HJBR Nov/Dec 2024

52 NOV / DEC 2024 I  HEALTHCARE JOURNAL OF BATON ROUGE MEDICAID patterns and features, reducing the need for domain-specific feature en- gineering. Limitations: • Data requirements: DL models re- quire large amounts of labeled data for training, which can be challenging to obtain in healthcare. • Computational complexity: DL is computationally intensive, requiring powerful hardware such as GPUs. • Black box nature: Deep neural net- works lack transparency, making it difficult to interpret how decisions are made, which can limit clinical adoption. 2 CHALLENGES IN USING ML AND DL IN HEALTHCARE • Data privacy and security: Handling sensitive health information requires adherence to strict privacy regula- tions such as HIPAA and GDPR. En- suring data security during training and model deployment is a major concern. • Data availability and quality: Health- care data is often fragmented, incom- plete, or noisy, making it challenging to train reliable models. • Generalizability: ML and DL models may not generalize well across differ- ent healthcare settings due to varia- tions in patient populations, clinical practices, and data collection meth- ods. 1 • Clinical adoption: Clinicians may be hesitant to adopt AI tools due to concerns about reliability, interpret- ability, and integration into existing workflows. 3 FUTURE DIRECTIONS The integration of machine learning and deep learning in healthcare will continue to expand as new techniques are devel- oped and data availability improves. The combination of ML and DL approaches, known as “hybrid models,” is a promising area that leverages the strengths of both methods to achieve higher accuracy and reliability. Additionally, ongoing research in “explainable AI” aims to improve the interpretability of deep learning models, making themmore suitable for clinical de- cision support. 1 CONCLUSION ML and DL are powerful tools in health- care, each with its own strengths and lim- itations. ML is well-suited for structured data and applications that require inter- pretability, while DL is ideal for complex tasks involving large datasets and unstruc- tured data. Understanding the distinctions between these two approaches is essen- tial for selecting the appropriate method for specific healthcare applications. As AI continues to evolve, the combined use of ML and DL techniques will play a pivotal role in advancing precision medicine and improving patient care. n REFERENCES 1 Rahman, A.; Debnath, T.; Kundu, D.; Khan, S.I.; et al. “Machine learning and deep learning-based approach in smart healthcare: Recent advanc- es, applications, challenges, and opportunities.” AIMS Public Health 11, issue 1 (Jan. 5, 2024): 58- 109. DOI: 10.3934/publichealth.2024004 2 Chakraborty, C.; Bhattacharya, M.; Pal, S.; Lee, S.S. “From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare.” Current Re- search in Biotechnology 7 (2024). https://doi. org/10.1016/j.crbiot.2023.100164 3 DeCicco, D.; Krupica, T.M.; Pellegrino, R.; Dim- achkie, Z.O. “Hospital-Wide Intervention in Billing and Coding to Capture Complexity of Care at an Academic Referral Center.” Journal of Healthcare Management 67, No. 6 (November/December 2022), 416-424. DOI: 10.1097/JHM-D-21-00213 Machine Learning vs. Deep Learning CRITERIA MACHINE LEARNING DEEP LEARNING Data Requirement Effective with small to medium-sized datasets. Requires large volumes of data for optimal performance. Feature Engineering Manual feature extraction and selection are often necessary. Automatically learns features from raw data. Model Interpretability High interpretability in models like decision trees and logistic regression. Often considered a "black box," making it challenging to interpret. Computational Power Relatively low computational power required. High computational power required, often necessitating specialized hardware (e.g., GPUs). Performance on Structured Data Performs well on structured and tabular data (e.g., patient records, clinical variables). May be overkill for structured data analysis. Performance on Unstructured Data Requires extensive preprocessing for unstructured data (e.g., images, text). Excels at processing unstructured data without extensive preprocessing (e.g., medical imaging, speech, and text). Suitability for Real-Time Use More suitable for real-time applications due to lower computational requirements. Can be used in real time but may require optimized infrastructure and hardware.