Live Science described AI in What Is Artificial Intelligence?:
One of the standard textbooks in the field, by University of California computer scientists Stuart Russell and Google’s director of research, Peter Norvig, puts artificial intelligence in to four broad categories:
The differences between them can be subtle, notes Ernest Davis, a professor of computer science at New York University. AlphaGo, the computer program that beat a world champion at Go, acts rationally when it plays the game (it plays to win). But it doesn’t necessarily think the way a human being does, though it engages in some of the same pattern-recognition tasks. Similarly, a machine that acts like a human doesn’t necessarily bear much resemblance to people in the way it processes information.
• machines that think like humans,
• machines that act like humans,
• machines that think rationally,
• machines that act rationally.
Even IBM’s Watson, which acted somewhat like a human when playing Jeopardy, wasn’t using anything like the rational processes humans use.
Tough tasks
Davis says he uses another definition, centered on what one wants a computer to do. “There are a number of cognitive tasks that people do easily — often, indeed, with no conscious thought at all — but that are extremely hard to program on computers. Archetypal examples are vision and natural language understanding. Artificial intelligence, as I define it, is the study of getting computers to carry out these tasks,” he said….
Computer vision has made a lot of strides in the past decade — cameras can now recognize faces Other tasks, though, are proving tougher. For example, Davis and NYU psychology professor Gary Marcus wrote in the Communications of the Association for Computing Machinery of “common sense” tasks that computers find very difficult. A robot serving drinks, for example, can be programmed to recognize a request for one, and even to manipulate a glass and pour one. But if a fly lands in the glass the computer still has a tough time deciding whether to pour the drink in and serve it (or not).
Common sense
The issue is that much of “common sense” is very hard to model. Computer scientists have taken several approaches to get around that problem. IBM’s Watson, for instance, was able to do so well on Jeopardy! because it had a huge database of knowledge to work with and a few rules to string words together to make questions and answers. Watson, though, would have a difficult time with a simple open-ended conversation.
Beyond tasks, though, is the issue of learning. Machines can learn, said Kathleen McKeown, a professor of computer science at Columbia University. “Machine learning is a kind of AI,” she said.
Some machine learning works in a way similar to the way people do it, she noted. Google Translate, for example, uses a large corpus of text in a given language to translate to another language, a statistical process that doesn’t involve looking for the “meaning” of words. Humans, she said, do something similar, in that we learn languages by seeing lots of examples.
That said, Google Translate doesn’t always get it right, precisely because it doesn’t seek meaning and can sometimes be fooled by synonyms or differing connotations….
The upshot is AIs that can handle certain tasks well exist, as do AIs that look almost human because they have a large trove of data to work with. Computer scientists have been less successful coming up with an AI that can think the way we expect a human being to, or to act like a human in more than very limited situations…. https://www.livescience.com/55089-artificial-intelligence.html
NYU scientists used AI to diagnose PTSD which is short for Post-Traumatic Stress Disorder.
The National Institute of Mental Health defined PTSD:
Post-Traumatic Stress Disorder
Overview
PTSD is a disorder that develops in some people who have experienced a shocking, scary, or dangerous event.
It is natural to feel afraid during and after a traumatic situation. Fear triggers many split-second changes in the body to help defend against danger or to avoid it. This “fight-or-flight” response is a typical reaction meant to protect a person from harm. Nearly everyone will experience a range of reactions after trauma, yet most people recover from initial symptoms naturally. Those who continue to experience problems may be diagnosed with PTSD. People who have PTSD may feel stressed or frightened even when they are not in danger.
Signs and Symptoms
Not every traumatized person develops ongoing (chronic) or even short-term (acute) PTSD. Not everyone with PTSD has been through a dangerous event. Some experiences, like the sudden, unexpected death of a loved one, can also cause PTSD. Symptoms usually begin early, within 3 months of the traumatic incident, but sometimes they begin years afterward. Symptoms must last more than a month and be severe enough to interfere with relationships or work to be considered PTSD. The course of the illness varies. Some people recover within 6 months, while others have symptoms that last much longer. In some people, the condition becomes chronic.
A doctor who has experience helping people with mental illnesses, such as a psychiatrist or psychologist, can diagnose PTSD.
