Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson's Disease Patient

Parkinson’s disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson’s patients to be 21,482,withanadditional29,695 burden to society. Due to the high stakes and rapidly rising Parkinson’s patients’ count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient’s conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson’s over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson’s progression

Application of deep learning techniques for biomedical data analysis

Deep learning and machine learning methods have been used for addressing the problems in the biomedical applications, such as diabetic retinopathy assessment and Parkinson's disease diagnosis. The severity of diabetic retinopathy is estimated by the expert's examination of fundus images based on the amount and location of three diabetic retinopathy signs (i.e., exudates, hemorrhages, and microaneurysms). An automatic and accurate system for detection of these signs can significantly help clinicians to make the best possible prognosis can result in reducing the risk of vision loss. For Parkinson's disease diagnosis, analysis of a speech voice is considered as the earliest symptom with the advantage of being non-intrusive and suitable for online applications. While some reported outcomes of the developed techniques have shown the good results and ongoing progress for these two applications, designing new algorithms is a thriving research field to overcome the poor sensitivity and specificity of the outcomes as well as the limitations such as dataset size and heuristic selection of the network parameters. This thesis has comprehensively studied and developed various deep learning frameworks for detection of diabetic retinopathy signs and diagnosis of Parkinson's disease. To improve the performance of the current systems, this work has had an investigation on different techniques: (i) color space investigation, (ii) examination of various deep learning methods, (iii) development of suitable pre/post-processing algorithms and (iv) appropriate selection of deep learning architectures and parameters. For diabetic retinopathy assessment, this thesis has proposed the new color space as the input for the deep learning models that obtained better replicability compared with the conventional color spaces. This has also shown the pre-trained model can extract more relevant features compared to the models which were trained from scratch. This has also presented a deep learning framework combined with the suitable pre and post-processing algorithms that increased the performance of the system. By investigation different architectures and parameters, the suitable deep learning model has been presented to distinguish between Parkinson's disease and healthy speech signal

Clinical drug development in Parkinson’s disease : descriptive and exploratory analysis of success and failure pathways

