(Version 2021) Lifesciences Data Mining And Visualization Market report published by Stratagem Market Insights is an in-depth analysis of the market covering its size, share, value, growth and current trends for the period of 2021-2028 based on the historical data. This research report delivers recent developments of major players with their respective market share. In addition, it also delivers detailed analysis of regional and country market.

Companies Mentioned of the Global Lifesciences Data Mining And Visualization Market:

Tableau Software, SAP SE, IBM, SAS Institute, Microsoft, Oracle, TIBCO Software, Information Builders, Dundas Data Visualization, Pentaho, InetSoft Technology.

This report examines all the key factors influencing growth of global Lifesciences Data Mining And Visualization market, including demand-supply scenario, pricing structure, profit margins, production and value chain analysis. Regional assessment of global Lifesciences Data Mining And Visualization market unlocks a plethora of untapped opportunities in regional and domestic market places. Detailed company profiling enables users to evaluate company shares analysis, emerging product lines, scope of Lifesciences Data Mining And Visualization in new markets, pricing strategies, innovation possibilities and much more.

Key Topics Covered:

Executive Summary

Lifesciences Data Mining And Visualization Market Landscape

• Market ecosystem
• Market characteristics
• Value chain analysis

Lifesciences Data Mining And Visualization Market Sizing

• Market definition
• Market segment analysis
• Market size 2021
• Market outlook: Forecast for 2021 – 2028

Five Forces Analysis

• Five forces summary
• Bargaining power of buyers
• Bargaining power of suppliers
• Threat of new entrants
• Threat of substitutes
• Threat of rivalry
• Market condition

Lifesciences Data Mining And Visualization Market Segmentation by Product

• Market segments
• Comparison by Product
• Lifesciences Data Mining And Visualization – Market size and forecast 2021 – 2028
• Market opportunity by Product

This report includes assessment of various drivers, government policies, technological innovations, upcoming technologies, opportunities, market risks, restrains, market barriers, challenges, trends, competitive landscape, and segments which gives an exact picture of the growth of the global Lifesciences Data Mining And Visualization market.

Key questions answered in the report:

• What is the growth potential of the Lifesciences Data Mining And Visualization market?
• Which product segment will grab a lion’s share?
• Which regional market will emerge as a frontrunner in the coming years?
• Which application segment will grow at a robust rate CAGR?
• What are the growth opportunities that may emerge in the Lifesciences Data Mining And Visualization industry in the years to come?
• What are the key challenges that the global Lifesciences Data Mining And Visualization market may face in the future?
• Which are the leading companies in the global Lifesciences Data Mining And Visualization market?
• Which are the key trends positively impacting the market growth?

Why choose Stratagem Market Insights?

Stratagem Market Insights is a management consulting organization providing market intelligence and consulting services worldwide. The firm has been providing quantified B2B research and currently offers services to over 350+ customers worldwide.

The post Trending Report: Lifesciences Data Mining And Visualization Market Wrap: Now Even More Attractive| Keyplayers- Tableau Software, SAP SE, IBM, SAS Institute appeared first on Artificial Intelligence.

A machine learning application’s value is dependent on the quality of data used to train and deploy it. Organizations are responsible for creating or acquiring enough data, that this data is useful for the specific application and that the analytics team is capable of sorting through and learning useful things from it.

The knowledge discovery in databases (KDD) finds knowledge in data; organizations use data mining methods to draw out its usefulness.

KDD vs. data mining

While most data scientists are familiar with data mining, KDD is a specialized process that applies high-level, sophisticated data mining techniques to find and interpret patterns from data. Though the terms are sometimes used interchangeably, KDD is used especially for machine learning, databases, pattern matching, AI and enterprise use.

“[In comparison], the term data mining is broadly applied to looking through piles of data and trying to find interesting patterns,” said Peter Aiken, associate professor at Virginia Commonwealth University.

In general, these processes both extract data from large databases, but KDD is more often used to explain the larger picture. There are varying divisions of the steps of KDD but in general they can be broken down into several steps:

Step 1: Selection — Sort out the data you would like to mine.

Step 2: Preprocessing — Data cleaning (removing any noise or outliers within the data set) using statistical techniques or data mining algorithms.

