Abstract

Deep Speech AI and Decoding Mental Health

Author: EINSTEIN NTIM

By the year 2030 depression and mental health are poised to be the biggest diseases globally, and suicide is expected to be the leading cause of death. As a result, global stakeholders anticipate a sustainable solution that leverages tomorrow’s technologies to diffuse the accelerating magnitude of this global epidemic. Enabled Health Tech is the current and future solution for mental health. Our mission is "Decoding Mental Health" via Deep Speech and Deep Learning Artificial Intelligence technology. How? We integrate medical health data, user audio input summaries and social media integration to help our beta bot system anticipate mental issues before it becomes a problem. This process ensures that we get to the root cause of mental health issues as opposed to diagnosing it with ineffective treatments after its too late. Our current process is simple. We first make it easy to centralize and synthesize mental health data from health care facilities which includes: Electronic Health Records (EHR) and Electronic Medical Records (EMR) at hospitals, mental health facilities, nursing homes, care homes and behavioural management institutions. Second, we allow patients and professionals to input audio feedback commentary on their mental well being into the system. Thirdly, we leverage Deep Speech and Deep Learning technologies to transcribe verbal user input, and cross reference it with mental health records. This whole procedure is customized and organized for each patient addressing decision support and health centre monitoring inefficiencies. . Our procedure not only alleviates mental health issues but also addresses other health related concerns. For example, current documentation systems are insufficient to mine data and create information and intelligence in a systematic way that can enhance efficiency of care providers. Our system, makes it easier to input and organize documentation data. Another example, is that current solutions don't include people with mental health issues in the care process, which disempowers the patient to contribute by documenting their own mental state. Once again, our system provides a medium for mental health patients to input their feedback sentiments on a consistent basis. A final example, is health care hospitals can now save costs from using a tech platform that automates documentation and patient reports. No need for filing, paper work grouping or even personnel to manage the whole documentation and filing process. We currently have an intuitive beta app for our current user network. Some of the biggest barriers to scale is industry bias and precaution regarding new technologies, privacy breach of mental health data and marketing competition from current industry solutions. We aim to scale with consumer case study success stories and institutional partnerships with health care centers.