for the support. This work has been supported by the Spanish Government (SEV 2015–0493) and grant PT17/0009/0001, of the Acción Estratégica en Salud 2013–2016 of the Programa Estatal de Investigación Orientada a los Retos de la Sociedad, funded by the Instituto de Salud Carlos III (ISCIII) and European Regional Development Fund (ERDF). EG has received funding from the Innovative Medicines Initiative 2 (IMI2) Joint Undertaking under grant agreement No 116030 (TransQST), which is supported by the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA).Peer ReviewedPostprint (published version

2023 Medical Student Research Day Abstracts

Medical student research day is designed to highlight the breadth of research and scholarly activity that medical students have accomplished during their education at The GW School of Medicine and Health Sciences. All medical students are invited to present research regardless of the area of focus. Abstract submissions represent a broad range of research interests and disciplines, including basic and translational science, clinical research, health policy and public health research, and education-related research

Burden of allergic disease among ethnic minority groups in high income countries

The COVID-19 pandemic raised acute awareness regarding inequities and inequalities and poor clinical outcomes amongst ethnic minority groups. Studies carried out in North America, the UK and Australia have shown a relatively high burden of asthma and allergies amongst ethnic minority groups. The precise reasons underpinning the high disease burden are not well understood, but it is likely that this involves complex gene–environment interaction, behavioural and cultural elements. Poor clinical outcomes have been related to multiple factors including access to health care, engagement with healthcare professionals and concordance with advice which are affected by deprivation, literacy, cultural norms and health beliefs. It is unclear at present if allergic conditions are intrinsically more severe amongst patients from ethnic minority groups. Most evidence shaping our understanding of disease pathogenesis and clinical management is biased towards data generated from white population resident in high-income countries. In conjunction with standards of care, it is prudent that a multi-pronged approach towards provision of composite, culturally tailored, supportive interventions targeting demographic variables at the individual level is needed, but this requires further research and validation. In this narrative review, we provide an overview of epidemiology, sensitization patterns, poor clinical outcomes and possible factors underpinning these observations and highlight priority areas for research

Smoking assessment and work ability trends in asthma patients – prospective and retrospective study approach