To be diagnosed with PTSD, an adult must have all of the following for at least 1 month:
• At least one re-experiencing symptom
• At least one avoidance symptom
• At least two arousal and reactivity symptoms
• At least two cognition and mood symptoms
Re-experiencing symptoms include:
• Flashbacks—reliving the trauma over and over, including physical symptoms like a racing heart or sweating
• Bad dreams
• Frightening thoughts
Re-experiencing symptoms may cause problems in a person’s everyday routine. The symptoms can start from the person’s own thoughts and feelings. Words, objects, or situations that are reminders of the event can also trigger re-experiencing symptoms.
Avoidance symptoms include:
• Staying away from places, events, or objects that are reminders of the traumatic experience
• Avoiding thoughts or feelings related to the traumatic event
Things that remind a person of the traumatic event can trigger avoidance symptoms. These symptoms may cause a person to change his or her personal routine. For example, after a bad car accident, a person who usually drives may avoid driving or riding in a car.
Arousal and reactivity symptoms include:
• Being easily startled
• Feeling tense or “on edge”
• Having difficulty sleeping
• Having angry outbursts
Arousal symptoms are usually constant, instead of being triggered by things that remind one of the traumatic events. These symptoms can make the person feel stressed and angry. They may make it hard to do daily tasks, such as sleeping, eating, or concentrating.
Cognition and mood symptoms include:
• Trouble remembering key features of the traumatic event
• Negative thoughts about oneself or the world
• Distorted feelings like guilt or blame
• Loss of interest in enjoyable activities
Cognition and mood symptoms can begin or worsen after the traumatic event, but are not due to injury or substance use. These symptoms can make the person feel alienated or detached from friends or family members.
It is natural to have some of these symptoms after a dangerous event. Sometimes people have very serious symptoms that go away after a few weeks. This is called acute stress disorder, or ASD. When the symptoms last more than a month, seriously affect one’s ability to function, and are not due to substance use, medical illness, or anything except the event itself, they might be PTSD. Some people with PTSD don’t show any symptoms for weeks or months. PTSD is often accompanied by depression, substance abuse, or one or more of the other anxiety disorders….
https://www.nimh.nih.gov/health/topics/post-traumatic-stress-disorder-ptsd/index.shtml
See, Recognizing PTSD Early Warning Signs, Matthew Tull, PhD https://www.verywellmind.com/recognizing-ptsd-early-warning-signs-2797569
Science Daily reported in Artificial intelligence can diagnose PTSD by analyzing voices:
A specially designed computer program can help diagnose post-traumatic stress disorder (PTSD) in veterans by analyzing their voices, a new study finds.
Published online April 22 in the journal Depression and Anxiety, the study found that an artificial intelligence tool can distinguish — with 89 percent accuracy — between the voices of those with or without PTSD.
“Our findings suggest that speech-based characteristics can be used to diagnose this disease, and with further refinement and validation, may be employed in the clinic in the near future,” says senior study author Charles R. Marmar, MD, the Lucius N. Littauer Professor and chair of the Department of Psychiatry at NYU School of Medicine.
More than 70 percent of adults worldwide experience a traumatic event at some point in their lives, with up to 12 percent of people in some struggling countries suffering from PTSD. Those with the condition experience strong, persistent distress when reminded of a triggering event.
The study authors say that a PTSD diagnosis is most often determined by clinical interview or a self-report assessment, both inherently prone to biases. This has led to efforts to develop objective, measurable, physical markers of PTSD progression, much like laboratory values for medical conditions, but progress has been slow.
Learning How to Learn
In the current study, the research team used a statistical/machine learning technique, called random forests, that has the ability to “learn” how to classify individuals based on examples. Such AI programs build “decision” rules and mathematical models that enable decision-making with increasing accuracy as the amount of training data grows.
The researchers first recorded standard, hours-long diagnostic interviews, called Clinician-Administered PTSD Scale, or CAPS, of 53 Iraq and Afghanistan veterans with military-service-related PTSD, as well as those of 78 veterans without the disease. The recordings were then fed into voice software from SRI International — the institute that also invented Siri — to yield a total of 40,526 speech-based features captured in short spurts of talk, which the team’s AI program sifted through for patterns.
