Towards Evidence Based M-Health Application Design in Cancer Patient Healthy Lifestyle Interventions

Cancer is one of the most prevalent diseases in Europe and the world. Significant correlations between dietary habits and cancer incidence and mortality have been confirmed by the literature. Physical activity habits are also directly implicated in the incidence of cancer. Lifestyle behaviour change may be benefited by using mobile technology to deliver health behaviour interventions. M-Health offers a promising cost-efficient approach to deliver en-masse interventions. Smartphone apps with constructs such as gamification and personalized have shown potential for helping individuals lose weight and maintain healthy lifestyle habits. However, evidence-based content and theory-based strategies have not been incorporated by those apps systematically yet. The aim of the current work is to put the foundations for a methodologically rigorous exploration of wellness/health intervention literature/app landscape towards detailed design specifications for connected health m-apps. In this context, both the overall work plan is described as well as the details for the significant steps of application space and literature space review. Both strategies for research and initial outcomes of it are presented. The expected evidence based design process for patient centered health and wellness interventions is going to be the primary input in the implementation process of upcoming patient centered health/wellness m-health interventions.ENJECT COST-STSM-ECOST-STSM-TD1405-220216-07045

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

Systematic associations between germ-line mutations and human cancers

YesThe revolution in Big Data has opened the gate for new research challenges in biomedical science. The aim of this study was to investigate whether germ-line gene mutations are a significant factor in 29 major primary human cancers. Using data obtained from multiple biological databases, we identified 424 genes from 8879 cancer mutation records. By integrating these gene mutation records a human cancer map was constructed from which several key results were obtained. These include the observations that missense/nonsense and regulatory mutations might play central role in connecting cancers/genes, and tend to be distributed in all chromosomes. This suggests that, of all mutation classes missense/nonsense and regulatory mutation classes are over-expressed in human genome and so are likely to have a significant impact on human cancer aetiology and pathomechanism. This offers new insights into how the distribution and interconnections of gene mutations influence the development of cancers

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

How to improve efficiency in cancer care: dimensions, methods, and areas of evaluation

Efficiency in healthcare is crucial since available resources are scarce, and the cost of inefficient allocation is measured in prior outcomes. This is particularly relevant for cancer. The aim of this paper is to gain a comprehensive overview of the areas and dimensions to improve efficiency, and establish the indicators, different methods, perspectives, and areas of evaluation, to provide recommendations for how to improve efficiency and measure gains in cancer care.Methods: We conducted a two-phase design. First, a comprehensive scoping literature review was conducted, searching four databases. Studies published between 2000 and 2021 were included if they described experiences and cases of efficiency in cancer care or methods to evaluate efficiency. The results of the literature review were then discussed during two rounds of online consultation with a panel of 15 external experts invited to provide insight and comments to deliberate policy recommendations.Results: 46 papers met the inclusion criteria. Based on the papers retrieved we identified six areas for achieving efficiency gains throughout the entire care pathway and, for each area of efficiency, we categorized the methods and outcomes used to measure efficiency gain.Conclusion: This is the first attempt to systemize a scattered body of literature on how to improve efficiency in cancer care and identify key areas of improvement. Policy summary: There are many opportunities to improve efficiency in cancer care. We defined seven policy recommendations on how to improve efficiency in cancer care throughout the care pathway and how to improve the measurement of efficiency gains

An Assessment of Health-Economic Burden of Obesity Trends with Population-Based Preventive Strategies in a Developed Economy

The burden of obesity varies with age, ethnicity, socio-economic status and state economies. All new projections should hence accommodate population ageing, and other population changes such as immigration, health-care system reform, or technological advances for disease treatment for a comprehensible assessment of global burden. The unfordable and expensive nature for reversing the obesity tide arises from policies developed to combat obesity. Most of these approaches aim at bringing the problem under control, rather than affecting a cure, and obviously require a multi-disciplinary and intensive regimen. Prevention is the only feasible option and is essential for all affected countries. Yet it is not simple to have population based UK-wide strategic framework for tackling obesity. Besides existence of multiple layers of governance, there are clear demarcations between targets in diet; nutrition and physical activity level between regions some of which are not realistic. Population based approaches target policies and process, aiming for a transition towards healthy population diets, activity levels and weight status. It is essential to understand these aspects differ culturally and between and within countries. There are still no clear and appropriate answers about answer when, where, why, and, how costs accrue in obese populations, further long term commitments are required for the same. Most population-based prevention policies are cost effective, largely paying for themselves through future health gains and resulting reductions in health expenditures. Therefore these prevention programs should be high on the scientific and political agendas