Step 3: Transformation — Data is prepared and developed through dimension reduction and attribute transformation. This step may be quite project-specific but always crucial to the success of the project.

Step 4: Data mining — Outline what kind of data mining would be most useful by judging which objective you are seeking (prediction or description).

Step 5: Interpretation/Evaluation — Assess and interpret the mined patterns, rules, and reliability in comparison to the original objective.

Association rules

Data mining is the process of identifying patterns and establishing relationships by sorting through data sets. Within this broad definition are association rules that analyze the data set for if/then patterns and use support and confidence criteria to locate the most important relationships. Support is how often items appear in the database and confidence is the amount of if/then statements that are correct.

Among the more common data mining parameters include anything from sequence analysis, classification and clustering, as well as forecasting.

Sequence analysis. Identifies patterns where one event points to another, later event.

Classification. Looks for new patterns and can change the way in which the data is organized.

Clustering. Locate and document groups of facts that had not been known yet. Groups are organized by how similar they are to one another.

Forecasting. These parameters within data mining discover patterns in data that point to reasonable predictions.

This is all a relatively manual process, however. Human intervention and decision-making come to play majorly in the KDD/data mining process. This is one of the largest differentiators from a similar process, machine learning. When it comes to machine learning, the quality of data is crucial and data mining allows for better insight to be drawn out from this data.

“Usually the most critical thing in [removing deficiencies in] performance of your model is also usually the most critical step in getting your model put into production,” said Kjell Carlsson, a Forrester Research analyst.

KDD, data mining and machine learning

If an enterprise is working on a machine learning project, then some form of the KDD process is also going on in-house. Both fall under the umbrella of data science and both processes are used for solving complex problems with data.

“The real question is from a user’s perspective, what are you trying to do,” Aiken said. “And if the data that you’re trying to use is more likely to come from a database than a big data pile.”

Machine learning and data mining share the same principles but function differently. A data scientist turns to data mining to pull from existing information to find emerging patterns that can help shape decision-making processes. Machine learning is more active and less hands-on. Machine learning takes this process a step further because it can learn from the existing data and teach itself what to look for in the future and predict patterns. Data mining is typically used as an information source from which a machine learning algorithm can learn.

Both are analytics processes that are good with pattern recognition and are therefore often confused. Machine learning may use some data mining techniques to build its models and data mining can use machine learning techniques to produce more accurate analysis.

“The biggest problem with computer science in today’s environment is that machine learning algorithms don’t have training data,” Aiken said.

Without training data, a machine learning model is unable to reach any kind of effective performance. As Aiken sees it, any boasting about a model without data is like saying well you’ve got this great baseball team you just have to teach them how to play baseball.

Uses of KDD/data mining and machine learning

Data mining and the overall process of KDD have carved out their own specialty. Data mining has been deployed in the retail industry in order to better understand the patterns of customer buying habits. Organizations can mine their customer data for relevant information on the success and failure of items and adjust from there.

It has also been used in finance by organizations looking into potential investments and whether a new organization is going to succeed. Past performance of successful startups, as well as patterns of indicators of business prowess, inform those in the finance industry of where to put their money.

Machine learning’s applications vary widely across industries for purposes such as fraud detection, autonomous vehicles and personalized marketing, among others. Organizations turn to machine learning algorithms to analyze vast amounts of data and provide continued growth and value as more data is brought in.

Machine learning algorithms can function better with relevant data sets and these can be brought about through the process of data mining.

The post KDD in data mining assists data prep for machine learning appeared first on Artificial Intelligence.

Underground cloud services may seem like an oxymoron, but they are quite real, and criminals are using them to speed up attacks and leave very little room for compromised businesses to react.

This is according to a new report from cybersecurity firm Trend Micro, which found terabytes of internal business data and logins – including for Google, Amazon and PayPal – for sale on the dark web.

The logins are sold through access to the cloud logs where they’re stored. As a result, Trend Micro argues, more accounts are monetized and the time from compromise to the account actually being used for nefarious purposes is cut from weeks to days or hours.

Just as businesses enjoy the speed and scalability of cloud services, so do criminals; more computing power and bandwidth allows them to optimize their operations.