The random forest program linked patterns of specific voice features with PTSD, including less clear speech and a lifeless, metallic tone, both of which had long been reported anecdotally as helpful in diagnosis. While the current study did not explore the disease mechanisms behind PTSD, the theory is that traumatic events change brain circuits that process emotion and muscle tone, which affects a person’s voice.
Moving forward, the research team plans to train the AI voice tool with more data, further validate it on an independent sample, and apply for government approval to use the tool clinically.
“Speech is an attractive candidate for use in an automated diagnostic system, perhaps as part of a future PTSD smartphone app, because it can be measured cheaply, remotely, and non-intrusively,” says lead author Adam Brown, PhD, adjunct assistant professor in the Department of Psychiatry at NYU School of Medicine.
“The speech analysis technology used in the current study on PTSD detection falls into the range of capabilities included in our speech analytics platform called SenSay Analytics™,” says Dimitra Vergyri, director of SRI International’s Speech Technology and Research (STAR) Laboratory. “The software analyzes words — in combination with frequency, rhythm, tone, and articulatory characteristics of speech — to infer the state of the speaker, including emotion, sentiment, cognition, health, mental health and communication quality. The technology has been involved in a series of industry applications visible in startups like Oto, Ambit and Decoded Health.” https://www.sciencedaily.com/releases/2019/04/190422082232.htm
Citation:
Artificial intelligence can diagnose PTSD by analyzing voices
Study tests potential telemedicine approach
Date: April 22, 2019
Source: NYU Langone Health / NYU School of Medicine
Summary:
A specially designed computer program can help to diagnose post-traumatic stress disorder (PTSD) in veterans by analyzing their voices.Speech‐based markers for posttraumatic stress disorder in US veterans
First published: 22 April 2019
https://doi.org/10.1002/da.22890
Preliminary findings from this study were presented at the 16th annual conference of the International Speech Communication Association, Dresden, Germany, September 6–10, 2015.
Charles R. Marmar
Corresponding Author
E-mail address: Charles.Marmar@nyulangone.org
http://orcid.org/0000-0001-8427-5607
Department of Psychiatry, New York University School of Medicine, New York, New York
Steven and Alexandra Cohen Veterans Center for the Study of Post‐Traumatic Stress and Traumatic Brain Injury, New York, New York
Marmar and Brown should be have considered joint first authors.
Correspondence Charles R. Marmar, M.D., Department of Psychiatry, New York University School of Medicine, 1 Park Avenue, New York, NY 10016. Email: Charles.Marmar@nyulangone.org
Background
The diagnosis of posttraumatic stress disorder (PTSD) is usually based on clinical interviews or self‐report measures. Both approaches are subject to under‐ and over‐reporting of symptoms. An objective test is lacking. We have developed a classifier of PTSD based on objective speech‐marker features that discriminate PTSD cases from controls.
Methods
Speech samples were obtained from warzone‐exposed veterans, 52 cases with PTSD and 77 controls, assessed with the Clinician‐Administered PTSD Scale. Individuals with major depressive disorder (MDD) were excluded. Audio recordings of clinical interviews were used to obtain 40,526 speech features which were input to a random forest (RF) algorithm.
Results
The selected RF used 18 speech features and the receiver operating characteristic curve had an area under the curve (AUC) of 0.954. At a probability of PTSD cut point of 0.423, Youden’s index was 0.787, and overall correct classification rate was 89.1%. The probability of PTSD was higher for markers that indicated slower, more monotonous speech, less change in tonality, and less activation. Depression symptoms, alcohol use disorder, and TBI did not meet statistical tests to be considered confounders.
Conclusions
This study demonstrates that a speech‐based algorithm can objectively differentiate PTSD cases from controls. The RF classifier had a high AUC. Further validation in an independent sample and appraisal of the classifier to identify those with MDD only compared with those with PTSD comorbid with MDD is required.
Here is the press release from NYU:
NEWS RELEASE 22-APR-2019
Artificial intelligence can diagnose PTSD by analyzing voices
Study tests potential telemedicine approach
NYU LANGONE HEALTH / NYU SCHOOL OF MEDICINE
VIDEO: NYU School of Medicine researchers say artificial intelligence could be used to diagnose PTSD by analyzing voices. view more
Credit: NYU School of Medicine
A specially designed computer program can help diagnose post-traumatic stress disorder (PTSD) in veterans by analyzing their voices, a new study finds.
