Thinkee

Description

Thinkee offers a software platform for automating process monitoring, data collection, management of connected devices, real-time data analysis, intervention tracking, audits, alerts for malfunctions, non-conformity management, and automated reporting. The platform allows users to visualize information, create customized dashboards, and make data-driven decisions efficiently.

Official source

https://thinkee.io

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Related concepts (21)

Related courses (106)

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Related units (4)

MoDN-Flip: Optimizing Modular Clinical Decision Support Networks with Decision Flippability Score

Kuan Tung

BACKGROUND. Clinical Decision Support Systems (CDSS) are point-of-care questionnaires that guide consultations to evidence-based predictions with the aim of standardizing care and optimizing resource allocation. The data collected in these tools contain potentially useful insights to further train its predictive algorithms, however, the questionnaire structure biases data with systematic missingness, making it unusable in traditional models. Further, privacy and interoperability issues make it challenging to synergize with data collected in related CDSS tools. The Modular Decision Support Network (MoDN) was previously proposed by our group to address these issues. It can predict diagnoses at each step in a consultation using sequentially encoded questions. However, it lacks the ability to provide information on unencoded questions, such as predicting their importance, which could potentially optimize questionnaire structure. AIM. In this work, we aim to optimize MoDN by deriving a flippability score (FS) which computes the importance of a question in the form of how likely its answer would flip (change) the predicted diagnoses. METHODS & FINDINGS. The FS is computed with the help of MoDN’s feature decoders that predict unknown feature values in the data. The optimal questionnaire order is then derived by feeding MoDN the question with the highest FS. This approach—-MoDN-flip—- is validated on two independent real world CDSS-derived data sets of pediatric outpatients. We show that FS-optimized question order can achieve the baseline predictive performance 14.71% faster (or with 3.53 fewer questions) compared to MoDN with the original question order. FS-optimized questionnaires are also able to improve the cumulative F1 score by 5.24%. Furthermore, we test the utility of FS-optimization when porting MoDN to an imperfectly interoperable data set (i.e. with slightly different questionnaire structures and partially-overlapping feature sets). The results show that the score is valid in a new dataset and can provide better predictive performance at an earlier stages, albeit a slight cost to final performance. CONCLUSION. With the aim of providing data-driven questionnaire optimization for CDSS tools, we propose the flippability score, which indicates the importance of each potential question in a consultation. The experimental results show that FS can express predictive importance of questions and has the potential to significantly optimize questionnaire structure. It is now necessary to evaluate this ordering against human consultation logic.

2022

Alpine: Efficient In-Situ Data Exploration in the Presence of Updates

Anastasia Ailamaki, Matthaios Alexandros Olma, Antonios Anagnostou

The ever growing data collections create the need for brief explorations of the available data to extract relevant information before decision making becomes necessary. In this context of data exploration, current data analysis solutions struggle to quickly pinpoint useful information in data collections. One major reason is that loading data in a DBMS without knowing which part of it will actually be useful is a major bottleneck. To remove this bottleneck, state-of-the art approaches perform queries in situ, thus avoiding the loading overhead. In situ query engines, however, are index-oblivious, and lack sophisticated techniques to reduce the amount of data to be accessed. Furthermore, applications constantly generate fresh data and update the existing raw data files whereas state-of-the art in situ approaches support only append-like workloads. In this demonstration, we showcase the efficiency of adaptive indexing and partitioning techniques for analytical queries in the presence of updates. We demonstrate an online partitioning and indexing tuner for in situ querying which plugs to a query engine and offers support for fast queries over raw data files. We present Alpine, our prototype implementation, which combines the tuner with a query executor incorporating in situ query techniques to provide efficient raw data access. We will visually demonstrate how Alpine incrementally and adaptively builds auxiliary data structures and indexes over raw data files and how it adapts its behavior as a side-effect of updates in the raw data files.

ACM New York, NY, USA2017

Data and information visualization

Data and information visualization (data viz or info viz) is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.

Data analysis

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Automated decision-making

Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics.

Data collection

Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same.

Information design

Information design is the practice of presenting information in a way that fosters an efficient and effective understanding of the information. The term has come to be used for a specific area of graphic design related to displaying information effectively, rather than just attractively or for artistic expression. Information design is closely related to the field of data visualization and is often taught as part of graphic design courses.

Statistical graphics

Statistical graphics, also known as statistical graphical techniques, are graphics used in the field of statistics for data visualization. Whereas statistics and data analysis procedures generally yield their output in numeric or tabular form, graphical techniques allow such results to be displayed in some sort of pictorial form. They include plots such as scatter plots, histograms, probability plots, spaghetti plots, residual plots, box plots, block plots and biplots. Exploratory data analysis (EDA) relies heavily on such techniques.

Topics in data science

Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.

High-level programming languages

In computer science, a high-level programming language is a programming language with strong abstraction from the details of the computer. In contrast to low-level programming languages, it may use natural language elements, be easier to use, or may automate (or even hide entirely) significant areas of computing systems (e.g. memory management), making the process of developing a program simpler and more understandable than when using a lower-level language. The amount of abstraction provided defines how "high-level" a programming language is.

Descriptive statistics

A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information, while descriptive statistics (in the mass noun sense) is the process of using and analysing those statistics. Descriptive statistics is distinguished from inferential statistics (or inductive statistics) by its aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent.

Data science