Tese de mestrado, Neurociências, Universidade de Lisboa, Faculdade de Medicina, 2021INTRODUÇÃO: A marca patológica da Doença de Parkinson (DP) é a perda de neurónios dopaminérgicos, predominantemente nos gânglios de base, e a agregação de proteínas dos corpos de Lewys (LBs) nas células nervosas restantes. O início do desenvolvimento clínico de fármacos na DP começou com uma formulação oral de levodopa em 1967. Desde então, os ensaios clínicos foram essenciais para entender quão seguros são os tratamentos e para trazer novos compostos ao mercado com efeito terapêutico comprovado. Biofarmacêuticas em todo o mundo desenvolvem tratamentos inovadores para cobrir necessidades médicas ainda não exploradas. Contudo, e embora os numerosos avanços no desenvolvimento de fármacos nos últimos 20 anos, ensaios clínicos ainda não produziram um número significativo de medicamentos novos e seguros na DP. Efetivamente, menos de 10% dos medicamentos testados foram aprovados pelas agências reguladoras. É então importante identificar fatores que contribuam para uma trajetória de licenciamento de sucesso. Nas últimas décadas tem sido feito um esforço rigoroso em várias áreas como a genética, bioquímica, epigenética, ómica, clínica e imagem para definir marcadores fiáveis para melhorar a qualidade dos ensaios clínicos. Será que marcadores farmacológicos podem melhorar as abordagens para restaurar a dopamina no núcleo estriado de forma direcionada ou para prevenir a neurodegeneração e progressão da DP? OBJETIVOS: O principal objetivo desta tese é a descrição dos programas de desenvolvimento terapêutico na DP e a avaliação crítica das causas de atrito dos compostos nas fases distintas do desenvolvimento do fármaco, i.e., desde a fase 1 até à fase 4 dos estudos da DP. Quais as razões para o insucesso no desenvolvimento de fármacos na DP ser elevado? Quais as razões do decréscimo do número de compostos licenciados nas últimas duas décadas? O insucesso no desenvolvimento de drogas é observado em todas as fases de desenvolvimento, incluindo nas fases tardias? Estará o insucesso relacionado com ensaios iniciais não informativos? Para responder a estas questões, o propósito desta tese centra-se em identificar as razões de sucesso no desenvolvimento de fármacos na DP. MÉTODOS: Esta tese investigou 1304 ensaios clínicos na DP. A metodologia compreendeu uma revisão de literatura, o desenho de parâmetros de seleção de ensaios, uma extração de dados, uma análise de dados, a criação do PDCard Database e a análise estatística. Para esse efeito, foram utilizadas informações de ensaios clínicos relativos à doença de Parkinson que estavam registadas em 3 plataformas: clinicaltrials.gov, World Health Organization (WHO) International Clinical Trials Search Portal (ICTRP) e Australian New Zealand Clinical Trials Registry (ANZCTR). Uma base de dados incorporada, chamada PDCard Database, foi obtida usando os critérios de extração e análise de dados, totalizando uma soma de 613 estudos de intervenção em G20 – Doença de Parkinson. Em seguida, foi realizada uma análise exploratória das vias de sucesso e insucesso, e foram calculadas as respetivas taxas para cada fase experimental. RESULTADOS: Os programas de desenvolvimento de medicamentos para a DP entre o ano 2000 e agosto de 2019 foram analisados e os dados publicados no banco de dados PDCard. Ao analisar o banco de dados, verifica-se que há uma alta frequência de insucesso no desenvolvimento de fármacos, bem como uma diminuição no número de compostos licenciados para DP nas últimas duas décadas. Para estudar essa tendência, as vias de sucesso e insucesso dos 613 ensaios clínicos e dos 187 compostos testados, totalizando 77496 participantes, são exploradas. As 50 variáveis foram (i) categorizadas, (ii) sistematizadas e (iii) organizadas, com base nas diretrizes do SPIRIT. Cada uma das variáveis foi adicionada manualmente e extraída da publicação original do ensaio clínico ou dos bancos de dados clinictrials.gov, WHO ICTRP e ANZCTR. Objetivamente, o banco de dados inclui 115 ensaios clínicos na fase 1, 23 ensaios clínicos na fase 1|2, 194 ensaios clínicos na fase 2, 19 ensaios clínicos na fase 2|3, 172 ensaios clínicos na fase 3 e 90 ensaios clínicos na fase 4. 42,90% eram estudos multicêntricos, enquanto apenas 13,38% são monocentrados. 74,55% dos estudos foram randomizados e 9,79% são estudos não randomizados. 67,86% são estudos concluídos, 11,91% são estudos em fase de recrutamento, 9,62% são estudos que não estão a recrutar, 8,16% são estudos terminados e 2,45% são estudos desqualificados. São apresentadas falhas no desenvolvimento de fármacos em todas as fases do programa de desenvolvimento, incluindo estágios tardios. Efetivamente, de todas as causas para terminar um ensaio clínico, na fase 1, o mais importante foi o primeiro milestone não ser atingido. Na fase 2, a falta de eficiência foi a principal causa. Na fase 2|3, o principal motivo foi que era improvável fornecer evidências de efeito significativo. Na Fase 3, a falta de eficácia foi o principal motivo para o término do estudo. Portanto, as falhas estão principalmente relacionadas aos estudos de fases precoces serem pouco informativos. A maioria dos estudos (79,93%) recrutou indivíduos adultos ou idosos. Apenas 10,77% dos estudos incluíram indivíduos saudáveis. A maioria das condições estudadas nos ensaios clínicos são doenças do sistema nervoso (IV), relacionadas com a condição DP, totalizando 