Published online April 22 in the journal Depression and Anxiety, the study found that an artificial intelligence tool can distinguish – with 89 percent accuracy – between the voices of those with or without PTSD.
“Our findings suggest that speech-based characteristics can be used to diagnose this disease, and with further refinement and validation, may be employed in the clinic in the near future,” says senior study author Charles R. Marmar, MD, the Lucius N. Littauer Professor and chair of the Department of Psychiatry at NYU School of Medicine.
More than 70 percent of adults worldwide experience a traumatic event at some point in their lives, with up to 12 percent of people in some struggling countries suffering from PTSD. Those with the condition experience strong, persistent distress when reminded of a triggering event.
The study authors say that a PTSD diagnosis is most often determined by clinical interview or a self-report assessment, both inherently prone to biases. This has led to efforts to develop objective, measurable, physical markers of PTSD progression, much like laboratory values for medical conditions, but progress has been slow.
Learning How to Learn
In the current study, the research team used a statistical/machine learning technique, called random forests, that has the ability to “learn” how to classify individuals based on examples. Such AI programs build “decision” rules and mathematical models that enable decision-making with increasing accuracy as the amount of training data grows.
The researchers first recorded standard, hours-long diagnostic interviews, called Clinician-Administered PTSD Scale, or CAPS, of 53 Iraq and Afghanistan veterans with military-service-related PTSD, as well as those of 78 veterans without the disease. The recordings were then fed into voice software from SRI International – the institute that also invented Siri – to yield a total of 40,526 speech-based features captured in short spurts of talk, which the team’s AI program sifted through for patterns.
The random forest program linked patterns of specific voice features with PTSD, including less clear speech and a lifeless, metallic tone, both of which had long been reported anecdotally as helpful in diagnosis. While the current study did not explore the disease mechanisms behind PTSD, the theory is that traumatic events change brain circuits that process emotion and muscle tone, which affects a person’s voice.
Moving forward, the research team plans to train the AI voice tool with more data, further validate it on an independent sample, and apply for government approval to use the tool clinically.
“Speech is an attractive candidate for use in an automated diagnostic system, perhaps as part of a future PTSD smartphone app, because it can be measured cheaply, remotely, and non-intrusively,” says lead author Adam Brown, PhD, adjunct assistant professor in the Department of Psychiatry at NYU School of Medicine.
“The speech analysis technology used in the current study on PTSD detection falls into the range of capabilities included in our speech analytics platform called SenSay Analytics™,” says Dimitra Vergyri, director of SRI International’s Speech Technology and Research (STAR) Laboratory. “The software analyzes words – in combination with frequency, rhythm, tone, and articulatory characteristics of speech – to infer the state of the speaker, including emotion, sentiment, cognition, health, mental health and communication quality. The technology has been involved in a series of industry applications visible in startups like Oto, Ambit and Decoded Health.”
###
Along with Marmar and Brown, authors of the study from the Department of Psychiatry were Meng Qian, Eugene Laska, Carole Siegel, Meng Li, and Duna Abu-Amara. Study authors from SRI International were Andreas Tsiartas, Dimitra Vergyri, Colleen Richey, Jennifer Smith, and Bruce Knoth. Brown is also an associate professor of psychology at the New School for Social Research.
The study was supported by the U.S. Army Medical Research & Acquisition Activity (USAMRAA) and Telemedicine & Advanced Technology Research Center (TATRC) grant W81XWH- ll-C-0004, as well as by the Steven and Alexandra Cohen Foundation.
Media Inquiries:
Jim Mandler
(212) 404-3500
jim.mandler@nyulangone.org
Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.
Resources:
Artificial Intelligence Will Redesign Healthcare https://medicalfuturist.com/artificial-intelligence-will-redesign-healthcare
9 Ways Artificial Intelligence is Affecting the Medical Field https://www.healthcentral.com/slideshow/8-ways-artificial-intelligence-is-affecting-the-medical-field#slide=2
Where information leads to Hope. © Dr. Wilda.com
Dr. Wilda says this about that ©
Blogs by Dr. Wilda:
COMMENTS FROM AN OLD FART©
http://drwildaoldfart.wordpress.com/
Dr. Wilda Reviews ©
http://drwildareviews.wordpress.com/
Dr. Wilda ©
https://drwilda.com/
drwilda 