Doctors have developed a new data mining method to detect many young people with emerging psychosis.

The new methods, based on advanced data mining to pick up early risk sign 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 which 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 €94 billion European 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 which can potentially detect most people who are at risk of developing psychosis.

This, in turn would allow to offer them preventive psychological interventions that can halve their risk of developing full-blown psychosis.

“We have developed a data mining method (using Natural Language Processing), 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 which 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”.

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 detection of these young people is the first step towards prevention. Preventive interventions in these people can translate in 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 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”
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”

Commenting, 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.”

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Patient safety, data privacy, and health equity are key considerations for the use of chatbots powered by artificial intelligence in healthcare, according to a viewpoint piece published in JAMA.

With the emergence of COVID-19 and social distancing guidelines, more healthcare systems are exploring and deploying automated chatbots, the authors noted. However, there are several key considerations organizations should keep in mind before implementing these tools.

“We need to recognize that this is relatively new technology and even for the older systems that were in place, the data are limited,” said the viewpoint’s lead author, John D. McGreevey III, MD, an associate professor of Medicine in the Perelman School of Medicine at the University of Pennsylvania.

“Any efforts also need to realize that much of the data we have comes from research, not widespread clinical implementation. Knowing that, evaluation of these systems must be robust when they enter the clinical space, and those operating them should be nimble enough to adapt quickly to feedback.”

The authors outlined 12 different focus areas that leaders should consider when planning to implement a chatbot or conversational agent (CA) in clinical care. For chatbots that use natural language processing, the messages these agents send to patients are extremely significant, as are patient’s reactions to them.

“It is important to recognize the potential, as noted in the NAM report, that CAs will raise questions of trust and may change patient-clinician relationships. A most basic question is to what extent CAs should extend the capabilities of clinicians (augmented intelligence) or replace them (artificial intelligence),” the authors said.

“Likewise, determining the scope of the authority of CAs requires examination of appropriate clinical scenarios and the latitude for patient engagement.”

The authors considered the example of someone telling a chatbot something as serious as “I want to hurt myself.” In this case, the patient safety element is brought to the forefront, as someone would need to be monitoring the chatbot often.

This hypothetical situation also raises the question of whether patients would take a response from a chatbot seriously, as well as who is responsible if the chatbot fails in its task.

“Even though technologies to determine mood, tone, and intent are becoming more sophisticated, they are not yet universally deployed in CAs nor validated for most populations,” the authors said.

“Moreover, there is no mention of CAs in the US Food and Drug Administration’s (FDA) proposed regulatory framework for AI or machine learning for software as a medical device nor is there a user’s guide for deploying these platforms in clinical settings.”

The authors also noted that regulatory organizations like the FDA should develop frameworks for appropriate classification and oversight of CAs in healthcare. For example, policymakers could classify CAs as low risk versus higher risk.

“Low-risk CAs might be less automated, structured for a specialized task, and have relatively minor consequences if they fail. A CA that guides patients to appointments might be one such example,” the authors wrote.

“In contrast, higher-risk CAs would involve more automation (natural language processing, machine learning), unstructured, open-ended dialogue with patients, and have potentially serious patient consequences in the event of system failure. Examples of higher-risk CAs might be those that advise patients after hospital discharge or offer recommendations to patients about titrating medications.”

Additionally, the authors noted that in partnerships between vendors and healthcare organizations to use CAs, all should be mindful of converging incentives and work to balance these goals with attention to each of the domains.

“Given the potential of CAs to benefit patients and clinicians, continued innovation should be supported. However, hacking of CA systems (as with other medical systems) represents a cybersecurity threat, perhaps allowing individuals with malicious intent to manipulate patient-CA interactions and even offer harmful recommendations, such as quadrupling an anticoagulant dose,” the authors stated.

The authors stated that ultimately, the successful and effective deployment of chatbots in healthcare will depend on the industry’s ability to assess these tools.

“Conversational agents are just beginning in clinical practice settings, with COVID-19 spurring greater interest in this field. The use of CAs may improve health outcomes and lower costs. Researchers and developers, in partnership with patients and clinicians, should rigorously evaluate these programs,” the authors concluded.

“Further consideration and investigation involving CAs and related technologies will be necessary, not only to determine their potential benefits but also to establish transparency, appropriate oversight, and safety.”

Healthcare leaders will need to ensure they continually evaluate the capacity of these tools to improve care delivery.

