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The Hitchhiker’s Guide To Survival Analysis

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Benedict Timmerman


Survival analysis is the best thing in the world since sliced bread! However, in most machine learning circles, it’s pretty much synonymous with an “# it’scomplicated” relationship status.

Survival Analysis is an Extremely Valuable Branch of Statistics.

We want our guide to better serve you as a straightforward go-to/how-to, eliminating any confusion. The guide provides a valuable resource on how survival analysis, which can be applied to — well, almost anything.

However, survival analysis is wrought with misunderstanding and misuse.

What else should I know about survival analysis?

Also referred to as “time-to-event” analysis, simply put, it’s what we find when we analyze the time it takes for something like buying a new home (an event) to happen after getting a promotion -which we call “an exposure.

Basically, it’s modeling or a set of statistical stratagems which measure the time as mentioned above to an event. Literally how long it takes for something of interest to happen. Depending on what you are studying, observing, researching, or just finding interesting- you want to know, and we can now actionably determine how long it takes to happen.

To get started, first and foremost, you need to set and formulate your research question aptly to perform a survival analysis approach.

Often, researchers will simply use ‘when’ and/or ‘whether’ terminology. But, first, the information is given — as a prediction of when and/or whether something will happen.

Then the conclusion is in a yes, or no determination. Finally, the conclusion is an analysis about how long it takes before what we want to see (the subject of interest being examined) will happen, and whether what we’re looking for will happen or not.

When you’re analyzing how long it takes for an event to happen and whether it will happen at all, it is imperative that what you want to eventually see and find is the same (equal) for all the subjects you examine.

In other words, you don’t want a sample with elements that have no chance of experiencing the event. It just won’t work.

Exposure is the point when we’re off to the races and start the proverbial research clock in order to analyze any time-to-event.

The event itself, in this case buying a new home, means simply the time needed to process and develop from the exposure which is getting that promotionthe moment when we stop the “clock.”

The time elapsed between these two points is the focus of interest which we call “the survival time.”

Survival analysis is a game-changer for a diverse variety of disciplines and areas of research.

Most experts, however, mistakenly consider survival analysis a tool solely applied to study death and disease, an accurate method to measure relapse of a medical condition, the potential hospitalization of a patient, and the mortality rate in medicine and biomedical disciplines.

Survival analysis application has thankfully spread to serve a variety of fields and disciplines, including engineering, social and behavioral sciences, even professional sports.

In engineering, this process is known as “failure-time analysis” and is mostly applied to test the durability and quality of products.

Incorporating survival analysis in engineering is valuable. For example, we see a manufacturer wants to test how long it takes for light bulbs to burn out, how often the company’s computers crash, even predict when a mechanical part like an engine head gasket will crack.

In social sciences, survival analysis is known as “time-to-event” analysis. This is because there have been scientific studies to answer queries such as how long it takes for one to get married, get a first tattoo, buy a first home, or to graduate.

Medicine and biomedical research

In addition to medicine and biomedical research, JADBio can definitively perform survival analysis on several out-of-the-box and even what one may consider ‘weird’ cases, including:

Health – Obviously, when analyzing health disciplines, we can actionably determine values such as the time to: death, device failure such as a heart pump, or simply the readmission rate of a specific subset of patients.

Market – We use survival analysis more and more in marketplace areas of research such as manufacturing or sales when we want to determine the time to: a component failure in machines, whether a certain device becomes obsolete, and how long it will take to obtain a certain patent for example.

Finance – A valuable tool in the evermore elusive waters of finance, survival analysis can be applied to calculate the time to predict when a hospital may turn a profit or report loss, calculate costs, and how often staff present burnout or should be promoted.

Social Sciences – Especially helpful in social sciences, where experts now can analyze the time to: divorce, new couples having their first or second child, and how long it will take new families to buy their first home.

Government and Social Services – Helps determine the time to: child welfare and to match children with appropriate foster parents. Used to optimize the length of stay of children in the program, to estimate participation time in various social programs, and to estimate the time it takes for various policies to take effect.

Law Enforcement – Predicts the time to: estimate the likelihood of recidivism in criminal offenders.

Marketing Operations – Performed to assess the time to: the length of participation in loyalty programs.

Sports – Sports is a field where survival analysis can really be your golden goose. Sports — that’s right. In professional sports, survival analysis will change the game when it comes to delivering results like time to: mechanical failure of race car engines, or tires in F1, and the time it takes athletes to be substituted in team sports like football.

A coach can know the best time to switch out a soccer player. Team doctors and government health authorities can accurately evaluate and certainly limit the rate of Chronic Traumatic Encephalopathy (CTE) –a degenerative brain disease observed in professional athletes, military veterans, and anyone with a history of repetitive brain trauma.

In essence, there is no differentiation to survival analysis being used as a tool whether we consider health disciplines, the global market, social and behavioral issues, or professional sports.

When researching for survival analysis — survival time is the main driving interest.

We perform survival analysis on subjects that present a delayed onset of events where our goal is to observe that specific timeframe, how long it takes for the event to happen.

It is irrelevant whether there is a positive or negative correlation attributed to the event. The event may very well be death (negative), yet it can also be a new promotion (positive).

Although initially developed in the biomedical sciences to analyze time to death either of patients or of laboratory animals, survival analysis is now widely used in engineering, economics, finance, healthcare, marketing, and public policy. Survival analysis can be used to predict when a patient will expire; when cancer will metastasize, or on anything you are trying to predict time-wise.

Our Secret Special Sauce

At the core of this work is JADBio. JADBio systematically compares the performance and stability of a selection of machine learning algorithms and feature selection methods that are suitable for high-dimensional, heterogeneous, censored, clinical and other forms of data. The data set is used in the context of providing specific, accurate, and actionable predictions.

Leveraging the advances in modern data collection techniques will produce ever-larger clinical and other large data sets. It’s imperative to identify methods that can be used to analyze high-dimensional, heterogeneous, survival data.

JADBio has a world-class team and constructs a range of machine learning algorithms capable of analyzing vast types of data providing clients with the power to make decisions and steer their respective objectives in the direction of success.

Definitions of standard terms in survival analysis:

  • Event: Death, disease occurrence, disease recurrence, recovery, or other experience of interest.
  • Time: The time from the beginning of an observation period (such as surgery or beginning treatment) to (i) an event, or (ii) end of the study, or (iii) loss of contact or withdrawal from the study.

Image Credit: Provided by the author from The Hitchhikers Guide to Survival Analysis; thank you!

Benedict Timmerman

Benedict Timmerman is a Senior IT Experience Analyst supporting Digital Giraffe’s clients operating within the AI industry. Benedict covers data and machine learning solutions, providing quantitative and qualitative analysis on the available practices, people and markets. Benedict also spearheads the company’s lead generation process for its clients designing outreach campaigns.

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Fintech Kennek raises $12.5M seed round to digitize lending

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Google eyed for $2 billion Anthropic deal after major Amazon play


London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 tech.eu report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from Kennek.io; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Fortune 500’s race for generative AI breakthroughs

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Deanna Ritchie


As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations

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Deanna Ritchie


As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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