Characteristics of Inpatient Fever

Transcription

1 2016 International Conference on Computational Science and Computational Intelligence Characteristics of Inpatient Fever A Case in Teaching Hospital Prof. SuFeng Tseng NCCU MIS Taipei, TW (ROC) sftseng@nccu.edu.tw Frank H NCCU MIS Taipei, TW (ROC) @nccu.edu.tw Abstract over a 3-year period, fever is always the most frequent icd9 code in inpatient health records including admission and discharge notes, and over 25% accounted for the top 6 frequent ICD9 codes. In this study, we illustrated the fever-related diseases and comorbidity. Given that % of fever-related ICD9 codes appear within 14 times, the association rules performed for fever-related comorbidity still managed to improve care with % coverage rate by proper CPOE. Keywords- ICD9CM; inpatient admission; fever; comorbidity I. PRELUDE The 780.6, fever, is the most frequent ICD9 code in all of the inpatient admission and discharge notes from 2007 to 2009 in TMUH. It owns 6446 entries in 3212 inpatient (ex. one 2008/07/14 admission even got 6 fever codes) over entries in all of the inpatients, and it is times higher than the second popular, unspecified leiomyoma of uterus. Various kinds of inflammation and pathogeny can be blamed for fever. Therefore, in order to deal with this major suffering of inpatient, we need to clarify and predict its etiology with comorbidity so that we may help with Computerized Physician Order Entry (CPOE) [1]. TMU in Taiwan R.O.C. between January 1, 2007, and December 31, B. Study Data Set Every inpatient that is in the health records got 5 icd9 code slots in admission note and 8 icd9 code slots in discharge note. The entered slots per admission are varied from 2 to 10 codes with an average of codes. Fever is always the top popular code each year from 2007 to 2009: 2199 times within 1097 hospitalizations in 2007, 2151 times within 1068 hospitalizations in 2008, and 2096 times within 1047 hospitalizations in II. METHODS A. Data and Variables The Taipei medical university hospital (TMUH, ) is a health care facility in Taiwan that was founded in We received information on all inpatient health records with personally identifying information (PII) encrypted provided by the Unified Informatics Office in C. Comorbidity 1) Fever-Related ICD9 Analysis In most cases, three-digit ICD-9 codes identify diagnostic categories (will be mentioned as icd9_p), and they should be used only if there is no four-digit code (ICD-9 refers to these as diagnostic subcategories ) within the category [2]. Above those digits, there are lists of codes for International Statistical Classification of Diseases and Related Health Problems [3], so ICD-9 code offers three-tiered hierarchy which allows us to do the plotting, decision tree, and rule induction based on layers of granularities. We will specify the diagnostic classes of diagnostic categories by serial numbers (will be mentioned as cat_id) of following table /16 $ IEEE DOI /CSCI

2 Bubble charts which are swelling according to entry counts illustrate the hot codes of icd9 or icd9_p lined horizontally within their own diagnostic classes. The most popular icd9 is in the range of 550~600, the second is in 450~500, and the third is in 750~800, just consistent with aforementioned word cloud. But when it comes to icd9_p some small bubbles are merged becoming larger making 466, *ACUTE BRONCHITIS AND BRONCHIOLITIS, becomes the third larger instead and top bubble is still in the cat_id 11, diseases of the genitourinary system. When it comes to diagnostic classes, the accumulated counts of cat_id 9 (diseases of the respiratory system) and cat_id 17 (symptoms, signs, and ill-defined conditions) outnumber the cat_id 11 becomes the top 2. 2) Association Rules Recommendation and Validation Based on the association of icd9 codes related to fever within one patient, we can suggest that one fever-related icd9 code by another. By divided the three year inpatient health records into different train/test ratio, we can know better how good the efficacy it can perform based on the characteristics of inpatient fever. III. RESULTS A. Fever-Related ICD9 By feeding all of the fever patients icd9 codes without fever, top 100 popular icd9 codes word cloud within fever are generated: 807 entries in URINARY TRACT INFECTION (SITE NOT SPECIFIED), 636 in PNEUMONIA (ORGANISM UNSPECIFIED), 314 in ABDOMINAL PAIN, 192 in OTHER AND UNSPECIFIED NONINFECTIOUS GASTROENTERITIS AND COLITIS, and 180 in BRONCHOPNEUMONIA (ORGANISM UNSPECIFIED). By trimming the icd9 digits after the dot, generate top 100 popular diagnostic categories word cloud within fever: 843 entries in *OTHER DISORDERS OF URETHRA AND URINARY TRACT, 636 in PNEUMONIA (ORGANISM UNSPECIFIED), 486 in *ACUTE BRONCHITIS AND BRONCHIOLITIS, 400 in *OTHER SYMPTOMS INVOLVING ABDOMEN AND PELVIS, and 320 in *SEPTICAEMIA. 129