265 estudos. Os outros 264 ensaios clínicos estudaram a eficácia e a segurança do próprio medicamento, sem especificar uma condição relacionada com a DP. A maioria dos ensaios é projetado para fins de tratamento. A eficácia é o primary gold predominante em todas as fases do desenvolvimento na população estudada. Como o principal objetivo desta tese foi identificar as razões para o sucesso ou insucesso no programa de desenvolvimento do fármaco, dois tipos de caminhos foram comparados: a taxa de sucesso vs. a taxa de insucesso. A taxa de sucesso é mais alta na vigilância e monitorização pós-mercado, i.e. em compostos licenciados em fase 4, e é mínima na transição do active pharmaceutical ingredient (API) da fase 2 e 1|2 para a fase 3, i.e. antes das provas regulatórias. Assim sendo, 613 ensaios clínicos em DP realizados de 1998 a 2019 e 187 compostos foram amplamente estudados. Destes 187 compostos, apenas 29 passaram os ensaios de confirmação e foram finalmente considerados caminhos de sucesso, que permitiram o licenciamento do fármaco. Boehringer Ingelheim, Sandoz e GlaxoSmithKline foram as grandes empresas farmacêuticas que conduziram mais estudos completando a totalidade do programa de desenvolvimento do fármaco, incluindo a vigilância e monitorização pós-mercado de moléculas em fase 4. De todos os 29 compostos testados, 12 foram considerados novas entidades moleculares. As novas entidades moleculares mais estudadas foram a rasagilina (30,77%), seguida pela rotigotina, ropinirol e pramipexol. De todos os 29 compostos aprovados, 75,44% foram testados numa amostra de indivíduos em todas as fases da DP. Os ensaios clínicos de quadrupla ocultação (47,22%) são os que produziram mais compostos licenciados por ensaios confirmatórios, seguidos pelos de ocultação dupla (38,89%). Os estudos paralelos (59,65%) foram o tipo de desenho que produziu mais compostos aprovados, seguidos pelos estudos de grupo único (26,32%) e pelos estudos cruzados (12,28%). Os estudos (76,79%) que utilizaram a via oral como via preferencial de administração e comprimidos como formulação preferida foram os que tiveram mais sucesso nas fases confirmatórias do programa de desenvolvimento do fármaco. Os moduladores dos recetores de dopamina, assim como as formulações puras de levodopa e as combinações de levodopa, representam 52,63% das vias de sucesso nos últimos 20 anos. Seguidamente, os moduladores da monoamina oxidase (19,30%), como a selegilina e a rasagilina, foram os compostos testados com mais sucesso. Em terceiro lugar, os moduladores de recetores adrenérgicos (5,26%), como a droxidopa e o mirabegron, e os moduladores da catecol o-metiltransferase (5,26%), como o entacapone e o opicapone, foram os compostos aprovados pelas autoridades reguladoras com mais sucesso. Dos 29 compostos licenciados e comercializados que passaram nos ensaios confirmatórios, 26,32% dos estudos que foram realizados foram para medicamentos não parkinsonianos, enquanto 73,68% dos estudos que foram realizados foram para agentes antiparkinsonianos. Nos últimos 20 anos, e resumindo as vias de sucesso no programa de desenvolvimento, 15 agentes antiparkinsonianos passaram primariamente na fase 3 e, portanto, completaram as provas regulatórias. Após o processo e aprovação do New Drug Application, apenas 14 APIs foram registados para a DP e, subsequentemente, concluíram com êxito os estudos de vigilância e monitorização pós mercado. Os agentes antiparkinsonianos bem-sucedidos são a amantadina, a apomorfina, a carbidopa/levodopa, a levodopa, a levodopa/carbidopa/entacapona, a entacapona, a opicapona, o pramipexol, a rasagilina, a rivastigmina, o ropinirol, a rotigotina, a safinamida e a selegilina. Esses 14 agentes antiparkinsonianos diferentes foram comercializados por 16 promotores diferentes e em 20 formulações distintas. A Novartis foi a farmacêutica que licenciou mais compostos. Contrastando com os caminhos de sucesso, os caminhos de insucesso que forçaram a suspensão do ensaio antes da fase final do programa de desenvolvimento também são descritos. Nos últimos 20 anos, os motivos foram principalmente devido à falta de eficácia, segurança e financiamento. Trinta e cinco compostos falharam na fase 3 e seis compostos falharam na fase 4. A maioria dos estudos que falharam a completa aprovação após os estudos de vigilância e monitorização pós-mercado (34%), não publicam os motivos da interrupção ou retirada do ensaio clínico. CONCLUSÃO: A tese revisou o estado de arte do desenvolvimento clínico de fármacos na doença de Parkinson entre 1998 e 2019. A problemática de explorar as causas do sucesso e insucesso é um tópico de tendência na neurociência. Fundamentalmente, o trabalho desta dissertação respondeu aos objetivos inicialmente definidos. Espera-se, então, que este trabalho leve a novas e interessantes questões, que possam justificar uma análise multivariada das 50 variáveis obtidas. O panorama dos programas de desenvolvimento terapêutico da DP foi investigado e as causas de atrito dos compostos nos distintos estágios do desenvolvimento farmacológico, da fase 1 à fase 4 nos estudos da doença de Parkinson, foram avaliadas criticamente. Com efeito, há uma diminuição do sucesso no desenvolvimento de fármacos e do número de compostos licenciados nos últimos 20 anos. Além disso, falhas no desenvolvimento de medicamentos são observadas em todas as fases do desenvolvimento, incluindo em fases mais tardias, que estão