Criminals that buy the logs of cloud-based stolen data usually use the data for the purposes of secondary infection, with ransomware being one of the more popular choices.

The report argues that this is a new trend that may gain even more popularity in the future, and even create a “new type of cybercriminal”: an expert in data mining that uses machine learning to enhance pre-processing and extraction of information to maximize usefulness to potential buyers.

Trend Micro believes criminals will focus on standardizing their services and pricing.

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Trend Micro Incorporated, the leader in cloud security, has identified a new class of cybercrime. Criminals are using cloud services and technology to speed up attacks, which decreases the amount of time enterprises have to identify and respond to a breach.

Trend Micro Research found terabytes of internal business data and logins for popular providers like Amazon, Google, Twitter, Facebook, and PayPal offered for sale on the dark web. This data is sold via access to the cloud logs in which it is stored. This results in more stolen accounts being monetized, and the time from initial data theft to stolen information being used against an enterprise has decreased from weeks to days or hours.

“The new market for access to cloud logs ensures stolen information can be used more quickly and effectively by the cybercrime community—that’s bad news for enterprise security teams,” said Robert McArdle, director of forward-looking threat research for Trend Micro. “This new cybercriminal market shows how criminals are using cloud technologies to compromise you. Which also means a business is not exempt from this attack method if they only use on-prem services. All organizations will need to double down on preventative measures and ensure they have the visibility and controls needed to react fast to any incidents that occur.”

Once access is purchased for logs of cloud-based stolen data, the purchaser will use the information for secondary infection. For example, Remote Desktop Protocol (RDP) credentials can be found in these logs and are a popular entry point for criminals targeting enterprises with ransomware.

Storing terabytes of stolen data in cloud environments has similar appeal for criminal businesses as it does for legitimate organizations. Cloud storage offers scalability and speed that provides more computing power and bandwidth to optimize operations.

Access to these logs of cloud data are often sold on a subscription basis for as much as $1,000 per month. Access to a single log can include millions of records, and higher prices are earned for frequently updated data sets or the promise of relative exclusivity.

With ready access to data in this way, cybercriminals can streamline and accelerate execution of attacks and potentially expand their number of targets. The result is to optimize cybercrime by ensuring threat actors who specialize in specific areas—say cryptocurrency theft, or e-commerce fraud—can get access to the data they need: quickly, easily and relatively cheaply.

The Trend Micro report warns that in the future, such activity could even give rise to a new type of cybercriminal—an expert in data mining who uses machine learning to enhance pre-processing and extraction of information to maximize its usefulness to buyers. The overall trend will be towards standardization of services and pricing, as the industry matures and professionalizes. 

The post Cybercriminals Use Cloud Technology To Accelerate Business Attacks appeared first on Artificial Intelligence.

WITH Kuala Lumpur, Putrajaya, Sabah and Selangor placed under a conditional movement control order (MCO) following the spike in Covid-19 cases nationwide, the onus is on every Malaysian now to play a greater and crucial role in flattening the infection curve.

We were successful in fighting the pandemic’s spread the first time around, so we know we can do it again.

If we are all disciplined and diligently comply with standard operating procedures and restrictions put in place by the authorities, areas under the conditional MCO will see a downward trend in case numbers over the next several weeks, according to health experts.

There is no point playing the blame game over the surge of infections now. It’s spilt milk.

Our priority now should be on bringing the numbers down and preventing further havoc in the economy that would wipe out more businesses and jobs, and cause disruptions to people’s livelihoods.

Economic data for early in the third quarter showed the promising beginnings of recovery with the unemployment rate easing from 4.8% in June to 4.7% in July.

Let’s not lose that momentum.

To keep the positivity going, remember the two most important things when you leave home: wear a mask and – this is vital – keep you distance from people.

In handling this health crisis, the government must also be firm and clear in issuing instructions.

The speed at which decisions have to be made sometimes causes confusion, for instance, how many people can travel together in a car (two, with the passenger sitting in the back, according to the National Security Council, or MKN) and how many people can sit at restaurant tables – two, according to MKN on Wednesday; but larger tables can have four people, said Senior Minister (Security) Datuk Seri Ismail Sabri Yaakob on Thursday; a maximum of five people at a table normally seating 10 is possible, said Selangor Mentri Besar Datuk Seri Amirudin Shari on Friday.