“It’s our belief that the work is not done when the conversational agent is deployed,” McGreevey said. “These are going to be increasingly impactful technologies that deserve to be monitored not just before they are launched, but continuously throughout the life cycle of their work with patients.”

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November 04, 2019 – What do the following numbers have to do with the annual meeting of the Radiological Society of North America: 2, 12, 32, 271, and 308? They refer to the presence of “artificial intelligence” at the show from 2015 to 2019, in that order.

RNSA sees tremendous potential in the application of AI and its various permutations to the work of radiologists across the continent — a significant shift from the initial belief that AI would make radiologists redundant.

The society has gone so far as standing up an expanded AI showcase for this year’s show, which takes place December 1 through 6 at the McCormick Place in Chicago.

“Many RSNA meeting attendees seek out AI subject matter. Creating an encompassing showcase on artificial intelligence for exhibitors, educators and researchers will create a dynamic environment for our attendees,” said Steve Drew, RSNA Assistant Executive Director of Scientific Assembly, Informatics and Corporate Relations in a July announcement.

“High interest by commercial companies and meeting attendees led to this exciting development,” added John Jaworski, CEM, Director: Meetings and Exhibition Services of RSNA. “We now have more than 100 AI Showcase companies participating—which is up 25 percent over 2018’s final showcase figures—and the AI Theater, Deep Learning Classroom and Hands-on Classroom will provide various educational opportunities on artificial intelligence within the Showcase.”

Given this explosion of AI at RNSA’s annual event, attendees must know the terms that will be thrown around and differentiate between hype and reality. So here’s a primer for you, dear reader.

Getting Conversant in AI

AI is often seen as the silver bullet to healthcare’s many problems. It holds the promise of detecting diseases earlier and with more accuracy, standardizing clinical processes, and eliminating scheduling and paperwork. Ultimately, integrating artificial intelligence into clinical workflows can help ease provider burnout and improve patient outcomes.

Since 2016 alone, the FDA has approved 38 artificial intelligence algorithms for clinical use. Nearly half of these apply to radiology practice, the field most quickly adopting AI. Images and image reads easily lend themselves to interpretation by artificial intelligence.

Radiology is littered with studies demonstrating how algorithms and machine models are outperforming providers in detecting, characterizing, and monitoring disease. In the future, many predict artificial intelligence will continue to improve, exceeding humans in certain, more complex tasks.

Many radiologists are fearful that the widespread use of AI will result in machines replacing their jobs. However, artificial intelligence should be a supplement to the traditional workflow of providers, complimenting their work rather than eliminating it.

In order for radiologists to confidently implement artificial intelligence into clinical workflow, they must understand the different types of artificial intelligence and how these methods can be leveraged in radiology practice to dissuade false assumptions and hesitancy towards adoption. 

Natural Language Processing

Natural language processing (NLP) is one branch of artificial intelligence that allows computers to understand and interpret language. The technology can comb through reports, interpret spoken language, and generate structured text from free text.

A systematic review of NLP in radiology practice identified dozens of natural language processing methodologies applicable to clinical practice. Results demonstrated how the technology can be used for diagnostic surveillance, quality assessment, clinical support services, and cohort building for epidemiological studies.

All four of these aspects of care will help improve provider efficiency and care quality. Diagnostic surveillance allows the machine to alert providers when items have not been acted on, promoting efficient care and quick referral. The ability of natural language processing to transform free text into structured text can eliminate the administrative burden on providers, automating routine data entry and improving clinical workflow. Building a cohort allows researchers to quickly identify individuals for studies or allow providers to identify high-risk groups sooner.

Machine Learning

Another branch of artificial intelligence is machine learning. In this model, algorithms learn from a data set on how to solve a specific task. Data is inputted into the system, the machine learns from it, and uses that data to predict a desired outcome (e.g., risk of contracting a disease). Rather than being programmed to give a specific result from a data set, the machine learns how to predict outcomes using patterns in the data to identify which variables are most influential to the result.

In radiology practice, machine learning has a wide array of potential applications as the sheer amount of data radiology has is ripe for algorithm development. Machine learning processes can learn how to read and interpret a variety of medical images, including PET scans, MRIs, and CT scans. Quicker and more accurate reads of these imagines can identify disease faster and in an earlier stage with more accuracy.

