HJBR Nov/Dec 2024

HEALTHCARE JOURNAL OF BATON ROUGE  I  NOV / DEC 2024 51 Dana Lawson, DNP, MHA, MSN, APRN, CCM Senior Vice President of Population Health Clinical Operations and Health Equity Louisiana Healthcare Connections MACHINE LEARNING IN HEALTHCARE MLis a branch ofAI that focuses on devel- oping algorithms that learn patterns from data and make predictions or decisions without being explicitly programmed for specific tasks. Traditional machine learn- ing techniques include: Neural network architectures: • Supervised learning: Algorithms learn from labeled datasets to predict out- comes. Common techniques include decision trees, support vector ma- chines (SVM), and logistic regression. • Unsupervised learning: Algorithms find hidden patterns in data without predefined labels, such as clustering algorithms (e.g., k-means clustering). • Reinforcement learning: Models learn by interacting with an environment and receiving feedback, optimizing decision-making through trial and error. 2 Applications in healthcare: • Predictive analytics: Predicting pa- tient outcomes (e.g., risk of readmis- sion, disease progression); or early detection of sepsis or complications based on patient vitals and historical data. • Clinical decision support systems (CDSS): Assisting clinicians in diag- nosing complex diseases based on patient history and symptoms. • Personalized medicine: Tailoring treatments to patients based on de- mographic and clinical data, improv- ing therapeutic efficacy. Genomics and pharmacogenomics: An- alyzing genomic data to predict gene-dis- ease associations or patient-drug re- sponse. 3 Advantages: • Interpretability: Traditional ML mod- els such as decision trees and logistic regression offer interpretability, mak- ing them easier to understand and trust in clinical settings. • Efficiency: ML models are less com- putationally intensive compared to DL and work well with smaller data- sets. Limitations: • Feature engineering requirement: ML models depend on high-quality fea- ture engineering, which is labor-in- tensive and requires domain exper- tise. • Performance on unstructured data: ML models are less effective when dealing with unstructured data such as images or free-text notes without extensive preprocessing. DEEP LEARNING IN HEALTHCARE DL, a subset of ML, utilizes neural net- works with multiple layers to automati- cally learn complex patterns from large datasets. Unlike traditional ML, DL can perform end-to-end learning, meaning it can automatically learn the features from raw data, eliminating the need for manual feature extraction. 2 Neural network architectures: • Convolutional neural networks (CNNs): Commonly used for analyz- ing images and visual data (e.g., ra- diology, dermatology). • Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks: Suitable for sequential data such as time series and natural lan- guage processing. 3 • Generative adversarial networks (GANs): Used for generating synthet- ic medical data and enhancing image quality. Applications in healthcare: • Medical imaging: Automatic detec- tion of abnormalities (e.g., lung nod- ules, breast cancer, retinal diseases) from X-rays, CT scans, and MRIs us- ing CNNs. • Genomics and bioinformatics: Iden- tifying genetic variants and under- standing gene expression patterns using deep learning models. • Natural language processing (NLP): Analyzing unstructured clinical notes and research articles to extract rele- vant medical information (Rahman et al., 2024). • Drug discovery: Predicting drug-tar- get interactions and optimizing drug design using DL models trained on chemical structure data. Advantages: • Handling complex and unstructured data: DL models excel at processing and learning from unstructured data, such as medical images, free text, and audio recordings. • Automatic feature extraction: DL models can automatically identify