3 B. Distribution of Fever-Related ICD9 This study sample consisted of inpatient admissions (16785 in 2007, in 2008, and in 2009). By pooling their admission and discharge ICD9 entries together, we are managed to realize the distributions of the frequency of icd9 and icd9_p codes are both very long tail. Most of the icd9 codes happened within 28 times (90.213% of icd9 and % of icd9_p), and the highest amounts are in the 2 occurrence which include % of fever-related icd9, and % of fever-related icd9_p. In the finest granularity, the top 10 frequent icd9 codes accounted % of hospitalizations entries in 3 years: 9.115% in URINARY TRACT INFECTION (SITE NOT SPECIFIED), 7.183% in PNEUMONIA (ORGANISM UNSPECIFIED), 3.546% in ABDOMINAL PAIN, 2.169% in OTHER AND UNSPECIFIED NONINFECTIOUS GASTROENTERITIS AND COLITIS, 2.033% in BRONCHOPNEUMONIA (ORGANISM UNSPECIFIED), 2.01% in ACUTE TONSILLITIS, 1.954% in ACUTE BRONCHITIS, 1.807% in *SEPTICAEMIA, 1.785% in UNSPECIFIED SEPTICAEMIA, and 1.717% in VOLUME DEPLETION. Second, the top ten icd9_p codes covered % of entries: 9.196% in *OTHER DISORDERS OF URETHRA AND URINARY TRACT, 6.938% in PNEUMONIA (ORGANISM UNSPECIFIED), 5.302% in *ACUTE BRONCHITIS AND BRONCHIOLITIS, 4.363% in *OTHER SYMPTOMS INVOLVING ABDOMEN AND PELVIS, 3.491% in *SEPTICAEMIA, 3.109% in *GENERAL SYMPTOMS, 2.705% in *INFECTIONS OF KIDNEY, 2.662% in *DIABETES MELLITUS, 2.596% in *DISORDERS OF FLUID (ELECTROLYTE AND ACID- BASE BALANCE), and 2.596% in *SYMPTOMS INVOLVING RESPIRATORY SYSTEM AND OTHER CHEST SYMPTOMS. In top granularity, the top 10 diagnostic classes covered % of entries: % in diseases of the respiratory system, 16.93% in symptoms & signs & ill-defined conditions, % in diseases of the genitourinary system, 9.818% in diseases of the digestive system, 9.392% in neoplasms, 5.629% in endocrine & nutritional and metabolic diseases & immunity disorders, 5.607% in infectious and parasitic diseases, 3.458% in diseases of the circulatory system, 2.618% in diseases of the skin and subcutaneous tissue, and 1.44% in diseases of the musculoskeletal system and connective tissue. A list of ranges is given to discretize icd9 entries counts for machine learning. When the machine learning is performed with their related diagnostic category and classes, the distribution of inpatient icd9 entries over 3-year study 130

4 period was divided into 10 groups whose spectrums are quite homogeneous expect one: 3 groups with 1 range, 5 groups with 2 ranges, 1 groups with 3 ranges, and 1 group (49 icd ) with 559 ranges. In rule induction (RIPPER, Cohen 1995) [4], the modeling including 211 rules can predict % entries count ranges (402 out of 583 training examples) with 0.9 pureness, 0.25 minimal prune benefit, and 9/1 train/test ratio. *DISORDERS OF FLUID (ELECTROLYTE AND ACID- BASE BALANCE) for 276, *OTHER DISORDERS OF URETHRA AND URINARY TRACT for 599, *ACUTE BRONCHITIS AND BRONCHIOLITIS for 466, ACUTE PHARYNGITIS for 462, *INFECTIONS OF KIDNEY for 590, TRACHEA (BRONCHUS AND LUNG CARCINOMA) for 162, and ACUTE TONSILLITIS for 463. C. Association Rules for Another Fever-Related Comorbidity 1) Training Phase The training periods of splits which are for one another fever-related icd9 or icd9_p are 30, 33, 35, and 36 months from January, Throughout these periods, the top ten hot relating icd9 codes which are ranked by the entries including several other icd9 codes of comorbidity they are relating are consistent: PNEUMONIA (ORGANISM UNSPECIFIED) for 486, STREPTOCOCCAL SEPTICAEMIA for 038.0, URINARY TRACT INFECTION (SITE NOT SPECIFIED) for 599.0, UNSPECIFIED SEPTICAEMIA for 038.9, *DIABETES MELLITUS WITHOUT MENTION OF COMPLICATION for 250.0, VOLUME DEPLETION for 276.5, ACUTE BRONCHITIS for 466.0, ACUTE PHARYNGITIS for 462, ACUTE TONSILLITIS for 463, and BRONCHUS AND LUNG (UNSPECIFIED CARCINOMA) for Likewise, the consistency appears in top 10 of icd9_p and the order only changed once from 30m to 33m: *SEPTICAEMIA for 038, PNEUMONIA (ORGANISM UNSPECIFIED) for 486, *DIABETES MELLITUS for 250, The association rule algorithms which were performed with 1 support and 1% confidence threshold show the increments of rules according to more samples are increasing (from 492 to 766), and the max combination set which means the inpatient hospitalization got the most codes is 7 icd9 (icd9_p) codes in the period of 2007~2009. While the training period gets longer, the longest list of one feverrelated code appears in the left-hand side (LHS) to infer is still increasing in the aspect of icd9 but shrinks and be stabilized in icd9_p. 131

