Comparación de dos tipos de clasificación en dispepsia funcional; distrés postprandial o dolor epigástrico vs. un análisis multidimensional por conglomerados utilizando aprendizaje no supervisado

Los métodos de inteligencia artificial utilizando herramientas de aprendizaje no supervisado pueden apoyar la resolución de problemas al establecer patrones de agrupación o clasificación no identificados, que permiten tipificar subgrupos para manejos más individualizados. Existen pocos estudios que permiten conocer la influencia de síntomas digestivos y extradigestivos en la tipificación dispepsia funcional; esta investigación realizó un análisis de aprendizaje no supervisado por conglomerados basándose en dichos síntomas, para discriminar subtipos de dispepsia y comparar con una de las clasificaciones actualmente más aceptadas. Se realizó un análisis exploratorio de conglomerados en adultos con dispepsia funcional según síntomas digestivos, extradigestivos y emocionales. Se conformaron patrones de agrupación de tal manera que dentro de cada grupo existiera homogeneidad en cuanto a los valores adoptados por cada variable. El método de análisis de conglomerados fue bietápico y los resultados del patrón de clasificación se compararon con una de las clasificaciones más aceptadas de dispepsia funcional. De 184 casos, 157 cumplieron con criterios de inclusión. El análisis de conglomerados excluyó 34 casos no clasificables. Los pacientes con dispepsia de tipo 1 (conglomerado uno), presentaron mejoría al tratamiento en el 100% de los casos, solo una minoría presentaron síntomas depresivos. Los pacientes con dispepsia de tipo 2 (conglomerado dos) presentaron una mayor probabilidad de falla al tratamiento con inhibidor de bomba de protones, padecieron con mayor frecuencia trastornos de sueño, ansiedad, depresión, fibromialgia, limitaciones físicas o dolor crónico de naturaleza no digestiva. Esta clasificación de dispepsia por análisis de clúster establece una visión más holística de la dispepsia en la cual características extradigestivas, síntomas afectivos, presencia o no de trastornos de sueño y de dolor crónico permiten discriminar el comportamiento y respuesta al manejo de primera línea.

Artificial intelligence methods using unsupervised learning tools can support problem solving by establishing unidentified grouping or classification patterns that allow typing subgroups for more individualized management. There are few studies that allow us to know the influence of digestive and extra-digestive symptoms in the classification of functional dyspepsia. This research carried out a cluster unsupervised learning analysis based on these symptoms to discriminate subtypes of dyspepsia and compare with one of the currently most accepted classifications. An exploratory cluster analysis was carried out in adults with functional dyspepsia according to digestive, extra-digestive and emotional symptoms. Grouping patterns were formed in such a way that within each group there was homogeneity in terms of the values adopted by each variable. The cluster analysis method was two-stage and the results of the classification pattern were compared with one of the most accepted classifications of functional dyspepsia. Of 184 cases, 157 met the inclusion criteria. The cluster analysis excluded 34 unclassifiable cases. Patients with type 1 dyspepsia (cluster one) presented improvement after treatment in 100% of cases, only a minority presented depressive symptoms. Patients with type 2 dyspepsia (cluster two) presented a higher probability of failure to treatment with proton pump inhibitor, suffered more frequently from sleep disorders, anxiety, depression, fibromyalgia, physical limitations or chronic pain of a non-digestive nature. This classification of dyspepsia by cluster analysis establishes a more holistic vision of dyspepsia in which extradigestive characteristics, affective symptoms, presence or absence of sleep disorders and chronic pain allow discriminating behavior and response to first-line management.

Research advances in machine learning models for acute pancreatitis / 临床肝胆病杂志

Acute pancreatitis （AP） is a gastrointestinal disease that requires early intervention， and when it progresses to moderate-severe AP （MSAP） or severe AP （SAP）， there will be a significant increase in the mortality rate of patients. Machine learning （ML） has achieved great success in the early prediction of AP using clinical data with the help of its powerful computational and learning capabilities. This article reviews the research advances in ML in predicting the severity， complications， and death of AP， so as to provide a theoretical basis and new insights for clinical diagnosis and treatment of AP through artificial intelligence.

Thinking of the current status and application of machine learning in the field of laboratory medicine / 中华检验医学杂志

In recent years, machine learning has become a hot spot in various research fields. Using machine learning can realize the transformation from data driven to knowledge discovery, which is an important development direction of laboratory intelligence in the future. The application of machine learning in laboratory medicine has shown great potential, but t it also has many challenges and difficulties. The direction of our joint efforts is to promote the clinical transformation of machine learning technology, realize the practicality and industrialization in medical laboratories, and achieve the goal of assisting clinical decision-making as soon as possible.

Application of Deep Learning System into the Development of Communication Device for Quadriplegic Patient / 대한신경손상학회지

OBJECTIVE: In general, quadriplegic patients use their voices to call the caregiver. However, severe quadriplegic patients are in a state of tracheostomy, and cannot generate a voice. These patients require other communication tools to call caregivers. Recently, monitoring of eye status using artificial intelligence (AI) has been widely used in various fields. We made eye status monitoring system using deep learning, and developed a communication system for quadriplegic patients can call the caregiver. METHODS: The communication system consists of 3 programs. The first program was developed for automatic capturing of eye images from the face using a webcam. It continuously captured and stored 15 eye images per second. Secondly, the captured eye images were evaluated for open or closed status by deep learning, which is a type of AI. Google TensorFlow was used as a machine learning tool or library for convolutional neural network. A total of 18,000 images were used to train deep learning system. Finally, the program was developed to utter a sound when the left eye was closed for 3 seconds. RESULTS: The test accuracy of eye status was 98.7%. In practice, when the quadriplegic patient looked at the webcam and closed his left eye for 3 seconds, the sound for calling a caregiver was generated. CONCLUSION: Our eye status detection software using AI is very accurate, and the calling system for the quadriplegic patient was satisfactory.