Visual analysis for Type 2 Diabetes Mellitus —based on electronic medical records Xi Meng Jijiang Yang

Outline Introduction Related research Research Method Description Results and Discussion Conclusion

Introduction Result & Discussion ReviewConclusion Research Method T2DM: emerged as a major global health problem Embedded in a very complex group of genetic and epigenetic systems There is no research to verify the noteworthy clinical symptoms which are involved in T2DM MDS is employed to analyze the the relationships among various kinds of T2DM symptoms The research of T2DM : Development and Impact

Related Research Introduction Result & Discussion ReviewConclusion Research Method Ways to explore the main symptoms of T2DM Surveys and interviews with clinical professionals Filtering information from popular medical websites Electronic medical records analysis

Related Research Introduction Result & Discussion ReviewConclusion Research Method Electronic medical records analysis Medical record is a legal document provides a chronicle of a patient’s medical history and care. Medical record, refers to health record or medical chart, is used interchangeably to describe the systematic documentation of a single patient’s medical history and care across time within one particular health care provider's jurisdiction. Medical record contains variety of types of "notes" entered over time by health care professionals, recording observations and administration of drugs and therapies, orders for the administration of drugs and therapies, test results, x-rays, reports, etc.

Related Research Introduction Result & Discussion ReviewConclusion Research Method Electronic medical records analysis Medical term: Medical terms makes the medical records easy to read and find information. And the terms used in medical records are medical vocabulary and Terminology,and can represent the salient attributions of the disease.

Literature Review Introduction Result & Discussion ReviewConclusion Research Method Electronic medical records analysis Medical records analysis can reveal first-hand and real-world patients’ information. It enables researchers to better understand patients ‘detailed symptoms and reflect the complexity of undergoing symptoms.

Literature Review Introduction Result & Discussion ReviewConclusion Research Method Visualization method Using visualization method to reveal the hidden relationships among data from the medical records is not the new endeavor Visualization method has become a complex systematical project that covers various kinds of data processing methods.

Literature Review Introduction Result & Discussion ReviewConclusion Research Method Visualization method In this study, the co-word analysis with MDS and clustering techniques is selected as the data processing tool to analyze the main symptoms of T2DM.

Introduction Result & Discussion ReviewConclusion Research Method Co-word analysis Co-word analysis is a powerful method in discovering relationships among research objects and revealing hidden connections that may be not obvious. The hypothesis of co- word analysis are: (1) Two entries appear in the same document also illustrates the correlations of these two themes ; (2) The co-occurrence, which aims to obtain hierarchical clustering based on similarity measure, is consistent with the common research subjects, objectives, and interests..

Introduction Result & Discussion ReviewConclusion Research Method Multidimensional Scaling (MDS) What is MDS?  MDS refers to a broad class of exploratory data tools that display the structure of high dimensional distance-like data as a geometrical picture. What can MDS do ?  it can handle various types of data  Ordinal  interval  ratio-level

Introduction Result & Discussion ReviewConclusion Research Method Clustering techniques What is clustering technique ?  Generally speaking, information visualization methods can be used for object/Subject clustering analysis. What can it do ? The visual presentation can clearly illustrate object relationship in a two or three-dimensional space. It helps people to observe multiple perspectives of relationships among objects in a vivid way

Introduction Result & Discussion ReviewConclusion Research Method The goals of this study are : What are the main symptoms related to T2DM, especially hidden ones? What are the relationships between the symptoms? Better understand how usually patients descript their T2DM symptoms. Help young and new doctors to understand some easily ignored T2DM causes and provide them some references. Offer useful information for T2DM researchers. The findings may expand their research thinking and horizon. primary aim of this study is to dig the hidden symptoms of T2DM, illustrate the relationships and shed light on the understanding of T2DM.