Some studies indicate that machine learning can help improve overall workflow, communication, and patient safety if image read time is decreased and the quality of the image read is improved. Not only can this give providers more time to spend with patients instead of interpreting results, but it can also improve patient safety as more accurate reads will result in fewer false positive or false negative diagnoses.

Other research demonstrates how machine learning can help identify complex patterns in diagnosis. As a result, this artificial intelligence method can improve radiologists’ ability to make accurate decisions, identifying diseases more precisely and accurately.

Deep Learning/Neural Networks

Deep learning, often referred to as neural networks, is a type of machine learning where the algorithm is trained using a complex network of patterns similar to the brain’s neural network. The methodology has demonstrated high performance in identifying disease from imaging studies, taking the methods of machine learning one step further. Rather than learning from a set of inputs given to the machine from the algorithm developer, the algorithm learns from the data. It is a more advanced kind of machine learning that requires large datasets to train the algorithm and the data must be standardized as the machine has to learn where to identify irregularities in images.

With obvious applicability to radiology practice, research demonstrates deep learning models can be particularly useful in screening images or early-stage identification.

Deep learning algorithms, though, are at risk of the ‘black box’ problem if their neural networks are not extensively understood. ‘Black box’ AI is the development of an algorithm without an understanding of how the machine generated the output. Thus, many providers as uneasy trusting diagnostic and treatment decisions to an algorithm they do not understand.

If deep learning methods are to be more widely and confidently utilized in radiology practice, their interpretability will need to improve, and their methods must be clearly laid out. As with all artificial intelligence methodologies, the higher quality data inputted into generating the algorithm, the more accurate and more trusted the results will be.

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Behind trillions of institutional assets invested in hedge funds, private equity, and venture capital is a reporting process dominated today by manual pencil, paper, and typing.

Canoe Intelligence is for the first time using cutting-edge ocular character recognition (OCR) techniques, natural language processing technology, machine learning, and a thorough validation process to eliminate this error-prone, slow, and un-secure manual data entry process for institutional investors, pensions funds and family offices worldwide.

Today’s sophisticated allocators manage complex portfolios including scores of alternative investments such as hedge funds, private equity and venture capital. As a result, allocators are inundated with up to 50,000 documents annually, containing 200,000 or more critical transaction, valuation and performance data points. Firms have historically relied on teams of individuals or offshore groups to manually manage data extraction and document storage, leading to industry-wide frustrations due to errors, latency, spiraling costs, and lack of control.

Canoe solves these problems using a proprietary, patent-pending AI engine. Using the merits of traditional CRF modeling and cutting-edge deep learning algorithms, as well as OCR Canoe’s technology recognizes, validates and extracts unstructured investment data across asset classes, ownership structures, and document types. The data is organized digitally for easy and immediate access, and can be seamlessly fed into any portfolio reporting, accounting, and analytics platform.

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Google has become very interested in artificial intelligence in recent years, and particularly its applications for regular people. For example, here’s a load of experiments that it’s running involving machine learning.

Now, however, researchers at the Texas Advanced Computing Center have shown how artificial intelligence techniques can also deliver better search engine results. They’ve combined AI, crowdsourcing and supercomputers to develop a better system for information extraction and classification.

At the 2017 Annual Meeting for the Association of Computational Linguistics in Vancouver this week, associate professor Matthew Lease led a team presenting two papers that described a new kind of informational retrieval system.

Intelligent systems

“An important challenge in natural language processing is accurately finding important information contained in free-text, which lets us extract it into databases and combine it with other data in order to make more intelligent decisions and new discoveries,” Lease said.

“We’ve been using crowdsourcing to annotate medical and news articles at scale so that our intelligent systems will be able to more accurately find the key information contained in each article.”

They were able to use that crowdsourced data to train a neural network to predict the names of things, and extract useful information from texts that aren’t annotated at all.

In the second paper, they showed how to weight different linguistic resources so that the automatic text classification is better. “Neural network models have tons of parameters and need lots of data to fit them,” said Lease.

Consistently better results

In testing on both biomedical searches and movie reviews, the system delivered consistently better results than methods that didn’t involve weighting the data.

“We had this idea that if you could somehow reason about some words being related to other words a priori, then instead of having to have a parameter for each one of those word separately, you could tie together the parameters across multiple words and in that way need less data to learn the model,” said Lease.

He added: “Industry is great at looking at near-term things, but they don’t have the same freedom as academic researchers to pursue research ideas that are higher risk but could be more trans-formative in the long-term.”

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