HOSA Biomedical Debate 23-24 Machines
Exam Questions and Answers
MYCIN (1970) - ANSWER-- Helps with diagnosing bacterial infections by analyzing patient symptoms
and providing treatment recommendations
- Limitations: lack flexibility and inability to adapt to new info
Neural Networks (1980's-1990's) - ANSWER-- Uses interconnected nodes to analyze data and identify
patterns
-Quick medical reference: Patient data to predict possibilities of medical conditions
-Limitations: availability and quality of data , often incomplete or inaccurate
Support Vector Machines - ANSWER-- Classification and regression analysis
- Diagnosis of breast cancer, detection of Alzheimer's disease, prediction of patient outcomes.
Deep Learning (DL) - ANSWER-- 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
Disease Management (Application of CDSS) - ANSWER-- Manage chronic diseases by analyzing
patient data and recommending treatment plans, lifestyle changes, and self management strategies
Improved patient outcomes (Benefit of CDSS) - ANSWER-More accurate and timely diagnosis by
selecting appropriate treatments and monitoring patients
Medical Imaging - ANSWER-- Diagnosis and treatment of many medical conditions
- Can improve accuracy and efficiency of medical imaging by automating image analysis and
providing more accurate diagnoses
- Can analyze large data sets of medical records and genetic data to get effective and personalized
treatments.
- AI potential: Drug discovery (AI identifies drug candidates faster and accurately than traditional
method)
, - Advances: able to identify subtle patterns and features that humans miss
- Limitations: ability of radiologists to interpret complex images
Rule Based System - ANSWER-- Operates on predefined rules and decision-making algorithms to
analyze data & generate recommendations/decisions
- Can be utilized to analyze electrocardiography (ECG) data for diagnosing heart disease
- Advantages: transparency and interoperability, adaptable and can be easily updated with new
knowledge
- Limitations: Heavily rely on accuracy and completeness of predefined rules, Struggle to handle
uncertainty or ambiguity
Robotic Process Automation - ANSWER-- Automates rules based tasks by mimicking human
interactions with computer systems and applications
- Perform tasks that require little to no cognitive decision making
- Used to automate the process of verifying patient insurance eligibility
- Programmed to extract patient data from health records, interact w/ insurance portals, and
perform verification steps
- Advantages: Can work with existing It infrastructure w/o needing extensive change or integrations,
can work 24/7 without interruptions, and can interact with apps and systems through same user
interfaces as humans
- Limitations: not good for tasks with complex decision making, unstructured data, or needing high
level cognitive abilities
Machine Learning (ML) - ANSWER-- Uses training algorithms on large datasets to identify patterns
and make predictions
- Used for diagnostic imaging, drug discovery, and personalized treatment recommendations
Natural Language Processing - ANSWER-- Analyze electronic health records, patient notes, other text
based data to identify potential trends and patterns
Disease Management (Application of CDSS) - ANSWER-- Manage chronic diseases by analyzing
patient data and recommending treatment plans, lifestyle changes, and self management strategies
Improved patient outcomes (Benefit of CDSS) - ANSWER-More accurate and timely diagnosis by
selecting appropriate treatments and monitoring patients
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