maioritariamente relacionadas a ensaios clínicos não informativos principalmente em estágios iniciais. Duas vias foram comparadas: (i) uma via de sucesso, completando as provas regulatórias e ensaios confirmatórios, com uma taxa de sucesso de 16%, que permitiu o licenciamento e a vigilância e monitorização pós-mercado de 29 medicamentos; e (ii) uma via de insucesso com uma taxa de insucesso de 84%, que levou à suspensão de 158 compostos antes da fase final do programa de desenvolvimento. Finalmente, dos 613 ensaios clínicos em DP realizados entre 1998 e agosto de 2019 e dos 187 compostos, apenas 14 APIs foram finalmente aprovados e comercializados como agentes antiparkinsonianos. A taxa de sucesso específica para agentes antiparkinsonianos é igual a 7% e a taxa de insucesso específica de agentes antiparkinsonianos é igual a 93%.INTRODUCTION: The pathological hallmark of Parkinson’s Disease (PD) is the loss of dopaminergic neurons, most prominently in certain parts of the basal ganglia, and the aggregation of Lewy bodies proteins in remaining nerve cells. The beginning of clinical drug development in PD started with an orally formulation of levodopa in 1967. Since then, clinical trials were essential to understand how safe the treatments are and to bring new compounds to the market with a proven therapeutic effect. Biopharmaceutical companies around the world attempted to develop innovative therapies to achieve unmet medical necessities. Although the numerous advances on drug development in PD, a significant number of clinical trials still fail to produce new and safe medicines. Indeed, less than 10% of the drugs have been approved by regulatory agencies. It is then important to identify factors that contribute to a successful licensing path in regard to PD. Rigorous efforts have been made in the last decades in various areas such as genetic, biochemistry, epigenetic, omics, clinic and imaging to define reliable markers to improve the quality of clinical trials. Can drug related markers improve the pharmacologic approaches for restoring striatal dopamine in a targeted and physiological manner or to prevent ongoing neurodegeneration and progression of disease? OBJECTIVES: The principal aim of this thesis is the description of the landscape of PD therapeutic development programs and critically appraise the causes of compound attrition in the distinct stages of drug development, from phase 1 to phase 4 in Parkinson Disease’s studies. Why is it elevated the frequency of drug development failures? Why have it been a decrease in the number of approved compounds in the last two decades? Are failures in drug development observed in all phases of drug development including late stages? Are failures related with non-informative early stage trials? To answer these questions, the outcome of this thesis is centered in identifying the reasons for development drug success. METHODS: This thesis researched 1304 PD clinical trials. The methodology comprehended a literature research, the design of trial selection parameters, the data extraction, the data analysis, the creation of the PDCard Database and the statistical analysis. For that matter, this study used information from clinical trials relating to Parkinson's disease that were recorded on the 3 platforms: clinicaltrials.gov, World Health Organization (WHO) International Clinical Trials Search Portal (ICTRP) and Australian New Zealand Clinical Trials Registry (ANZCTR). An embedded type database, named PDCard Database, was finally obtained using criteria the data extraction and analysis, totaling a sum of 613 interventional studies of G20 – Parkinson’s disease. Then an exploratory analysis of success and failure paths was conducted, and success and failure rates for each trial phase were calculated. RESULTS: PD drug development registry between 2000 and August 2019 was analyzed, and data published in the PDCard Database. By analyzing the database, it is shown that there is a high frequency of drug development failures and there is a decrease in the number of licensed compounds in the last two decades. For that matter, success and failure paths of the 613 clinical trials and 187 tested compounds, in 77496 total participants, are explored. Fifty variables were (i) categorized (ii) systemized and (iii) organized, based on the SPIRIT guidelines. Each of the variables was added manually and extracted from the original publication of the clinical trial or the clinicaltrials.gov, WHO ICTRP and ANZCTZ. Objectively, the database includes 115 clinical trials in phase 1, 23 clinical trials in phase 1|2, 194 clinical trials in phase 2, 19 clinical trials in phase 2|3, 172 clinical trials in phase 3 and 90 clinical trials in phase 4. 42,90% were multicentered studies, while only 13,38% are monocentered. 74.55% of the studies were randomized and 9,79% are non-randomized studies. 