Some serious medical conditions, such as sepsis, might be more easily predicted thanks to artificial intelligence (AI). Sepsis, which results from a massive immune response to bacterial infection can often lead to organ failure and death. Mary Beth Moore, an AI and language analytics strategist at SAS, wrote about this and other serious medical conditions and how AI can help: "Early diagnosis and rapid intervention is critical in sepsis treatment, but symptoms aren't always apparent for its early onset stages. Mortality rates increase 8% for every hour treatment is delayed. With heavy caseloads and possibly asymptomatic patients in the early stages of sepsis, the human eye may not notice the correlation between data in medical records and early indicators of a deadly condition. But the application of natural language processing to data in those electronic health records is a key input for predictive models that trigger alert systems, notifying doctors and nurses that a patient may need medical intervention."
Amazon Web Services is extending its Comprehend natural language processing service to medical records. At AWS' re: Invent conference, the company outlined Amazon Comprehend Medical, an extension of Comprehend for medical customers. The importance of the service is that it is another toward applying artificial intelligence and machine learning to healthcare. The ability to automate medical record reading and x-ray and MRI analysis could save time for patients as well as physicians. Comprehend can model topics, detect language, conduct sentiment analysis and extract phrases.
In the healthcare industry, natural language processing has many potential applications. NLP can enhance the completeness and accuracy of electronic health records by translating free text into standardized data. It can fill data warehouses and semantic data lakes with meaningful information accessed by free-text query interfaces. It may be able to make documentation requirements easier by allowing providers to dictate their notes, or generate tailored educational materials for patients ready for discharge.
ECRI and the Institute for Safe Medication Practices PSO know that there were thousands of patient safety events reported in 2021 that will never get reviewed. The patient safety organization is one of about 96 across the country and collects data on mistakes that resulted in patient harm and near misses. This year, member hospitals sent ECRI more than 800,000 of these reports, according to director Sheila Rossi. Federal agencies and PSOs are only able to gain insights from a fraction of events reported every year. Not having the capacity to sift through all the reports has consequences, though it's not required by law.
Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using ICD-10. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. Further efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.