Teens and Technology and How It Benefits Society

August 05, 2019

10 min read

Alison Cheung
Posted on August 05, 2019

10 min read

Engineered Predictive Medicine’s influence in minimising
the impacts of autoimmune and viral conditions

Indigo Hennig

Alison Cheung

Currently, Australia’s and other countries’ health care models implemented after World War 2 are treating their populations with a ‘reactive’ medical system. This is where a patient feels sick and the doctor then reacts to the symptoms and behaviours based on the given clinical information, blood tests or medical imaging in order to ascertain a diagnosis and then treatment (Wise, MacIntosh, Rajakulendran, & Khayat, 2016). A reactive medical system is flawed resulting in a human and financial cost as it treats problems as they arise rather than preventing the complications from manifesting.

I propose that engineering and science can be used in ‘predictive’ medicine to minimise the adverse complications of autoimmune and viral diseases. This will create a health system model that improves the prevention techniques of autoimmune and viral diseases rather than addressing them when they occur. I was diagnosed with Type 1 diabetes when I was 2 years old. My blood glucose was tested, and I was diagnosed before being admitted into intensive care as I demonstrated diabetic symptoms. Unfortunately, one third of children diagnosed with diabetes are admitted to intensive care with Diabetic Ketoacidosis or in a coma (US National Library of Medicine National Institutes of Health, 2016) (Rashed, 2011) (Cameron, Kilov, & Audehm, 2016). I passionately believe with a predictive medicine model using engineering and science, it would decrease intensive care admissions and save lives.

Engineering could assist the predictive healthcare model by supplying new technologies such as artificial intelligence to monitor body systems and enhance people’s own health management. Examples are toilets or toothbrushes engineered to sample body tissue and fluids and give readings as predictors of certain diseases. In the Type 1 diabetes community, urine samples could be monitored via a sensor in the toilet and warn

Engineering could assist the predictive healthcare model by supplying new technologies such as artificial intelligence to monitor body systems and enhance people’s own health management. Examples are toilets or toothbrushes engineered to sample body tissue and fluids and give readings as predictors of certain diseases. In the Type 1 diabetes community, urine samples could be monitored via a sensor in the toilet and warn potential patients of higher than normal glucose readings and ketones. Toothbrushes could indicate antibody or bacterial levels and predict viral infections preventing spread of the disease. This information could be correlated and produce a patient health report via mobile phone with recommendations such as to see a doctor.

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Eric Dishman stated in a TEDMED that phones are revolutionary to the healthcare system and could use algorithms to detect behavioural cues (Dishman, 2009).

This technology could also be used to detect reduced cognitive functions such as slurred speech which can be a sign of many autoimmune and viral conditions such as low blood glucose in Diabetes. Apps on watches could detect cardiac anomalies or breathing difficulties and report recommendations. Statistics, reference ranges and predictive analytics from clinical information could generate alarms for body system malfunctions and

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Engineering could assist the predictive healthcare model by supplying new technologies such as artificial intelligence to monitor body systems and enhance people’s own health management. Examples are toilets or toothbrushes engineered to sample body tissue and fluids and give readings as predictors of certain diseases. In the Type 1 diabetes community, urine samples could be monitored via a sensor in the toilet and warn potential patients of higher than normal glucose readings and ketones. Toothbrushes could indicate antibody or bacterial levels and predict viral infections preventing spread of the disease. This information could be correlated and produce a patient health report via mobile phone with recommendations such as to see a doctor.

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Engineering could assist the predictive healthcare model by supplying new technologies such as artificial intelligence to monitor body systems and enhance people’s own health management. Examples are toilets or toothbrushes engineered to sample body tissue and fluids and give readings as predictors of certain diseases. In the Type 1 diabetes community, urine samples could be monitored via a sensor in the toilet and warn potential patients of higher than normal glucose readings and ketones. Toothbrushes could indicate antibody or bacterial levels and predict viral infections preventing spread of the disease. This information could be correlated and produce a patient health report via mobile phone with recommendations such as to see a doctor.

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Alison Cheung

Alison Cheung is a current postgraduate of Cambridge University, working to achieve an MSc in Psychological and Behavioural Sciences. She attends Caius College and is an experienced mentor, having worked with Immerse in 2018. She has also delivered workshops to Immerse participants on ‘Applying to study Sciences at University’

Alison Cheung is a current postgraduate of Cambridge University, working to achieve an MSc in Psychological and Behavioural Sciences. She attends Caius College and is an experienced mentor, having worked with Immerse in 2018. She has also delivered workshops to Immerse participants on ‘Applying to study Sciences at University’