"We start with this huge object of all these coupled-together differential equations; then we're using machine learning to turn it into something so small you can count it on your fingers," says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute's Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy. The formidable problem concerns how electrons behave as they move on a gridlike lattice. When two electrons occupy the same lattice site, they interact. This setup, known as the Hubbard model, is an idealization of several important classes of materials and enables scientists to learn how electron behavior gives rise to sought-after phases of matter, such as superconductivity, in which electrons flow through a material without resistance. The model also serves as a testing ground for new methods before they're unleashed on more complex quantum systems.

  As  per WHO one in   four people  in the world  will be affected by mental or neurological  disorder at some point in  their lives. Around  450 million people  currently suffer  from such conditions, placing  mental disorders  among the leading causes of  ill health and disability  worldwide. Mood Disorders – These disorders are also called   affective disorders which  involves persistent  feeling  of sadness or  period of  feeling overly happy , or fluctuations  from extreme happiness to  extreme  sadness. The most common  mood disorders are  depression , bipolar disorder and cyclothymic disorder. Another type of  disorder is the  Psychotic  disorder – This involves  distorted awareness and thinking. Two of the  main common  symptoms of psychotic disorders are  hallucination, where the patient experiences  images or  sounds that are  not real. Delusions- These are false  fixed beliefs that the  patient accepts as  true,  despite the  evidence to the contrary . Schizophrenia is an example of  psychotic  disorder. These  disorders cause detachment from reality. The  question today is how technology advancement in the  field of Artificial Intelligence can help in  the diagnosis of the mental disorders. Therefore it is important to understand what Artificial Intelligence is ? Artificial Intelligence  is a  software  program which can think and act like human . Basically we  are designing  programs which  acts like  our brain but with a  higher level of computing power. The Artificial  Intelligent program have multiple tools and subsets  which have  different  functions, but they combine  together to  create an Artificial Intelligent  program. One of the important  subset of AI is Machine Learning  – Machine Learning are algorithms that learn complex patterns from data and make predictions from it. Machine learning  programs have the following  steps:- It takes  data to  train the system. This  data  can be in the form of structured or unstructured data .  The data  can be  extracted from the data base.  It can be in  the form of text, it can be in the form  of images. After  processing this  data , the  algorithm understands   and learns the pattern  shown by this vast  data . It  can classify  the data that it has not seen before. Machine learning  is    trained by the features  or the traits of the  subjects. In  case of  patients  who suffer  from  a mental health issue, this data can be in the form of text data  that  a  patient may write on social media  site,  the  spoken data , language and data  captured  through spoken media and then converted to  text through the use of Natural Language processing. Artificial intelligent program  can be used  to detect  the Depression , we  take an example  of a  research paper where  the  researchers  accessed the Facebook status which  was posted by  683 patients  who visited  a large urban academic  emergency  department, 114 of whom  had a diagnosis of  depression in their medical record. The research  was   undertaken  to detect and predict the diagnosis of the  depression problem from the language used in the Facebook  posts. Prediction performances of future diagnosis of depression in the EMR based on demographics and Facebook posting activity, reported as cross-validated out-of-sample AUCs. With the  Facebook data  in  hand  and using the  ML model, researchers  could identify the  depressed patients with a  fair degree of  accuracy  at AUC=0.69, approximately matching  the accuracy of screening surveys bench marked  against medical  records. They  found that the language  predictor  of depression include emotional(sadness), interpersonal(loneliness, hostility) and cognitive(preoccupation with  self, rumination) process.  From the  result , it was  also observed that the  user  who  ultimately had a diagnosis of depression  used   more  first  person singular  pronouns( I , My , me)suggesting a preoccupation with self.  The  results  show that the  Facebook  language based prediction model  performs similarly to  screening surveys in identifying the patients with   depression when using  diagnostic codes in the EMR to identify diagnosis of depression. Growth of social media and the continuous improvement of machine learning  algorithm  suggest that social media based  screening methods for depression  may become  increasingly  feasible and more accurate.  The present analysis  therefore also suggests that the  social media based  prediction of  future  depression status  may be possible as  early as  3 months before  the  first documentation of depression  in the medical record.  Novel avenues are also becoming  available to  detect depression.  