의학 연구에서의 과학적 증거의 활용을 위한 시각적 분석 시스템 디자인

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022. 8. 서진욱.Evidence-based medicine, "the conscientious, explicit, and judicious use of current best evidence in healthcare and medical research" [98], is one of the most widely accepted medical paradigms of modern times. Searching, reviewing, and synthesizing reliable and high-quality scientific evidence is the key step for the paradigm. However, despite the widespread use of the EBM paradigm, challenges remain in applying Evidence-based medicine protocols to medical research. One of the barriers to applying the best scientific evidence to medical research is the severe literature and clinical data overload that causes the evidence-based tasks to be tremendous time-consuming tasks that require vast human effort. In this dissertation, we aim to employ visual analytics approaches to address the challenges of searching and reviewing massive scientific evidence in medical research. To overcome the burden and facilitate handling scientific evidence in medical research, we conducted three design studies and implemented novel visual analytics systems for laborious evidence-based tasks. First, we designed PLOEM, a novel visual analytics system to aid evidence synthesis, an essential step in Evidence-Based medicine, and generate an Evidence Map in a standardized method. We conducted a case study with an oncologist with years of evidence-based medicine experience. In the second study, we conducted a preliminary survey with 76 medical doctors to derive the design requirements for a biomedical literature search. Based on the results, We designed EEEVis, an interactive visual analytic system for biomedical literature search tasks. The system enhances the PubMed search result with several bibliographic visualizations and PubTator annotations. We performed a user study to evaluate the designs with 24 medical doctors and presented the design guidelines and challenges for a biomedical literature search system design. The third study presents GeneVis, a visual analytics system to identify and analyze gene expression signatures across major cancer types. A task that cancer researchers utilize to discover biomarkers in precision medicine. We conducted four case studies with domain experts in oncology and genomics. The study results show that the system can facilitate the task and provide new insights from the data. Based on the three studies of this dissertation, we conclude that carefully designed visual analytics approaches can provide an enhanced understanding and support medical researchers for laborious evidence-based tasks in medical research.근거중심의학(Evidence-Based Medicine)이란 "임상 치료 및 의학 연구에서 현재 존재하는 최고의 증거를 양심적이고, 명백하며, 분별 있게 이용하는 방법론"이며 [98], 현대 의학에서 가장 널리 받아들여지는 의학 패러다임이다. 신뢰할 수 있는 고수준의 과학적 근거를 검색, 검토, 합성하는 것이야 말로 근거중심의학의 핵심이다. 하지만, 근거중심의학이 이미 광범위하게 사용되고 있음에도 불구하고, 의학 연구에 근거중심의학의 프로토콜을 실천하는 데에는 여전히 많은 어려움이 따른다. 의료 문헌 정보, 임상 정보 및 유전체학 정보까지 연구자가 검토해야 할 근거의 양은 방대하며 광범위하다. 또한 의학과 기술의 발전으로 인해 점차 더 빠른 속도로 늘어나고 있기에, 이를 모두 엄밀히 검토하기 위해서는 막대한 양의 시간과 인력이 있어야 한다. 본 논문은 시각적 분석 방법론을 접목하여 의학 연구에서 방대한 과학적 증거를 검색하고 검토할 시 발생하는 막대한 인적 자원의 과부하 문제를 완화하고자 한다. 