67,86% are completed studies, 11,91% are studies in recruiting phase, 9,62% are studies that are not recruiting, 8,16% are terminated studies and 2,45% are withdrawn studies. It is presented that failures in drug development are observed in all phases of drug development including late stages. Effectively, from all the terminated study causes of all studies, in Phase 1 the most important was first milestone was not met. In Phase 2, lack of efficiency was the primary cause. In Phase 2|3, the main reason was that it was unlikely to provide evidence of significant effect. In Phase 3, lack of efficacy was the main reason for terminating the study. Therefore, failures are mostly related with non informative early stage trials. The majority of the studies (79,93%) have recruited adult or older adult subjects. Only 10.77% of clinical trials studies included healthy subjects. Most of the diseases studied in clinical trials are diseases of the nervous system (IV), totaling 265 studies. Otherwise, 264 of the clinical trials were studying the efficacy and safety of the drug itself, without specifying PD as a condition to the study. The majority of the trials are designed for treatment purpose. Efficacy is the predominating gold at all phases of development in the study population. As the main outcome of this thesis was identifying the reasons for development drug success or failure, two types of paths were compared: the success rate vs the failure rate. The success rate is higher in PostMarketing Surveillance (PMS), i.e. in phase 4 licensed compounds, and is minimum in the transition of the API from phases 2 and 1|2 to phase 3, i.e. before regulatory proofing. Henceforth, 613 clinical trials in PD conducted from 1998 to 2019, and 187 compounds were vastly studied. From these 187 compounds, only 29 passed confirmatory trials and were finally considered successful paths, that allowed drug licensing. Boehringer Ingelheim, Sandoz and GlaxoSmithKline were the big pharma that conducted more studies completing the full drug development program, including postmarketing surveillance of molecules in phase 4. From all the 29 compounds that were tested, 12 were considered new molecular entities. The new molecular entities that were more studied were rasagiline (30,77%), followed by rotigotine, ropinirole and pramipexole. From all the successful 29 approved compounds, 75,44% were tested in a sample of subjects with all stages of PD. Clinical trials that used quadruple blinding (47,22%) are the ones that produced more licensed compounds that passed confirmatory trials, followed by double blinding (38,89%). The parallel assignment (59,65%) was the type of design that produced more approved compounds, followed by single group assignment (26,32%) and crossover assignment (12,28%). The studies (76,79%) that used oral as a preferred route of administration, and tablets as a preferred drug formulation are the ones that approved more compounds passing confirmatory trials. Dopamine receptor modulators, like pure levodopa and levodopa combinations, represent 52,63% of the success path in the last 20 years. In second, monoamine oxidase modulators (19,30%), like selegiline and rasagiline, were tested in PD drug development. Thirdly, adrenergic receptor modulators (5,26%), like droxidopa and mirabegron, and catechol o-methyltransferase modulators (5,26%), like entacapone and opicapone, were approved compounds that passed confirmatory trials by the regulatory authorities. From the 29 licensed compounds that passed confirmatory trials, 26,32% of the studies conducted were for non-parkinsonians drugs, while 73,68% of the studies conducted were for antiparkinsonian agents. In the past 20 years, and reviewing the successful path, 15 antiparkinsonian agents primarily passed phase 3 and thus showed regulatory proof. After NDA process and approval, only 14 APIs were registered for PD and, subsequently, completed successfully the postmarketing surveillance studies. The successful antiparkinsonian agents are amantadine, apomorphine, carbidopa/levodopa, levodopa, levodopa/carbidopa/entacapone, entacapone, opicapone, pramipexole, rasagiline, rivastigmine, ropinirole, rotigotine, safinamide and selegiline. Those 14 different antiparkinsonian agents were marketed by 16 different sponsors, in 20 different formulations. Novartis was the big pharma licensing more compounds. Contrasting the success paths, the failure paths that forced the suspension of trial before the final phase of the development program are also described. In the last 20 years, the reasons were mainly due to efficacy, safety and financing. Thirty-five compounds failed to succeed phase 3, and six compounds failed to succeed phase 4. The majority of studies that failed to be approved (34%) do not publish the reasons for terminating or withdrawing the clinical trial. CONCLUSION: The thesis reviewed the state of the art of clinical drug development in Parkinson’s Disease from 1998 to 2019. The problematic of exploring the causes for success/unsuccess is a trending topic in neuroscience. Fundamentally, the work of this dissertation responded to the initially defined objectives. It is then expected that this work will lead to new and interesting questions, which might justify a future multivariate analysis of the 50 obtained variables. The landscape of PD therapeutic development programs was probed and the causes of compound attrition in the distinct stages of drug development, from phase 1 to phase 4 in Parkinson Disease’s studies were critically appraised. Indeed, there is a decrease on the success of drug development and in the number of approved compounds that passed confirmatory trials in the last 20 years. Moreover, failures in drug development are observed in all phases of development including late stages, but are mostly related with non-informative early stage trials. Two paths were compared: (i) a successful pathway, passing regulatory proof and confirmatory trials, with a success rate of 16%, that