Obviously, announcements of protocols should come only from one source, not from multiple agencies or politicians, to prevent misinformation in this critical time.

At the same time, the authorities should also fully utilise the MySejahtera app to fight this pandemic, especially in performing contact tracing and notifications to warn the public of Covid-19 hotspots.

MySejahtera was launched six months ago and was developed cooperatively by the Health Ministry, the National Security Council, the Malaysian Administrative Modernisation and Management Planning Unit (more familiarly known as Mampu) and the Malaysian Communications and Multimedia Commission.

But is the government using the app’s full capability beyond simply recording users’ whereabouts?

Big data has been used effectively in countries like South Korea and China to manage infection rates, and MySejahtera has access to pretty big data with 15.1 million registered users as at Aug 16.

However, out of 9,200 Covid-19 patients detected up to August, the app successfully found only 322 cases, which is a mere 3.5%.

There is also the question about how safe the data collected by MySejahtera is.

The authorities must be transparent about data mining and assure Malaysians that the information collected is confidential and used only to combat the spread of Covid-19, and that it will not fall into the wrong hands or be misused for commercial purposes.

The post Better equipped to flatten the curve appeared first on Artificial Intelligence.

While a single analyst usually only has a limited set of data available, which he usually evaluates by means of chart analysis, users of CryptoCaptain get access to a comprehensive evaluation of the overall market based on proprietary data mining algorithms and AI.

CryptoCaptain’s predictive analytics not only includes the assessments and forecasts of all relevant top analysts, but also captures the mood of market participants as well as current news. The method proves to be clearly superior to traditional forms of market analysis – especially in the crypto markets.

Taming the market’s high volatility
The price development of Bitcoin, but also of other cryptocurrencies, is characterized by large price peaks and high volatility. High volatility makes the crypto market incredibly interesting because it opens up profit potentials that would be unthinkable in other markets.

At the same time, however, volatility also harbors a high risk, because price swings in the wrong direction present challenges even for professionals.

With the help of CryptoCaptain, the big swings of bull and bear markets can be predicted well. CryptoCaptain guides long-term investors in particular as to when they should buy or sell Bitcoin & Co. Thus, drawdowns can be successfully avoided and market opportunities can be turned into profits more often. Eventually, traders also benefit by using the market sentiment of CryptoCaptain as an overlay for their own strategies to produce fewer bad trades.

Scientific grounding
In order to take advantage of the opportunities offered by crypto markets, the timing with which you enter the market is of crucial importance. In the course of many years of research, the market sentiment in crypto markets has proven to be twice as effective for this purpose as chart analysis.

Dr. Achim Klein, a co-founder of CryptoCaptain, has a doctorate in business information systems and has made it his business to extract valuable knowledge from large amounts of data. CryptoCaptain is benefited from his more than 10 years of experience in various university research projects in the field of smart data and predictive analytics.

Users of CryptoCaptain now have access to a service that has been refined over the years on the basis of scientifically sound findings and which clearly stands out from other solutions on the market. By using a unique combination of proprietary algorithms with the help of artificial intelligence and data mining, a real-time analysis of the crypto market is carried out, giving users a considerable advantage.

CryptoCaptain navigates investors comfortably and with little effort
CryptoCaptain provides its users with far-sighted signals. Investing becomes more comfortable due to a longer investment horizon with fewer trades and therefore less stress and lower costs.

CryptoCaptain offers a barometer for market sentiment in an online dashboard. Furthermore, it provides the Bull Market Compass, which provides investment signals, making it easier to decide when to invest into the crypto market. Users receive signals conveniently by e-mail. The Bull Market Compass sends signals a few times per year, thus requiring only little effort by users.

About CryptoCaptain
CryptoCaptain wants to help all those interested in crypto to profit from the financial revolution emanating from Bitcoin & Co.

CryptoCaptain was developed at the University of Hohenheim in Stuttgart, Germany, on the basis of years of research and is supported by the Federal Ministry of Economics and Energy and the European Social Fund as part of the “EXIST-Gründerstipendium”. As part of the BLOCKROCKET European Research Labs, CryptoCaptain sees itself in line with other up-and-coming start-ups that could become the next unicorn.