5 2) Testing Phase While test the trained rules by enlarging the train/test ratios, the tendencies of correctness percentage (coverage rate) which means the rate of successfully inducting another code by having one code on the rules LHS and tendencies of admissions helped are strictly increasing, and the icd9_p rules always performed better than icd9 rules. If we trained through the whole admissions in 2007~2009 and then test the last 1 or 6 months, we can get ~94.231% of entries coverage by icd9 rules with 100% admission helped, but only 86~88.462% coverage by icd9_p rules. entries counts per code are quite long tail that 80% of feverrelated icd9 codes are within 14 entries and 80% feverrelated diagnostic categories within 26 entries. With the efforts of decision tree and rule induction, we can predict the range of accumulated icd9 entries count based on the value of code, its diagnostic category, and its diagnostic class. The top ten hot related icd9 codes and diagnostic categories are always consistent in the comorbidity of fever. While recommending the icd9 codes and diagnostic categories based on association rules with 1 support, the max lengths of those lists are strictly increasing (from 36 to 47) over time in recommendation of icd9, but shrink and then be stabilized in recommendation of diagnostic category which can be accounted for larger granularity with 1% confidence threshold. While increasing the training period, the coverage rates of recommendation in admission entries of split test sample are strictly increasing, but the diagnostic category recommendation always got higher coverage than icd9 recommendation with the same train/test ratio. However, if we perform the fully trained recommendation against split test sample, the coverage rate of icd9 performs better than of diagnostic category. It might because of the modeling of icd9 is not yet over fitting but the modeling of diagnostic category may blame 1% confidence threshold for information loss because of the hottest related diagnostic category (*SEPTICAEMIA) got 108~140 related entries. Thus, if we keep the model trained within 35 month, the recommendation of diagnostic category might get % coverage rate in the hospitalization entries of the following month, and the spared part of icd9 can be simply assisted by automatic completion of Computerized Physician Order Entry (CPOE). IV. DISCUSSION This study showed that over the 2007~2009 period, fever is always the most frequent icd9 code accounted for hospitalizations while one admission got codes in average. The top 2 common codes related to fever are URINARY TRACT INFECTION (SITE NOT SPECIFIED) for 9.115% and then the PNEUMONIA (ORGANISM UNSPECIFIED) for 7.183%, and top 2 frequent feverrelated diagnostic categories are *OTHER DISORDERS OF URETHRA AND URINARY TRACT for 9.196% and PNEUMONIA (ORGANISM UNSPECIFIED) for 6.938%. Followed, the top 2 frequent diagnostic classes are diseases of the respiratory system for % and symptoms & signs & ill-defined conditions for 16.93%. The distributions of REFERENCES [1] Frank H. (2014). A Smart Medication Recommendation Model for The Electronic Prescription, Taipei City, Computer Methods and Programs in Biomedicine, 117(2), Nov [2] Kent J. Moore. (2000). ICD-9 Coding: Every Digit Counts, Washington, DC, Fam Pract Manag Oct;7(9):16. [3] Elkin P, Brown S: Automated enhancement of description logicdefined terminologies to facilitate mapping to ICD9-CM. J Biomed Inform. 2002, 35 (5-6): /S (03) [4] William W. Cohen and Yoram Singer. A simple, fast, and effective rule learner. In Proceedings of the Sixteenth National Conference on Artificial Intelligence,