- Healthcare Innovation editors
Doctors to use Artificial Emotional Intelligence to detect first signs of heart attack
BPU Holdings recently announced joint work using Artificial Emotional Intelligence (AEI) to harness better health and prevent sudden cardiac death, as well as assist with dementia. The company’s initiative is an outgrowth of its work with the United States National Science Foundation (USNSF) in partnership with the University of Arizona.
Integral to the project are researchers Dr. Zain I Khalpey and Dr. Salim Hariri. Together they are constructing a medical script to bring aiMei Framework to life. aiMei Framework is BPU’s automated assistant using Artificial Emotional Intelligence combined with BPU’s Operating System, AEIOS, and Natural Language Processing (NLP) to create a Virtual Nurse Assistant (VNA) and a Digital Patient Assistant (DPA).
Dr. Khalpey, a Surgical Director of Heart Transplant, a Fulbright Scholar and Honoree of the Hunterian Medal for Surgery at the Royal College of Surgeons in London, explains how aiMei Framework can be used by patients to help diagnose themselves with the first signs of a heart attack.
“Imagine you’re an athlete going out for a run and your heart skips a beat at three miles and it suddenly fires on the wrong part of the electrical part of your rhythm adrenaline surge. You go into Ventricular Fibrillation – you have sudden cardiac death. Alternatively, imagine the night before your wearable detects a problem with the heart rhythm. The wearable also reports you feel emotionally under the weather. Or, given the insights from the device the night before, you realize now that you’ve been having this for a while? Not only are you now diagnosing a condition, but also you are predicting the outcome.”
Self-awareness, which is one of aiMei’s Emotional Intelligence components, is able to educate the patient to help understand his or her emotions and physical warnings to make decisions towards better health.
Communication, data, and time are critical for the doctor-patient relationship, according to Khalpey. Too often, nurses in acute and subacute care in hospitals and skilled nursing facilities perform long, cyclical tasks taking up 35% of their time on charting. For dementia patients, windows of unsupervised care are when falls occur. Delirium affects up to 50% of hospitalized seniors and costs the US over $164 Billion per year and $182 Billion in Europe. Additionally, both heart and dementia patients have life-threatening seconds under distress, which can be fatal.
Using aiMei Framework, a Virtual Nurse Assistant and Digital Patient Assistant engage patients in live chatbots that are able to track their daily patterns, remind and track medication, and even assess physical symptoms outside the doctor’s office. Should emergencies occur, the wearable can alert hospitals and their physicians within minutes.
Dr. Hariri, a Professor in the Department of Electrical and Computer Engineering at the University of Arizona as well as Director of the NSF Center for Cloud and Autonomic Computing Center (CAC), states, “AEI can be utilized to interact with patients 24/7 to understand their emotional state, stress level and can even predict major heart failures before they occur. AEI technology will enable us to notify doctors ahead of time so they can take proactive actions that will save a life and will lead to significant improvement in the quality of healthcare.”
Goals for aiMei’s related services are Stress Level Detection Using Heartbeat Data of a Subject and Text Analysis to Detect Emotions. Dr. Khalpey further elaborates, “The problem with IBM Watson and its misdiagnosis with cancer was the lack of emotion in the data; there was no personalized data in their algorithms. The pathways didn’t talk to each other. The link between artificial intelligence – the predictability of it, harnessing the data in an intelligent way, along with emotions, fuses the concept of artificial emotional Intelligence (AEI) and artificial intelligence (AI).”
With AEI and the research of the USNSF, BPU is dedicated to bringing aiMei Framework to life – while saving future lives. BPU anticipates that the aiMei Framework will be ready for clinical trials within two to three years.