Unsupervised learning methods for identifying and evaluating disease clusters in electronic health records

Introduction Clustering algorithms are a class of algorithms that can discover groups of observations in complex data and are often used to identify subtypes of heterogeneous diseases in electronic health records (EHR). Evaluating clustering experiments for biological and clinical significance is a vital but challenging task due to the lack of consensus on best practices. As a result, the translation of findings from clustering experiments to clinical practice is limited. Aim The aim of this thesis was to investigate and evaluate approaches that enable the evaluation of clustering experiments using EHR. Methods We conducted a scoping review of clustering studies in EHR to identify common evaluation approaches. We systematically investigated the performance of the identified approaches using a cohort of Alzheimer's Disease (AD) patients as an exemplar comparing four different clustering methods (K-means, Kernel K-means, Affinity Propagation and Latent Class Analysis.). Using the same population, we developed and evaluated a method (MCHAMMER) that tested whether clusterable structures exist in EHR. To develop this method we tested several cluster validation indexes and methods of generating null data to see which are the best at discovering clusters. In order to enable the robust benchmarking of evaluation approaches, we created a tool that generated synthetic EHR data that contain known cluster labels across a range of clustering scenarios. Results Across 67 EHR clustering studies, the most popular internal evaluation metric was comparing cluster results across multiple algorithms (30% of studies). We examined this approach conducting a clustering experiment on AD patients using a population of 10,065 AD patients and 21 demographic, symptom and comorbidity features. K-means found 5 clusters, Kernel K means found 2 clusters, Affinity propagation found 5 and latent class analysis found 6. K-means 4 was found to have the best clustering solution with the highest silhouette score (0.19) and was more predictive of outcomes. The five clusters found were: typical AD (n=2026), non-typical AD (n=1640), cardiovascular disease cluster (n=686), a cancer cluster (n=1710) and a cluster of mental health issues, smoking and early disease onset (n=1528), which has been found in previous research as well as in the results of other clustering methods. We created a synthetic data generation tool which allows for the generation of realistic EHR clusters that can vary in separation and number of noise variables to alter the difficulty of the clustering problem. We found that decreasing cluster separation did increase cluster difficulty significantly whereas noise variables increased cluster difficulty but not significantly. To develop the tool to assess clusters existence we tested different methods of null dataset generation and cluster validation indices, the best performing null dataset method was the min max method and the best performing indices we Calinksi Harabasz index which had an accuracy of 94%, Davies Bouldin index (97%) silhouette score ( 93%) and BWC index (90%). We further found that when clusters were identified using the Calinski Harabasz index they were more likely to have significantly different outcomes between clusters. Lastly we repeated the initial clustering experiment, comparing 10 different pre-processing methods. The three best performing methods were RBF kernel (2 clusters), MCA (4 clusters) and MCA and PCA (6 clusters). The MCA approach gave the best results highest silhouette score (0.23) and meaningful clusters, producing 4 clusters; heart and circulatory( n=1379), early onset mental health (n=1761), male cluster with memory loss (n = 1823), female with more problem (n=2244). Conclusion We have developed and tested a series of methods and tools to enable the evaluation of EHR clustering experiments. We developed and proposed a novel cluster evaluation metric and provided a tool for benchmarking evaluation approaches in synthetic but realistic EHR