The post CryptoCaptain Navigates Investors Through the Market With AI and Data Mining appeared first on Artificial Intelligence.

Experts invited to a House of Lords special inquiry committee meeting on artificial intelligence (AI) warned of the brittleness of the technology and lack of understanding around it.

Following on from an Artificial Intelligence Committee report, published in April 2018, the expert panel was invited to discuss how the opportunities and risks of AI have changed over the past year, particularly in light of the coronavirus pandemic. The lowering of controls to protect public data and ethics was one of the topics discussed by the committee.

Michael Wooldridge, professor of computer science at the University of Oxford, spoke about why healthcare would benefit immediately from AI. “If it is to be done right, transferring AI techniques from labs to GPs and hospitals is a long process,” he said.

From a risk perspective, Woolridge said there were “endless examples” of data abuse. “For AI to work, it needs data. This is a huge challenge. Society has not yet found its equilibrium in this new world of big data,” he said.

Woolridge also voiced concerns that people will rely too heavily on AI technology to make decisions. “The tech is really brittle. It is important we don’t become complacent and naively rely on AI instead of human judgement,” he warned. 

When asked about ethical barriers, Wendy Hall, regius professor of computer science at the University of Southampton, said: “In the UK, we have a lot of people studying ethics. We need to develop some practical guidelines.”

Hall said AI ethics presents major issues for society. People also need to understand where they need to take control of their data, and where data is needed.

“Morals and ethics are not the same thing,” she said. “We have to self-regulate. We have to get people to understand their own responsibilities.”

Hall urged companies to get involved in the AI ethics conversation. She suggested that regulators should try to develop simple frameworks and audit arrangements which can be easily applied. Hall predicted actuaries, accountants, lawyers and new careers were likely to emerge to help companies audit algorithms for bias, fairness, accountability and ethics.

Daniel Susskind, a fellow in economics at Balliol College, urged the government to reinstate the data and privacy controls that were lowered to support coronavirus track and trace applications. “An important task in the months to come is to rein back that power we granted to tech companies and states around the world,” he said.

Discussing the need for ethical AI, Susskind said: “If we are honest, the finest computer scientists are not necessarily hired for the sensitivity of their moral reasoning. There is a burden on engineers to make these technologies as transparent as they can be to ensure users can scrutinise them.”

In the past, AI systems tended to be modeled on human decision-making, but systems now use deep learning. “Today’s systems are far more opaque and less transparent,” Susskind warned.

The availability of data to improve AI algorithms was another topic discussed at the meeting. When asked about the use of data for public good, Carly Kind, director of the Ada Lovelace Institute, described the situation as “a false dichotomy”.

While the pandemic has shown the use of data for public good, she said people wanted guarantees around privacy. Although the General Data Protection Regulation (GDPR) stood up well during the pandemic, Kind pointed out that there were numerous occasions when researchers needed to access data held in companies, such as to stem misinformation being spread on social media platforms, but that access was difficult.

She warned that public data is held by a few very large US companies, not the public sector. Kind said Apple, Amazon, Facebook and Google were in a much better position than public sector organisations to advance because they had monopolistic access to public data.

“To create a more even playing field, we need to break up monopolistic control of platforms,” she said.

The post Public data should not be held by US tech giants appeared first on Artificial Intelligence.

DeepTarget Inc., a solution provider that utilizes data mining and machine learning to deliver targeted communications across digital channels for banks and credit unions, announced its use of leading-edge machine learning techniques partnered with historical, proprietary data to help community financial institutions complete more transactions, open more accounts and enhance the quality of the customer and member experience.

Machine learning, a subset of artificial intelligence (AI) involving computer algorithms that improve automatically through experience, has been widely adopted in the financial services industry and has proven successful in helping financial institutions better tailor products and services to consumers. In fact, FinTech News recently highlighted that machine learning offers the financial services industry with “exceptional benefits like more efficient processes, better financial analysis, and customer engagement.”

