Prediction of viral pneumonia based on machine learning models analyzing pulmonary inflammation index scores.

OBJECT: To obtain Pulmonary Inflammation Index scores from imaging chest CT and combine it with clinical correlates of viral pneumonia to predict the risk and severity of viral pneumonia using a computer learning model. METHODS: All patients with suspected viral pneumonia on CT examination admitted to The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University from December 2022 to March 2023 were retrospectively selected. The respiratory viruses were monitored by RT-PCR and categorized into patients with viral pneumonia and those with non-viral pneumonia. The extent of lung inflammation was quantified according to the Pulmonary Inflammation Index score (PII). Information on patient demographics, comorbidities, laboratory tests, pathogenetic testing, and radiological data were collected. Five machine learning models containing Random Forest(RF), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), K Nearest Neighbour Algorithm (KNN), and Kernel Ridge Regression (KRR) were used to predict the risk of onset and severity of viral pneumonia based on the clinically relevant factors or PII. RESULTS: Among the five models, the SVM model performed best in ACC (76.75 %), SN (73.99 %), and F1 (72.42 %) and achieved a better area under the receiver operating characteristic curve (ROC) (0.8409) when predicting the risk of developing viral pneumonia. RF had the best overall classification accuracy in predicting the severity of viral pneumonia, especially in predicting pneumonia with a PII classification of grade I, the RF model achieved an accuracy of 98.89%. CONCLUSION: Machine learning models are valuable in assessing the risk of viral pneumonia. Meanwhile, machine learning models confirm the importance in predicting the severity of viral pneumonia through PII. The establishment of machine learning models for predicting the risk and severity of viral pneumonia promotes the further development of machine learning in the medical field.

[Evaluation of low intensity Nd: YAG laser and traditional drugs in the treatment of myofascial pain].

PURPOSE: To evaluate the efficacy of low intensity Nd: YAG laser and traditional drugs in the treatment of myofascial pain (MP). METHODS: Eighty patients with MP were divided into laser group(n=40) and traditional medicine group(n=40) according to the principle of randomization and double-blindness. The patients in the laser group were treated with low intensity Nd :YAG laser(1 064 nm, 8 J/cm2, 250 mW) , with an interval of 48 h between the two laser treatments. The whole course of treatment was 10 times. Patients in the traditional medicine group uesd celecoxib capsules, 1 capsulet each time(0.2 g), twice a day for 2 weeks. Before and after each treatment, mouth opening, protrusion excursion, lateral movement of the affected side and lateral movement of the contralateral side were measured, and pain visual analogue scores (VAS) were measured and recorded. The data were statistically analyzed with SPSS 22.0 software package. RESULTS: Patients in laser group had significantly improved mandibular function and movement status(P＜0.05) and pain symptoms(P＜0.05); patients in traditional medicine group had the same significant improvement on mandibular functional movement status(P＜0.05) and pain symptoms (P＜0.05). The total effective rate of the two groups had no significant difference(P＞0.05). The VAS score of patients in laser group was lower than that of traditional medicine group, but the difference was not significant(P＞0.05). CONCLUSIONS: Low intensity Nd: YAG laser and traditional medicine can effectively relieve the symptoms of myofascial pain and improve mandibular function and movement. Laser treatment has the advantages of short course of treatment, high efficiency, no pain and fewer side effects, which is worthy of clinical application.