Student Research Colloquium Proceedings 2013

2013 Student Research Colloquium proceedings include the following: a schedule of the day\u27s events, acknowledgement of research sponsors, conference presentation abstracts, formal paper competition participants; poster presentation competition participants; student presenter index, research sponsor index, poster and paper presentation judges, sponsors, and donors, map of Atwood Memorial Center

Utilisation de l’intelligence artificielle pour identifier les marqueurs de la démence dans le trouble comportemental en sommeil paradoxal

La démence à corps de Lewy (DCL) et la maladie de Parkinson (MP) sont des maladies neurodégénératives touchant des milliers de Canadiens et leur prévalence croît avec l’âge. La MP et la DCL partagent la même pathophysiologie, mais se distinguent par l’ordre de manifestation des symptômes : la DCL se caractérise d’abord par l’apparition d’un trouble neurocognitif majeur (démence), tandis que la MP se manifeste initialement par un parkinsonisme. De plus, jusqu’à 80% des patients avec la MP développeront une démence (MPD). Il est désormais établi que le trouble comportemental en sommeil paradoxal idiopathique (TCSPi) constitue un puissant prédicteur de la DCL et la MP. En effet, cette parasomnie, marquée par des comportements indésirables durant le sommeil, est considérée comme un stade prodromal des synucléinopathies, telles que la MP, la DCL et l'atrophie multisystémique (AMS). Ainsi, la majorité des patients atteints d’un TCSPi développeront une synucléinopathie. Malgré les avancées scientifiques, les causes du TCSPi, de la MP et de la DCL demeurent inconnues et aucun traitement ne parvient à freiner ou à arrêter la neurodégénérescence. De plus, ces pathologies présentent une grande hétérogénéité dans l’apparition et la progression des divers symptômes. Face à ces défis, la recherche vise à mieux cerner les phases précoces/initiales et les trajectoires évolutives de ces maladies neurodégénératives afin d’intervenir le plus précocement possible dans leur développement. C’est pourquoi le TCSPi suscite un intérêt majeur en tant que fenêtre d'opportunités pour tester l’efficacité des thérapies neuroprotectrices contre les synucléinopathies, permettant d'agir avant que la perte neuronale ne devienne irréversible. Le TCSPi offre ainsi une occasion unique d'améliorer la détection de la démence et le suivi des individus à haut risque de déclin cognitif. D'où l'importance cruciale de pouvoir généraliser les résultats issus de la recherche sur de petites cohortes à l'ensemble de la population. Sur le plan de la cognition, les études longitudinales sur le TCSPi ont montré que les atteintes des fonctions exécutives, de la mémoire verbale et de l'attention sont les plus discriminantes pour différencier les individus qui développeront une démence de ceux qui resteront idiopathiques. De plus, un grand nombre de patients TCSPi souffrent d’un trouble neurocognitif mineur ou trouble cognitif léger (TCL), généralement considéré comme un stade précurseur de la démence. Les recherches actuelles sur les données cognitives chez cette population offrent des perspectives prometteuses, mais reposent sur des approches statistiques classiques qui limitent leur validation et généralisation. Bien qu'elles offrent une précision élevée (80 à 85%) pour détecter les patients à risque de déclin cognitif, une amélioration est nécessaire pour étendre l'utilisation de ces marqueurs à une plus large échelle. Depuis les années 2000, l'accroissement de la puissance de calcul et l'accès à davantage de ressources de mémoire ont suscité un intérêt accru pour les algorithmes d'apprentissage machine (AM). Ces derniers visent à généraliser les résultats à une population plus vaste en entraînant des modèles sur une partie des données et en les testant sur une autre, validant ainsi leur application clinique. Jusqu'à présent, aucune étude n'a évalué les apports de l'AM pour la prédiction de l'évolution des synucléinopathies en se penchant sur le potentiel de généralisation, et donc d'application clinique, à travers l'usage d'outils non invasifs et accessibles ainsi que de techniques de validation de modèles (model validation). De plus, aucune étude n'a exploré l'utilisation de l'AM associée à des méthodes de généralisation sur des données neuropsychologiques longitudinales pour élaborer un modèle prédictif de la progression des déficits cognitifs dans le TCSPi. L’objectif général de cette thèse est d’étudier l’apport de l’AM pour analyser l’évolution du profil cognitif de patients atteints d’un TCSPi. Le premier chapitre de cette thèse présente le cadre théorique qui a guidé l’élaboration des objectifs et hypothèses de recherche. Le deuxième chapitre est à deux volets (articles). Le premier vise à fournir une vue d'ensemble de la littérature des études ayant utilisé l'AM (avec des méthodes de généralisation) pour prédire l'évolution des synucléinopathies vers une démence, ainsi que les lacunes à combler. Le deuxième volet vise à explorer et utiliser pour la première fois l'AM sur des données cliniques et cognitifs pour prédire la progression vers la démence dans le TCSPi, dans un devis longitudinal. Enfin, le dernier chapitre de la thèse présente une discussion et une conclusion générale, comprenant un résumé des deux articles, ainsi que les implications théoriques, les forces, les limites et les orientations futures.Lewy body dementia (LBD) and Parkinson's disease (PD) are neurodegenerative diseases affecting thousands of Canadians, and their prevalence increases with age. PD and DLB share the same pathophysiology, but differ in the order of symptom manifestation: DLB is characterized first by the onset of a major neurocognitive disorder (dementia), whereas PD initially manifests as parkinsonism. Moreover, up to 80% of PD patients will go on to develop dementia (PDD). It is established that idiopathic REM sleep behavior disorder (iRBD) is a powerful predictor of DLB and PD. Indeed, this parasomnia, marked by undesirable behaviors during sleep, is considered a prodromal stage of synucleinopathies, such as PD, DLB and multisystem atrophy (MSA). Therefore, the majority of patients with iRBD will develop synucleinopathy. Despite scientific advancements, the causes of iRBD, PD, and DLB remain unknown and no treatment has been able to slow or halt neurodegeneration. Furthermore, these pathologies display great heterogeneity in the onset and progression of various symptoms. Faced with these challenges, research aims to better understand the early/initial stages and the progressive trajectories of these neurodegenerative diseases in order to intervene as early as possible in their development. This is why iRBD garners major interest as a window of opportunities to test the effectiveness of neuroprotective therapies against synucleinopathies, enabling action to be taken before neuronal loss becomes irreversible. iRBD thus provides a unique opportunity to improve dementia detection and monitoring of individuals at high risk of cognitive decline. Hence the crucial importance of being able to generalize results of research on small cohorts to the entire population. In terms of cognition, longitudinal studies on iRBD have shown that impairments in executive functions, verbal memory, and attention are the most discriminating in differencing between individuals who will develop dementia from those who will remain idiopathic. In addition, many iRBD patients suffer from a mild neurocognitive disorder or mild cognitive impairment (MCI), generally considered as a precursor stage of dementia. Current research on cognitive data in this population offers promising prospects, but relies on traditional statistical approaches that limit their validation and generalizability. While they provide high accuracy (80 to 85%) for detecting patients at risk of cognitive decline, improvement is needed to extend the use of these markers to a larger scale. Since the 2000s, increased computational power and access to more memory resources have sparked growing interest in machine learning (ML) algorithms. These aim to generalize results to a broader population by training models on a subset of data and testing them on another, thus validating their clinical application. To date, no study has assessed the contributions of ML for predicting the progression of synucleinopathies, focusing on the potential for generalization, and hence clinical application, through the use of non-invasive, accessible tools and model validation techniques. Moreover, no study has explored the use of ML in conjunction with generalization methods on longitudinal neuropsychological data to develop a predictive model of cognitive deficit progression in iRBD. The general objective of this thesis is to study the contribution of ML in analyzing the evolution of the cognitive profile of patients with iRBD. The first chapter of this thesis presents the theoretical framework that guided the formulation of the research objectives and hypotheses. The second chapter is in two parts (articles). The first aims to provide an overview of the literature of studies that have used ML (with generalization methods) to predict the progression of synucleinopathies to dementia, as well as the gaps that need to be filled. The second part aims to explore and use for the first time ML on clinical and cognitive data to predict progression to dementia in iRBD, in a longitudinal design. Finally, the last chapter of the thesis presents a discussion and a general conclusion, including a summary of the two articles, as well as theoretical implications, strengths, limitations, and future directions

