Epidemiology of Intractable Diseases in Japan

Epidemiology of diseases of unknown etiology, Specified as "Intractable Diseases"

Background: Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention. Methods: We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs. Results: We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital’s HAI incidence from 1.31% to 0.58%. Conclusions: AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management. 

Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review

Aplastic anaemia (AA) is a rare and life-threatening haematologic disorder characterised by pancytopenia and bone marrow failure. Its occurrence during pregnancy is exceedingly rare, posing significant risks and management challenges for both the mother and the foetus. We present here the case of a 23-year-old female, six months pregnant, diagnosed with severe aplastic anaemia (AA), aiming to highlight the diagnostic challenges and management considerations of AA in pregnancy. Our case underscores the critical nature of considering aplastic anaemia in differential diagnosis for pregnant patients presenting with unexplained pancytopenia. Based on that, we performed a comprehensive literature review of the past 20 years of papers published in the English language identified through searches in PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), Embase and the Cochrane Library, to provide an in-depth analysis of the current understanding of AA in pregnancy. We emphasise the necessity for cautious yet thorough investigation in such cases to avoid complications in both maternal and foetal health, focusing attention on the need for further research into safe and effective treatment protocols for managing AA in pregnancy, given the complexities introduced by the condition and its treatment on pregnancy outcomes.

Aplastic Anaemia in Pregnancy: A Case-Based Comprehensive Review of the Literature

The epidemiology of aplastic anemia in thailand

Epidemiological aspects of aplastic anemia.

BACKGROUND/AIM
There are few studies on artificial intelligence-based prediction models for colon cancer built using clinicopathological factors. Here, we aimed to perform a preliminary evaluation of a novel artificial intelligence-based prediction model for surgical site infection (SSI) in patients with stage II-III colon cancer.


PATIENTS AND METHODS
The medical records of 730 patients who underwent radical surgery for stage II-III colon cancer between 2000 and 2018 at our institute were retrospectively analyzed. Kaplan-Meier curves were used to examine the association between SSI and oncological outcomes (recurrence-free survival time). Next, we used the machine learning software Prediction One to predict SSI. Receiver-operating characteristic curve analysis was used to evaluate the accuracy of the artificial intelligence model.


RESULTS
The prognosis in terms of recurrence-free survival time was poor in patients with SSI (p=0.005, 95% confidence interval=4892.061-5525.251). The area under the curve of the artificial intelligence model in predicting SSI was 0.731.


CONCLUSION
As SSI is an important prognostic factor associated with oncological outcomes, the prediction of SSI occurrence is important. Based on our preliminary evaluation, the artificial intelligence model for predicting SSI in patients with stage II-III colon cancer was as accurate as the previously reported model derived through conventional statistical analysis.

Preliminary Evaluation of a Novel Artificial Intelligence-based Prediction Model for Surgical Site Infection in Colon Cancer.

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Epidemiological aspects of aplastic anemia.

Preliminary Evaluation of a Novel Artificial Intelligence-based Prediction Model for Surgical Site Infection in Colon Cancer.