With it’s rich Digital Experience Platform (DXP) and innovative 3D StoryTeller, DeepTarget is unique in the industry in helping financial institutions design and execute intelligent cross-channel marketing campaigns that leverage the latest machine learning technology. By utilizing a predictive model that targets specific audiences with the highest propensity to purchase a particular product, financial institutions can calculate the likelihood that each user will open a specific account. Combined with the patent-pending 3D StoryTeller capability, financial institutions of all sizes can now drive customer engagement with unique, captivating AI-powered personalized financial stories to individual account holders. This latest innovation inspired by social media is powered by the DeepTarget “brain” – an advanced Digital Experience Platform developed over several years and available already integrated with multiple digital banking systems.

“Delivering rich content and relevant offers are critical to customer success,” said Jill Homan, President of DeepTarget. “Machine learning lets FIs further automate the process, lessening the burden on marketing staff. Utilizing the machine learning model provides complete flexibility for the financial institution to mix and match targeting methods per campaign – either AI, rules, or list-based targeting for truly relevant and human-like engagements. Offering this technology is critical in enabling financial institutions of all sizes to use techniques and insights previously reserved for only the largest institutions.”

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Whether you are a commercial or Medicare health plan, or a Medicaid managed care plan, you know that paying claims with third-party primary responsibility is a significant source of unnecessary cost.

Coordination of benefits (COB) is the best way to address this issue, but it’s not always easy. It’s often tough to identify other coverage, determine the order of payer responsibility and manage members’ constantly changing eligibility and life circumstances. Once you’ve paid a claim where other coverage exists, it can also be costly to recover those payments.

What is Proactive Coordination of Benefits?

True coordination of benefits goes beyond opportunistic identification of other coverage. An effective COB program requires a proactive solution that focuses on the entire lifecycle of a claim. The goal is accurate, real-time primary payer detection and verification to avoid or recover costs.

It’s essential to begin COB work before payments are made. Accurately determining eligibility before a claim is processed helps prevent improper payments. Corrections can be entered in the payer’s system or returned to the client to correct before payments are made. This can minimize costly “pay and chase” activity.

The unfortunate reality, however, is that not all COB efforts can be completed during the pre-payment phase. When overpayments inevitably occur, plans must also have a system to identify them and then recover the funds by billing claims to the correct, liable payer.

Proactive COB Relies on Data and Technology

Without a foundation of robust data and technology, health plans struggle to deploy proactive coordination of benefits. Here are six characteristics that differentiate proactive COB systems from other solutions:

1. Accurate, real-time detection and verification of other primary payers. Advanced data mining, data matching, and analytics are the keys to identifying other healthcare coverage for members. These technologies and algorithms address coverage complexities like common name matches. Comparative and predictive analytics help manage plan benefits and other types of denials to optimize recoveries.
2. Identification of claims with the highest likelihood of recovery. Machine learning technologies can analyze and target claims that have the greatest probability of recovery based on factors like claim type, employer group and procedure code.
3. Collection and longitudinal analysis of member data. When it comes to COB, big data infrastructure is a game changer. Large database repositories like data lakes can house carrier response files. With that information, plans can leverage analytics to create longitudinal case histories for members. These should identify when beneficiary coverage begins and ends. Coverage date ranges support post-payment recovery of claims from liable third parties.
4. Automatic dispatch of rejected or inappropriately paid claims. Revenue cycle management technologies can identify the root cause of denials and facilitate corrective action. Through automation, proactive COB systems analyze, correct, and resubmit rejected or inappropriately paid claims until third-parties have correctly paid.
5. Electronic reclamation claims billing. Using technology for claims billing results in fewer errors and higher yields than traditional paper claims.
6. Friction-free online communication with providers. Online portals allow two-way communication between plans and providers. When designed with end users in mind, these portals make it easy to review and submit claims information and additional information electronically. This minimizes the impact on providers, while accelerating recovery cycles.

Proactive COB Maximizes Cost Savings for Health Plans

Timely and accurate information about members’ other coverage enables plans to better coordinate care, maximize cost savings, ensure accurate reimbursements, and reduce administrative rework.

After transitioning to proactive COB solutions, health plans report lower costs which improve their bottom lines. Technology-based COB systems accelerate the return of recovered funds. Increased claim accuracy rates translate into fewer subsequent claims adjustments. Real-time review and reporting also enable employees to securely access the status of claims.

