Prediction test on small texts

.gitignore

 gptKey
+.venv
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api/pubmedApi.py

@@ -71,6 +71,8 @@ for key in obj.keys():
 
                     print(meshTerms)
 
+                    input("Press Enter to continue...")
+
                     if i > 20:
                         break
                 

model/modelTest.py

+import model.facebookBartLargeMnli as facebookBartLargeMnli
+
+data = [
+    {
+        "title": "Journal of managed care & specialty pharmacy",
+        "articleTitle": "Area deprivation index impact on type 2 diabetes outcomes in a regional health plan.",
+        "abstract": "Significant differences were identified between ADI quintiles 1 and 5 for noninsulin diabetes medication adherence, frequency of A1c test claims, all-cause health care service utilization, and total cost of care. There were no statistically significant differences between ADI quintiles for achievement of A1c goal or receipt of comorbidity-focused therapies."
+    },
+    {
+        "title": "Sleep & breathing = Schlaf & Atmung",
+        "articleTitle": "The association between chemosensitivity and the 10-year risk of type 2 diabetes in male patients with obstructive sleep apnea.",
+        "abstract": "Higher peripheral chemosensitivity was associated with an increased 10-year T2D risk, as calculated using a risk calculator based on clinical variables. For outcomes that reflect a moderate-to-high 10-year risk of T2D, the severity of OSA did not significantly affect the risk, irrespective of whether patients exhibited relatively low or high chemosensitivity."
+    },
+    {
+        "title": "Sleep & breathing = Schlaf & Atmung",
+        "articleTitle": "The effect of physical activity on sleep quality in people with diabetes: systematic review and meta-analysis.",
+        "abstract": "Preliminary evidence suggests that exercise can be prescribed to manage self-reported sleep quality in this population, although its effects may not surpass those of usual care."
+    },
+    {
+        "title": "Frontiers in endocrinology",
+        "articleTitle": "Predicting hypoglycemia in elderly inpatients with type 2 diabetes: the ADOCHBIU model.",
+        "abstract": "ChiCTR2200062277. Registered on 31 July 2022."
+    },
+    {
+        "title": "Diabetes & metabolism journal",
+        "articleTitle": "Cardiovascular Disease & Diabetes Statistics in Korea: Nationwide Data 2010 to 2019.",
+        "abstract": "The incidence of most CVD (IHD, ischemic stroke, and PAD) decreased between 2010 and 2019, whereas the incidence of HF increased. The overall use of high-intensity statins, SGLT2i, and GLP-1RA remained low among individuals with T2DM and CVD."
+    }
+]
+
+for obj in data:
+    title = obj["title"]
+    articleTitle = obj["articleTitle"]
+    abstract = obj["abstract"]
+    print(f"Title: {title}")
+    print(f"ArticleTitle: {articleTitle}")
+    print(f"Abstract: {abstract}")
+
+    results = facebookBartLargeMnli.classify(title + " \n " + articleTitle + " \n " + abstract)
+
+    print(f"Labels: {results["labels"]}")
+    print(f"Scores: {results["scores"]}")
+    print()
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model/test_results.md

+# Tests
+
+## facebookBartLargeMnli
+
+### Small texts
+
+I wanted to test the acuracy of the predictions of "facebookBartLargeMnli" on small texts to see if the lenght of the content has influence on the accuracy of the prediction. 
+
+I selected 5 entries from Pubmed with the mesh terms "Diabetes Mellitus" that where not to long. After that i predicted the corelation between the keywords ("Diabetes", "Cancer", "Chronic respiratory disease", "Cardiovascular diseases", "Mental Health", "Diabetes type 1", "Diabetes type 2") and the selected entries. I got the followin reasults:
+
+For an entrie that have:
+- Title: Frontiers in endocrinology
+- Abstract title: Predicting hypoglycemia in elderly inpatients with type 2 diabetes: the ADOCHBIU model.
+- Abstract : ChiCTR2200062277. Registered on 31 July 2022.
+- Results: 'Diabetes type 2' -> 0.7203449606895447 (~ 72%)
+
+Results for the 5 entries:
+![5 entries prediction](../docImg/smallTextPredictions.png)
+
+I saw that the lenght of the text didn't really have an impact on the acuracy of the prediction.
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