The Stylometric Processing of Sensory Open Source Data

This research project’s end goal is on the Lone Wolf Terrorist. The project uses an exploratory approach to the self-radicalisation problem by creating a stylistic fingerprint of a person's personality, or self, from subtle characteristics hidden in a person's writing style. It separates the identity of one person from another based on their writing style. It also separates the writings of suicide attackers from ‘normal' bloggers by critical slowing down; a dynamical property used to develop early warning signs of tipping points. It identifies changes in a person's moods, or shifts from one state to another, that might indicate a tipping point for self-radicalisation. Research into authorship identity using personality is a relatively new area in the field of neurolinguistics. There are very few methods that model how an individual's cognitive functions present themselves in writing. Here, we develop a novel algorithm, RPAS, which draws on cognitive functions such as aging, sensory processing, abstract or concrete thinking through referential activity emotional experiences, and a person's internal gender for identity. We use well-known techniques such as Principal Component Analysis, Linear Discriminant Analysis, and the Vector Space Method to cluster multiple anonymous-authored works. Here we use a new approach, using seriation with noise to separate subtle features in individuals. We conduct time series analysis using modified variants of 1-lag autocorrelation and the coefficient of skewness, two statistical metrics that change near a tipping point, to track serious life events in an individual through cognitive linguistic markers. In our journey of discovery, we uncover secrets about the Elizabethan playwrights hidden for over 400 years. We uncover markers for depression and anxiety in modern-day writers and identify linguistic cues for Alzheimer's disease much earlier than other studies using sensory processing. In using these techniques on the Lone Wolf, we can separate their writing style used before their attacks that differs from other writing

Investigating the Potential of Postmortem Metabolomics in Mammalian Decomposition Studies in Outdoor Settings

By focusing on solely forensic studies, this dissertation gives an overview of three seemingly independent studies, which at a deeper level, reveal their strong interconnectivity through their forensic importance. The consistent global theme carried through all chapters circles around the application of metabolomics on biological specimens collected postmortem at an outdoor taphonomy facility in Knoxville, Tennessee, USA. The overall intention was to fill the knowledge gap around postmortem metabolomics while stressing its importance in bridging analytical chemistry and forensic science. Global postmortem metabolomics studies will contribute to the so-far conducted taphonomic groundwork by providing a better understanding of the fundamental processes of decomposition and ultimately build a more comprehensive postmortem biochemical database. The first chapter applies postmortem metabolomics to soils and human skeletal remains obtained from a multi-individual grave. The primary goals of this study were: (1) to obtain insights into the metabolite pulse released from buried remains into grave soils at different depths of a shallow burial and (2) to assess metabolic signatures of bones using an inter- and intraindividual analysis approach. Decomposition progresses differently below ground compared to the soil surface with impacts on rates and patterns of decomposition. In contrast to the first chapter, the second chapter faces an environmental change with a study design constructed around surface decomposition. Additionally, given that rates and patterns of decay seem to vary among species, a comprehensive omics approach including metabolomics and lipidomics was utilized to investigate species-specific metabolic signatures in soils from the cadaver decomposition island. The final chapter completes the aforementioned studies by investigating one of the most complex of all factors – intrinsic drivers of decomposition. We evaluated the trackability of drugs through several specimens such as serum, larvae, decomposition fluid, and soils from human donors. Furthermore, comprehensive postmortem metabolomics of the same specimens provided (a) matrix-specific metabolic signatures, (b) groups of metabolites potentially useful as decomposition biomarkers, and (c) an assessment of possible impacts of perimortem health conditions on the postmortem metabolome