Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Constraint based approaches to interpretable and semi-supervised machine learning

Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social media and governance. A related major challenge in deploying ML systems pertains to reliable learning when expert annotation is severely limited. This dissertation prescribes a common framework to address these challenges, based on the use of constraints that can make an ML model more interpretable, lead to novel methods for explaining ML models, or help to learn reliably with limited supervision. In particular, we focus on the class of latent variable models and develop a general learning framework by constraining realizations of latent variables and/or model parameters. We propose specific constraints that can be used to develop identifiable latent variable models, that in turn learn interpretable outcomes. The proposed framework is first used in Non–negative Matrix Factorization and Probabilistic Graphical Models. For both models, algorithms are proposed to incorporate such constraints with seamless and tractable augmentation of the associated learning and inference procedures. The utility of the proposed methods is demonstrated for our working application domain – identifiable phenotyping using Electronic Health Records (EHRs). Evaluation by domain experts reveals that the proposed models are indeed more clinically relevant (and hence more interpretable) than existing counterparts. The work also demonstrates that while there may be inherent trade–offs between constraining models to encourage interpretability, the quantitative performance of downstream tasks remains competitive. We then focus on constraint based mechanisms to explain decisions or outcomes of supervised black-box models. We propose an explanation model based on generating examples where the nature of the examples is constrained i.e. they have to be sampled from the underlying data domain. To do so, we train a generative model to characterize the data manifold in a high dimensional ambient space. Constrained sampling then allows us to generate naturalistic examples that lie along the data manifold. We propose ways to summarize model behavior using such constrained examples. In the last part of the contributions, we argue that heterogeneity of data sources is useful in situations where very little to no supervision is available. This thesis leverages such heterogeneity (via constraints) for two critical but widely different machine learning algorithms. In each case, a novel algorithm in the sub-class of co–regularization is developed to combine information from heterogeneous sources. Co–regularization is a framework of constraining latent variables and/or latent distributions in order to leverage heterogeneity. The proposed algorithms are utilized for clustering, where the intent is to generate a partition or grouping of observed samples, and for Learning to Rank algorithms – used to rank a set of observed samples in order of preference with respect to a specific search query. The proposed methods are evaluated on clustering web documents, social network users, and information retrieval applications for ranking search queries.Electrical and Computer Engineerin

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

The prognosis of oral epithelial dysplasia and oral squamous cell carcinoma in individuals with oral lichen planus: a single-centre observational study and a pioneer preliminary exploration of UK national Electronic Health Records

Head and neck squamous cell carcinoma (HNSCC) is a significant public health problem worldwide due to its high mortality and morbidity. A notable proportion of HNSCC, particularly oral squamous cell carcinoma (OSCC), is preceded by several long-standing chronic oral mucosal diseases including oral lichen planus (OLP). However, it remains largely unknown regarding the impact of a pre-existing OLP upon the prognosis and behaviour of OSCC and its precursor, oral epithelial dysplasia (OED). Therefore, this PhD thesis has sought to determine the influence of OLP on the long-term behaviour and prognosis of oral epithelial dysplasia (OED) and oral squamous cell carcinoma (OSCC) using data from a single UK tertiary care centre. Additionally, this thesis has made the first steps towards providing understanding of the epidemiology of HNSCC in a representative sample with common chronic oral mucosal conditions including oral lichen planus, oral submucous fibrosis, leukoplakia and periodontal diseases of the UK population. A retrospective cohort study of 299 patients with OED revealed that individuals with OED arising on a background of OLP appeared to be at higher risk of developing new primary OEDs up to 3 years (in the early years) after the first diagnosis of OED compared to those without OLP. However, the risk of malignant progression was similar between the two groups. This thesis built on these findings by investigating the impact of OLP in determining the long-term behaviour and prognosis of OSCC using a retrospective cohort study of 285 patients with OSCC. The results indicated that patients with OSCC-associated OLP were more likely to develop multiple and multifocal new primary dysplastic and OSCC events following their first oral malignancy. Despite this, there seems to be no significant association between OLP and mortality. In order to reveal more about the relationship between long-standing oral mucosal conditions and HNSCC using national-scale data, this thesis went beyond single data sources. This project provides a detailed method for appropriate data handling and curation of a linked national database of a UK population (the CALIBER platform). This allowed the development and validation of a reliable phenotype algorithm to identify patients with HNSCC from this data platform. Taken together, these findings advance understanding of the impact of OLP on the behaviour and prognosis of OED and OSCC. In addition, the HSNCC phenotype algorithm developed here represents an important step towards understanding the association between common chronic oral mucosal conditions and HNSCC in the UK

Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges

Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records etc. Making the best use of these diverse and strategic resources will lead to high quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less efforts have been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate the future potentials and research directions of applying advanced machine learning, such as deep learning, to dementia informatics

Intravascular Iodinated Contrast Media Administration in Adults: A Patient Safety Approach

In 2013, The ICPS Contrast Media Usage and Exposure Workgroup was formed to review, define, assess and implement best practices regarding the use of intravascular iodinated contrast media (CM) in diagnostic and interventional procedures with respect to the associated risk of contrast media - induced nephropathy (CIN) and other adverse events. The interdisciplinary workgroup consisted of radiologists, cardiologists, nephrologists, nurses, technologists, pharmacists and patient safety experts. The workgroup met regularly to review published best practices and current practices within each member health-system. The workgroup focused on opportunities to improve patient safety within rep resent health-systems and emphasized consensus-based recommendations aimed at reducing intra-institutional variability. Based on current literature, best practices, and professional experience, the workgroup created these recommendations for safe use of intravenous iodinated contrast media. These recommendations do not replace sound clinical judgment or other published guidelines

Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers

As health information technologies continue to advance, routine collection and digitisation of patient health records in the form of electronic health records present as an ideal opportunity for data-mining and exploratory analysis of biomarkers and risk factors indicative of a potentially diverse domain of patient outcomes. Patient records have continually become more widely available through various initiatives enabling open access whilst maintaining critical patient privacy. In spite of such progress, health records remain not widely adopted within the current clinical statistical analysis domain due to challenging issues derived from such “big data”.Deep learning based temporal modelling approaches present an ideal solution to health record challenges through automated self-optimisation of representation learning, able to man-ageably compose the high-dimensional domain of patient records into data representations able to model complex data associations. Such representations can serve to condense and reduce dimensionality to emphasise feature sparsity and importance through novel embedded feature selection approaches. Accordingly, application towards patient records enable complex mod-elling and analysis of the full domain of clinical features to select biomarkers of predictive relevance.Firstly, we propose a novel entropy regularised neural network ensemble able to highlight risk factors associated with hospitalisation risk of individuals with dementia. The application of which, was able to reduce a large domain of unique medical events to a small set of relevant risk factors able to maintain hospitalisation discrimination.Following on, we continue our work on ensemble architecture approaches with a novel cas-cading LSTM ensembles to predict severe sepsis onset within critical patients in an ICU critical care centre. We demonstrate state-of-the-art performance capabilities able to outperform that of current related literature.Finally, we propose a novel embedded feature selection application dubbed 1D convolu-tion feature selection using sparsity regularisation. Said methodology was evaluated on both domains of dementia and sepsis prediction objectives to highlight model capability and generalisability. We further report a selection of potential biomarkers for the aforementioned case study objectives highlighting clinical relevance and potential novelty value for future clinical analysis.Accordingly, we demonstrate the effective capability of embedded feature selection ap-proaches through the application of temporal based deep learning architectures in the discovery of effective biomarkers across a variety of challenging clinical applications

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