These methods also include  algorithmic analysis of  phone sensors , GPS position on the phone,  facial expression in images and videos shared on social platforms. The predictive model of Logistic  regression was  used. Ten language topics most positively associated with a future depression diagnosis controlling for demographics (*P < 0.05, **P < 0.01, and ***P < 0.001; BHP < 0.05 after Benjamini–Hochberg correction for multiple comparisons). As per WHO close to 800 000 commit suicide every year. Some of the companies are also involved in building healthcare applications. Ginger is a  chat application that  is used by the employers that provide direct  counselling to its employees. The algorithm  analyses the words someone uses and then relies on the training from more than  2 billion behavioural data samples , 45 million chat  messages and  2 million  clinical assessments to provide a recommendation. The CompanionMX system has an app that allows patients being treated with depression, bipolar disorders, and other conditions to create an audio log where they can talk about how they are feeling. The AI system analyses the recording as well as looks for changes in behaviour for proactive mental health monitoring. Bark, a parental control phone tracker app, monitors major messaging and social media platforms to look for signs of cyber bullying, depression, suicidal thoughts and sexting on a child’s phone. Advantages of Artificial Intelligence in Healthcare Support Mental Health professionals –  AI  can  act as  a support  for the  health professionals in doing their  jobs. Algorithms can analyse data much faster than humans can suggest possible treatments, monitor a patient’s progress and  alert the human  professional  to any concern. 24/7 access- Due to lack of human mental health professionals, it can  take months to take an appointment. AI provides a tool that an individual can access without waiting for an appointment. Not expensive –  The cost of care prohibits some  individuals from seeking help. This is more affordable. Comfort  talking to a bot-   It is easier to disclose  an information to  a bot than to a human. Cognitive computers will analyse a patient’s speech or written words to look for tell-tale indicators found in language, including meaning, syntax and intonation. Combining the results of these measurements with those from wearable devices and imaging systems (MRIs and EEGs) can paint a more complete picture of the individual for health professionals to better identify, understand and treat the underlying disease, be it Parkinson’s, Alzheimer’s, Huntington’s disease, PTSD or even underdevelopment conditions such as autism and ADHD. In a study  with Columbia University psychiatrists, were able to predict, with 100 percent accuracy, who among a population of at-risk adolescents would develop their first episode of psychosis within two years. In other research with our Pfizer colleagues, we’re using only about 1 minute of speech from Parkinson’s patients to better track, predict and monitor the disease. We’re already seeing results of nearly 80 percent accuracy. In five years, we hope to advance the study of using words as windows into our mental health. IBM is building an automated speech analysis application that runs off a mobile device. By taking approximately one minute of speech input, the system uses text-to-speech, advanced analytics, machine learning, natural language processing technologies and computational biology to provide a real-time, overview of the patient’s mental health. Artificial Intelligence will play a pivotal role in creating ground-breaking tools to analyse and detect mental health problems and will play a substantially positive role in increasing the treatment coverage by early diagnosis and possibly be able to reduce the death rates due to mental health problems. REFERENCE Eichstaedt, Johannes C., et al. “Facebook Language Predicts Depression in Medical Records.” PNAS, National Academy of Sciences, 30 Oct. 2018, www.pnas.org/content/115/44/11203. Marr, Bernard. “The Incredible Ways Artificial Intelligence Is Now Used In Mental Health.” Forbes, Forbes Magazine, 22 May 2019, www.forbes.com/sites/bernardmarr/2019/05/03/the-incredible-ways-artificial-intelligence-is-now-used-in-mental-health/#74cf5137d02e. Cecchi, Guillermo. “With AI, Our Words Will Be a Window into Our Mental Health.” With AI, Our Words Will Be a Window into Our Mental Health- IBM Research, www.research.ibm.com/5-in-5/mental-health/. IBM Research Editorial Staff. “IBM 5 in 5: With AI, Our Words Will Be a Window into Our Mental Health.” IBM Research Blog, 5 Jan. 2017, www.ibm.com/blogs/research/2017/1/ibm-5-in-5-our-words-will-be-the-windows-to-our-mental-health/. Bedi, Gillinder, et al. “Automated Analysis of Free Speech Predicts Psychosis Onset in High-Risk Youths.” Nature News, Nature Publishing Group, 26 Aug. 2015, www.nature.com/articles/npjschz201530. WHO. “Mental Disorders Affect One in Four People.” World Health Organization, World Health Organization, 4 Oct. 2001, www.who.int/whr/2001/media_centre/press_release/. Goldberg, Joseph. “Mental Health: Types of Mental Illness.” WebMD, WebMD, 6 Apr. 2019, www.webmd.com/mental-health/mental-health-types-illness#1.

The question today is how technology advancement in the field of Artificial Intelligence can help in the diagnosis of the mental disorders.

These days' companies are implementing Artificial Intelligence to become more diverse. Artificial Intelligence is helping the business to find solutions for complex problems in a more human-like manner. Form starting Artificial Intelligence is poised to have a transformational impact on business in different dimensions. It helps to lower the costs associated with complex processes. The concepts of artificial intelligence have been around for a long time, but now AI is implemented to transform the way business is done.

So, how do we overcome "notification overload" in healthcare? We need the right kind of tools. We need tools that can automate the complex interdisciplinary workflows that result in much of the notification burden. AI-assisted care plan creation, sharing platforms to involve all stakeholders (physicians, home health agencies, care coordinator nurses, patients, and health plans), are a part of the answer. Regulatory and policy changes (within institutions and within health plans) will be needed to facilitate this, once the tools are available.