Introduction Result & Discussion ReviewConclusion Research Method 200 medical records from patients who had T2DM over a 1-year period dated from had been collected for the experiment. All patients’names were dismissed in this research. The medical record was assigned a unique diagnosed code. A. Data Source

Introduction Result & Discussion ReviewConclusion Research Method Terms are extracted from the main body of medical records. All terms are filtered and synonyms, plurals and abbreviations of raw terms are normalized. The frequency of each term was tallied. B. Term Extraction

Introduction Result & Discussion ReviewConclusion Research Method C. Term-Term Proximity Matrix The term master file can be converted to a term-document matrix as shown in equation (1).

Introduction Result & Discussion ReviewConclusion Research Method C. Term-Term Proximity Matrix The generated term-term matrix, which provides the proximity between each two terms, is defined as follow. The proximity between two terms is the result of dimension reduction of the highly dimensional space term-term relationship defined by similarity measure.

Introduction Result & Discussion ReviewConclusion Research Method D. MDS Analysis Based on a proximity matrix derived from variables measured on objects as input entity, these distances are mapped on a lower dimensional (typically two or three dimensional) spatial representation. There are two indicators in MDS analysis to show the quality of results: the Stress Value, and the squared correlation index (RSQ).

Introduction Result & Discussion ReviewConclusion Research Method Results & Discussion A. Term Frequency Analysis The frequencies of the 30 terms which were filtered from medical records were ranked in descending order and kept in the master file （ Table1 ）. Based on the 30 terms, a 30*30 term-term occurrence matrix was produced.

Introduction Result & Discussion ReviewConclusion Research Method TermsFrequencyTermsFrequency Weight Loss59 Lower Extremity Weakness 45 Inappetency57Dry Mouth45 Hyperlipidemia57 Cerebral Insufficiency 44 Polydipsia57 Parents-Of- Hypertension 44 Family Diabetes55 Reflux Esophagitis 44 Urorrhagia55Polyphagia4 Intermittent Dizziness 54 Chronic Cholecystitis4 Feeble53Fatty Liver3 Smoking52 Cerebral Embolism3

Introduction Result & Discussion ReviewConclusion Research Method TermsFrequencyTermsFrequency Limbs Tingling51 Coronary Heart Disease3 Coolness Of Extremities 39Fractura2323 Foam Urine39Osteoarthrosis2323 High Blood Pressure 39 Frequent Urination at Night2 Blurring Of Vision 36Osteoporosis2 Drink25Hyperhidrosis2 Palpitate25

Introduction Result & Discussion ReviewConclusion Research Method Results & discussion B. Subject Term Clustering Figure 1 shows MDS analysis of the 30 terms and Table 2 shows the terms that every cluster included. In order to display the inner structure of the causes, every cluster showed in figure1 was coded from C1 to C9.

Introduction Result & Discussion ReviewConclusion Research Method

Introduction Result & Discussion ReviewConclusion Research Method ClustersTerms C1 t12: Foam in Urine) t16: Intermittent Dizziness t22: Hyperlipemia t26: Feeble t29: Diuresis C2 t27: Polydipsia t28: Polyphagia t24: Parents-Of-Hypertension C3 t3: Frequent Urination at Night t7: Weight Loss t30: Hidrosis C4 t1: Fatty Liver t11: Inappetency t6: Smoke t17: Family Diabetes

Introduction Result & Discussion ReviewConclusion Research Method ClustersTerms C5 t19: Osteoporosis t21: Osteoarthrosis C6 t2: Drink t4: Palpitate t25: Reflux Esophagitis C7 t13: Cerebral Embolism t18: Coronary Heart Disease C8 t14: Cerebral insufficiency t15: Dry Mouth t31: Chronic Cholecystitis C9 t5: Lower Extremity Weakness t8: Limbs Tingling t9: Coolness of Extremities t10: Blurring of Vision t23: High Blood Pressure

Conclusion Through visualization analysis of electronic medical records from clinical, some long-term complications of T2DM take the form of clustering. The information visualization method like MDS is a powerful technique to mine the internal relationship which is hidden in the medical records. Future research will aim to expand the data scale and add new analysis topic to diabetes domain, hoping to provide more valuable analysis result.

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