nDatalyze Corp.
CSE:NDAT OTC:NDATF
Machine-learning-based,
objective mental health assessments.
May 10, 2024
CSE:NDAT OTC:NDATF
Forward Looking Statements
Certain information set forth in this presentation contains "forward-looking information", including "future-oriented financial information" and "financial outlook", under applicable securities laws (collectively referred to herein as forward-looking statements). Except for statements of historical fact, the information contained herein constitutes forward-looking statements and includes, but is not limited to, the (i) projected financial performance of the Company; (ii) completion of, and the use of proceeds from, the sale of the shares being offered hereunder; (iii) the expected development of the Company's business, projects, and joint ventures; (iv) execution of the Company's vision and growth strategy, including with respect to future M&A activity and global growth; (v) sources and availability of third-party financing for the Company's projects; (vi) completion of the Company's projects that are currently underway, in development or otherwise under consideration; (vi) renewal of the Company's current customer, supplier and other material agreements; and (vii) future liquidity, working capital, and capital requirements. Forward-looking statements are provided to allow potential investors the opportunity to understand management's beliefs and opinions in respect of the future so that they may use such beliefs and opinions as one factor in evaluating an investment. These statements are not guarantees of future performance and undue reliance should not be placed on them. Such forward-looking statements necessarily involve known and unknown risks and uncertainties, which may cause actual performance and financial results in future periods to differ materially from any projections of future performance or result expressed or implied by such forward-looking statements. Although forward-looking statements contained in this presentation are based upon what management of the Company believes are reasonable assumptions, there can be no assurance that forward looking statements will prove to be accurate, as actual results and future events could differ materially from those anticipated in such statements. The Company undertakes no obligation to update forward-looking statements if circumstances or management's estimates or opinions should change except as required by applicable securities laws. The reader is cautioned not to place undue reliance on forward-looking statements
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The Problem
Under-informed subjective mental condition mis/over-diagnosis results in mis/overprescription leading to poor outcomes with the resultant feedback loop clogging the mental health system and lowering the overall quality of societal mental health. Here are example mis/overdiagnosis rates:
71% | 65.9% | 85.8% | 92.7% |
General Anxiety Disorder (GAD) | Major Depressive Disorder | Panic Disorder | Bipolar Disorder |
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3184591/#:~:text=Misdiagnosis%20rates%20reached%2065.9%25%20for,97.8%25%20for%20social%20anxiety%20disorder.
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The Solution
YMI is a human/machine-learning hybrid "mind" designed to significantly lower mental condition mis/overdiagnosis & mis/overprescription rates of common mental conditions. The process involves using a proprietary Reference Database to establish a societal baseline for multiple mental conditions, thereafter determining an individual's divergence from the baseline in order to predict probabilities of developing mental conditions over time. The preemptive process use a purpose-built Reference Database to identify potential developing conditions so they can be treated before they become debilitating.
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YMI combines:
with…
01 "Memories"
A proprietary Reference Database of:
- Genetic expressions
- Childhood development/Parenting styles
- Traumas & Allergies
- diagnosed mental conditions
- Physical conditions
- Lifestyle choices and life experiences
02 An advanced, predictive psychiatric assessment system
Supervised AI/machine-learningis guided by multiple highly-qualified Psychotherapists/data scientists. Evidence-based data is used to predict cause of an individual's current/future probabilities of multiple mental health conditions.
to predict…
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Our Machine-learning Guidance
DR. KEITH COURTNEY | DR. CAROL MALEC | DR. ANDREA PROTZNER | DR. KELTIE DUGGIN | JAMES DURWARD |
Chief Psychiatric Officer
Keith is a double board- certified psychiatrist and a Clinical Assistant Professor at the University of Calgary and was a faculty member at the University of Vermont, Dartmouth Medical School, and the University of Nevada.
Ph.D,MSc,BScN
Carol has 22 years of private practice counselling experience
and is a Certified Counsellor
with Canadian Counselling and
Psychotherapy Association
(CCPA). She's also a guest lecturer at the University of
Calgary.
PhD, EEG Specialist
Andrea is an EEG specialist, a member of the Hotchkiss Brain
Institute, and an Associate
Professor at the University of Calgary. As the head of the
University's Brain Dynamics Lab, she's researching the link
between neural dynamics and
cognition.
M.D.
Keltie is a clinical lecturer at the
University of Calgary and a hospitalist physician leader in
the neurorehabilitation units at the Foothills Medical Centre
and Vernon Fanning Centre.
Director,
Chief Technical Officer
James' expertise lies in data science and technical product experience, including image analysis, pattern recognition, and specialized databases.
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Current Mental Condition Probabilities…
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and predicted trajectories over time.
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Our Mindbalanced acquisition - US market entry point
https://mindbalanced.org
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Clinical Study
nDatalyze has launched an independent clinical study with York University. This study is being conducted to determine the benefits of incorporating DNA data to enhance YMI's causal accuracy. The Principal Investigator is Dr. Dale Stevens, PhD, York University Neuroscience
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Disclaimer
nDatalyze Corp. published this content on 11 May 2024 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 11 May 2024 01:37:07 UTC.