이를 위하여 근거중심의학의 절차 중 특히 인력 소모가 막심한 절차들을 선정하고, 이러한 난관을 극복하고 보다 효율적이고 효과적으로 데이터에서 유의미한 정보를 도출할 수 있게끔 보조하는 세 가지 시각적 분석 시스템들을 구현하였으며, 각각의 시스템에 관한 디자인 연구를 수행하였다. 우선 첫 디자인 연구에서는 근거중심의학 연구에 있어 필수적 단계인 근거 합성 방법론의 하나인 근거 매핑(Evidence Mapping) 과정을 지원하기 위한 시각적 분석 시스템 PLOEM을 설계했다. 그리고 이를 검증하기 위해 다년간의 근거 기반 의료 경험이 있는 종양학자와 함께 사례 연구를 수행했다. 두 번째 디자인 연구에서는 의학 문헌 검색 시스템의 요구사항 분석을 위해 총 76명의 의사를 상대로 설문조사를 진행하였고, 이러한 분석을 바탕으로 대화형 시각적 분석 시스템인 EEEVis를 설계했다. 이 시스템은 여러 종의 서지 정보 시각화 인터페이스와 PubTator의 주석 정보를 활용하여 PubMed 검색 엔진의 검색 결과를 증강하는 시스템이며, 이를 평가하기 위해 총 24명의 의사와 함께 사용자 연구를 수행하였다. 이 연구 결과를 바탕으로 의학 문헌 검색 시스템에 대한 설계 지침과 과제를 제시한다. 마지막으로 세 번째 디자인 연구에서는 임의의 유전자군의 유전자 발현 패턴을 주요 암 유형에 따라 시각화하고 분석할 수 있는 시스템인 GeneVis를 설계하였다. 암 유형에 따른 유전자 발현 패턴의 분석과 비교는 암 연구자들이 정밀 의학에서 생체 지표(Biomarker)를 발견하기 위해 빈번히 수행하는 작업이다. 우리는 종양학 전문가 및 유전체학 전문가 총 4인을 대상으로 사례 연구를 진행하였고, 그 결과 GeneVis가 해당 작업을 더 수월하게 수행하는 것과 기존의 데이터에서 새로운 정보를 도출하는 것에 도움이 되었음을 확인하였다. 위의 세 디자인 연구의 결과를 바탕으로, 본 논문은 사용자 분석과 작업 분석을 동반한 시각적 분석 방법론이 의학 연구의 근거 관련 작업의 어려움을 해소하고, 분석 데이터에 대한 보다 나은 이해를 제공하는 것이 가능하다고 결론 내린다.CHAPTER1 Introduction 1 1.1 Background and Motivation 1 1.2 Dissertation Outline 5 CHAPTER2 Related Work 7 2.1 Evidence Mapping: Graphical Representation for a Scientific Evidence Landscape 7 2.2 Scientific Literature Visualizations and Bibliography Visualizations 9 2.3 Visual Anlytics Systems for Genomics Data sets and Research Tasks 10 CHAPTER3 PLOEM: An Interactive Visualization Tool for Effective Evidence Mapping with Biomedical literature 12 3.1 Introduction 12 3.2 Visual Representations and Interactions of PLOEM 14 3.2.1 Overview of the PICO Criteria 14 3.2.2 Trend Visualization with the Timeline view 17 3.2.3 Representing the PICO Co-occurrence with the Relation view 20 3.2.4 Study detail view 22 3.3 Usage Scenarios: Visualizing Various Study Sizes with PLOEM 23 3.4 Conclusion 24 CHAPTER4 EEEvis: Efficacy improvement in searching MEDLINE database using a novel PubMed visual analytic system 26 4.1 Introduction 26 4.1.1 Motivation 26 4.1.2 Preliminary Survey: A Questionnaire on conventional literature search methods 28 4.1.3 Design Requirements for Biomedical Literature Search Systems 36 4.2 System and Interface Implementation of EEEVis 37 4.2.1 System Overview 37 4.2.2 Bibliography Filters 40 4.2.3 Timeline View 41 4.2.4 Co-authorship Network View 43 4.2.5 Article List and Detail View 44 4.3 User Study 46 4.3.1 Participants 46 4.3.2 Procedures 48 4.3.3 Results and Observations 50 4.4 Discussion 54 4.4.1 Design Implications 56 4.4.2 Limitations and Future Work 57 4.5 Conclusions 59 CHAPTER5 GeneVis: A Visual Analytics Systemfor Gene Signature Analysis in Cancers 68 5.1 Motivation 68 5.2 System and Interface Implementation 69 5.2.1 System Overview 69 5.2.2 Gene Expression Detail View 71 5.2.3 Gene Vector Projection View 72 5.2.4 Gene x Cancer Type Heatmap view 74 5.2.5 User Interaction in Multiple Coordinated Views 76 5.3 Case Studies 76 5.3.1 Participants 76 5.3.2 Task and Procedures 76 5.3.3 Case1: Identifying SimilarGeneSignatures with TGFB1in Hallmark Gene Sets 80 5.3.4 Case2: Identifying Cluster Patterns in the HRD data set 81 5.3.5 Results 82 5.4 Summary 85 CHAPTER6 Conclusion and future work 86 6.1 Conclusion 86 6.2 Future Work 87 Abstract (Korean) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102박