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HOSA Biomedical Debate 23-24 Machines Exam – Questions and Answers
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HOSA Biomedical Debate 23-24 Machines Exam – Questions and Answers
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HOSA Biomedical Debate 23-24 MachinesExam – Questions and Answers
Atomwise - -- Uses AI to predict potential new drug candidates against
specific protein targets (drug candidates for ebola, sclerosis, etc.)
-Benevolent AI - -- Uses AI to better drug discovery and development
process by analyzing large amounts of biomedical data
- Natural processing ML alg extracts insights from data and identify drug
candidates for diseases
-Challenges in healthcare operations and resource optimization - --
resourceeee constraints: Limited staff, equipment, facilities
- fluctuating demand: difficult for healthcare providers to plan and allocate
resources effectively
- patient safety and quality: Maintain high level of patient safety and quality
while also managing their operations and resources effectively
- cost control: Manage cost effectively to maintain financial sustainability and
deliver high value care
-Challenges of Patient Monitoring and management systems - -- Data
quality and standardization: hard to integrate data because data from
different sources heterogeneous and use different formats and standards
- Privacy and Security: Ensure patient privacy and data security, idk ai is iffy
ab this yk ppl dont trust it
- User acceptance and adoption: Complex and training to use may change
workflow of healthcare workers
- Cost and sustainability: expensive, ensure sustainable over long term
- Regulatory compliance: Patient monitoring and management systems must
comply with regulatory requirements
-Chronic Disease management (Application of patient monitoring &
management systems) - -Hcp identifying early warning sign of disease
exacerbation and provide timely intervention to prevent hospital isolation or
other advance outcomes
-Clinical Decision making (Application of patient monitoring & management
systems) - -- Patient diagnosis, treatment plans, disease management
- Analyzing patient data in real time
- Provide healthcare providers with timely and relevant information to inform
clinical decisions
-Convolutional Neural Networks (CNN) - -- DL Algorithm
, Consist of layers of interconnected neurons trained to identify patterns and
features in images
- Used for tasks such as image segmentation, best detection, classification.
-Cost Savings (Benefit of CDSS) - -- Avoiding unnecessary test, procedures,
and treatments
- Identifying cost effective treatment options
-Data Guilty and Availability (Challenges of CDSS) - -- Rely on high quality
comprehensive patient data to make accurate recommendations (Bad when
data not gud)
-Decision Trees - -- ML Algorithm
- Works by repeatedly proportioning inputted data into subjects based on
values of different features
- Can predict likelihood of different medical conditions based on imaging
data
-Deep Learning (DL) - -- Uses artificial neural networks to analyze large
datasets and identify complex patterns in data and make accurate
predictions
- For medical imaging, drug discovery, and personalized medicine
-Diagnosis (Application of CDSS) - -- accurate and timely diagnosis
- analyzing patient data and providing recommendations based on
established guidelines and best practices
-Disease Management (Application of CDSS) - -- Manage chronic diseases
by analyzing patient data and recommending treatment plans, lifestyle
changes, and self management strategies
-Electronic health records (EHR) - -- Digital records of patient health history
and current health status
- Treat patient data over time and provide insights into patient health trends
and risk.
-Examples of CDSS - -- Uptodate
- ibm Watson for Technology
- Corner millennium
- Epic deterioration index
^^^^ All analyze data to give treatment recommendations to healthcare
-Expert Systems - -Replicate decision making capabilities of human experts
-future of healthcare operations and resource optimization - -- Artificial
intelligence: ML alg analyze large volumes of data and make predictions
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