These benefits are important to health plans of all kinds, but particularly for Medicaid managed care plans. The U.S. Government Accountability Office has reported that more than 13 percent of Medicaid members have Medicare or other sources of private insurance that are dynamic in nature.

Looking ahead, the complexity and cost of healthcare will most likely continue to increase. Given this environment, proactive COB represents a critical tool for plans to use to maintain their sustainability, competitive advantage and success in the market.

The post Proactive vs. Reactive Coordination of Benefits: Data and Technology Make the Difference appeared first on Artificial Intelligence.

Washington D.C. [USA], September 19 (ANI): To detect emerging psychosis in younger people, doctors have developed a new data mining method.

The new method, based on advanced data mining to pick up early risk signs from schools, hospitals, and general doctors, will be presented at the ECNP virtual congress and is in press with a peer-reviewed journal.

Psychosis is a condition that causes you to lose touch with reality, causing you to suffer from hallucinations or delusions. There are a variety of possible causes, including migration and social stress, trauma, substance abuse, etc. It represents a significant care burden, affecting about 20 million people and costing Europe around EUR94 billion every year (2011 estimate).

Clinical experience has shown that the best way to manage it is to stop it developing. Over the last 25 years, doctors have developed ways of detecting young people at risk of developing psychosis and predicting which young people might go on to develop the disorder, and so have been able to take steps to lower risk.

However, the way clinicians were detecting young people was not systematic and may have missed many at-risk people. Now doctors in the UK have developed new data mining methods that can potentially detect most people who are at risk of developing psychosis. This, in turn, would allow them to offer them preventive psychological interventions that can halve their risk of developing full-blown psychosis.

“Prevention is the most promising way of improving the mental health of young people. This generation’s mental health is particularly under stress, especially facing the ongoing COVID-19 worry, and we need to intervene urgently. The future for those at risk of psychosis is to intervene before the disorders strike,” Research leader Professor Paolo Fusar-Poli, of the Institute of Psychiatry at King’s College, London, said.”We have developed a data mining method to search medical records for those at risk of progressing to psychosis. Many medical records are fairly unstructured, with information of mental health being hidden in sections that do not allow systematic research. Our data-mining system does a more complete search of the records people who have been referred to hospital (secondary care), looking for keywords such as weight loss, insomnia, cocaine, guilt, etc. We can look for 14 different terms which we then evaluate for the risk of psychosis. At that point, patients might be invited for a one-to-one interview. We have found that prevention can halve the risk of psychosis developing,” added Fusar-Poli.

The systems have evaluated 92,151 patients over a long follow up period. They were able to confirm that their method worked well to detect young people at risk, although Professor Fusar-Poli cautioned that “these results need further replication in other countries before they can enter clinical routine but they look very promising. Replication will be facilitated by international research consortia such as the ECNP-funded Prevention of Mental Disorders and Mental Health Promotion Network”. Prof. Fusar-Poli suggested that the detection of these young people is the first step towards prevention. Preventive interventions in these people can translate into several benefits.

“This translates into real benefits. Although the initial cost for establishing specialised services detecting young people at risk of psychosis is greater, intervening before the onset of psychosis is associated with fewer treatments, fewer days in the hospital, in addition to the tangible and social health benefits, meaning that the NHS saved around £1000 per patient diagnosed. Our detection systems can extend these benefits to many other young people who might be at risk of psychosis,” said Fusar-Poli.Professor Fusar-Poli will present the work while chairing a session on the prevention of mental disorders (see below) at the ECNP Congress.

“We have been working with the ECNP special group on Prevention of Mental Disorders and Mental Health Promotion, and with the EU-Funded European Brain Research Area to set up a Europe-wide system of advance warning for young people at risk of psychosis. It is essential that we bring the best expertise to bear on this problem, and we can all learn from the experience of others,” he added.

Commenting independently, Professor Andreas Meyer-Lindenberg (Mannheim), member of the ECNP executive board said: “This work is an excellent example of the transformative role of artificial intelligence and big data processing in psychiatry. While much attention in this field has been focused on biological data and biomarkers, this result shows the gains that can be made if the wealth of written information that clinicians produce in their daily work is mined using innovative approaches.” (ANI)

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