Smoking increases the risk of asthma and impairs the prognosis of the disease and therapeutic response. Smoking cessation is an essential part of the treatment of asthma. The comprehensive treatment of asthma is also important for the patient’s work ability. The prevalence of asthma has grown, and an increasing number of workers have to cope with the disease in their working lives. The present study aimed to evaluate how reliably asthmatics reported their smoking status and the changes in smoking habits over the last 15 years. We investigated how actively physicians discuss and document patient’s smoking status. The study also examined the development of the work ability score (WAS) in asthma patients to find risk factors for poor development of WAS. This study included two cohorts. The Finnish obstructive airway disease (CAD) cohort included 1,329 asthma patients and 959 chronic obstructive pulmonary disease patients. Their smoking habits, work ability, and general health were followed by questionnaires during 10-years. The register-based cohort included 35,650 patients, whose electronic health records (EHR) were analysed with a combination of rule-and deep learning (ULMFiT)-based algorithms. Only 6% of asthmatics had unreliability in the self-reported smoking data. Pack years can be considered only a rough estimate of the comprehensive consumption of tobacco products. Based on the algorithmic analysis, 61% of asthma patients had documented smoking status, and 55% of current smokers had discussed smoking cessation with the clinician during the two-year follow-up. In the future, smoking cessation care should be activated in hospitals. The performance of the ULMFiT-based classifier was good and showed that deep-learning-based models can create efficient tools for utilising the Finnish EHR. Over 90% of the patients’ WAS remained stable throughout the 10-year study period, but 8% of the patients who had more severe asthma, higher BMI, and multiple comorbidities showed significantly poorer outcomes. To support asthma patients’ work ability, comprehensive treatment of asthma and comorbidities, regular controls, and weight management are needed.Tupakoinnin arviointi ja työkyvyn trendit astmapotilailla – prospektiivinen ja retrospektiivinen lähestymistapa Tupakointi lisää astmariskiä, heikentää sairauden ennustetta ja terapeuttista vastetta. Tupakoinnin lopettaminen on tärkeä osa astman hoitoa. Astman kokonaisvaltainen hoito on oleellista myös potilaan työkyvyn kannalta. Astman esiintyvyys on kasvanut ja yhä useamman täytyy selviytyä sairauden kanssa työelämässä. Tutkimuksen tavoitteena oli selvittää kuinka luotettavasti astmaatikot raportoivat tupakointitietojaan ja mitkä ovat tupakointitottumusten muutokset viimeisten 15 v aikana. Tutkimme myös kuinka aktiivisesti lääkärit keskustelevat tupakoinnista ja dokumentoivat potilaan tupakointistatuksen sairaskertomukseen. Lisäksi tavoitteena oli tutkia työkykypisteiden (WAS) kehitystä astmapotilailla, jotta löydettäisiin riskitekijöitä työkyvyn heikolle kehitykselle. Tutkimus sisälsi kaksi kohorttia. Astman ja keuhkoahtaumataudin yksilöllinen hoito -tutkimuskohortti (AST) koostui 1329 astma- ja 959 keuhkoahtauma-tautipotilaasta. Heidän tupakointitapojaan, työkykyään ja yleistä terveyttään seurattiin 10 vuoden ajan kyselylomakkeiden avulla. Rekisteripohjainen kohortti koostui 35 650 aikuispotilaasta, joiden sairauskertomustekstejä analysoitiin sääntöpohjaisten ja syväoppimiseen (ULMFiT) perustuvien algoritmien avulla. Vain 6%:lla astmapotilaista itseraportoidut tupakkatiedot olivat epäluotettavia. Askivuosia voidaan käyttää vain karkeana arviona tupakointitaakasta. Algoritmisten analyysien pohjalta 61%:lla astmapotilaista oli tupakointistatus merkittynä sairauskertomukseen ja 55% nykyisistä tupakoitsijoista oli keskustellut lopetta-misesta lääkärin kanssa. Tulevaisuudessa tupakka- ja nikotiiniriippuvuuden hoitoa tulee aktivoida sairaaloissa. ULMFiT:iin perustuvan tupakointiluokittelijan toimivuus oli hyvä ja osoitti, että syväoppimiseen perustuvat mallit voivat luoda tehokkaita työkaluja suomalaisen sairauskertomuksen hyödyntämiseen. Yli 90%:lla potilaista työkykypistemäärä pysyi vakaana 10 vuoden seuranta-ajan, mutta 8%:lla potilaista, joilla oli vaikeampi astma ja enemmän oheissairauksia, tulokset olivat selkeästi heikommat. Astmapotilaiden työkyvyn tukemiseksi tarvitaan astman ja oheissairauksien kokonaisvaltaista hoitoa sekä ohjausta painonhallinnan

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science\u27s ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploratio

2023 IMSAloquium

Welcome to IMSAloquium 2023. This is IMSA’s 36 th year of leading in educationalinnovation, and the 35th year of the IMSA Student Inquiry and Research (SIR) Program.https://digitalcommons.imsa.edu/archives_sir/1033/thumbnail.jp

Clinical text data in machine learning: Systematic review

Background: Clinical narratives represent the main form of communication within healthcare providing a personalized account of patient history and assessments, offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective: The main aim of this study is to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigate the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods: Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multi-faceted interface, to perform a literature search against MEDLINE. We identified a total of 110 relevant studies and extracted information about the text data used to support machine learning, the NLP tasks supported and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation and any relevant statistics. Results: The vast majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable due to sensitive nature of data considered. Beside the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The vast majority of studies focused on the task of text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management and surveillance. Conclusions: We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which does not require data annotation