Using Bayesian Networks for Cleansing Trauma Data

Transcription

1 Uing Bayeian Network for Cleaning Trauma Data Prahant J. Dohi Dept. of Computer Siene Univ of Illinoi, Chiago, IL Lloyd G. Greenwald Dept. of Computer Siene Drexel Univ, Philadelphia, PA John R. Clarke Dept. of Surgery Drexel Univ, Philadelphia, PA Abtrat Medial data i unique due to it large volume, heterogeneity and omplexity. Thi neeitate otly ative partiipation of medial domain expert in the tak of leaning medial data. In thi paper we preent a new data leaning approah that utilize Bayeian network to orret errant attribute value. Bayeian network apture expert domain knowledge a well a the unertainty inherent in the leaning proe, both of whih exiting leaning tool fail to model. Auray i improved by utilizing ontextual information in orreting errant value. Our approah operate in onjuntion with model of poible error type that we have identified through our leaning ativitie. We evaluate our approah and apply our method to orreting intane of thee error type. Introdution The ativity of leaning a raw data et involve deteting errant value, orreting the deteted error and optionally, filling in miing value. Data leaning i viewed a a ritial pre-proeing tep to data mining (Fayyad, Piatetky- Shapiro, & Smyth 1996) and i performed either eparately or in onjuntion with other data mining ativitie. Inuffiient data entry hek in legay ytem, diparate oure of data, and exigeny in entering the data are ome of the aue of error in medial data et. Current leaning method hinge on a manual inpetion, by hand, of the data to detet and orret errant value. The manual proe of data leaning i laboriou, time onuming, and prone to error thu neeitating automated method of leaning. A generi framework of automated data leaning inlude the definition and determination of error type, the identifiation of error intane, and the orretion of the diovered error intane (Maleti & Maru 1999). Medial data i unique due to it large volume, heterogeneity and omplexity (Cio & Moore 2002) making the tak of leaning a medial databae unuually diffiult. The omplexity of medial yntax and emanti neeitate ative and otly partiipation of a medial domain expert in the data leaning effort, uing exiting method. We preent a new approah to data leaning uing Bayeian network (BN). Thi approah both improve the auray of data leaning and redue the ot. Auray Copyright 2003, Amerian Aoiation for Artifiial Intelligene ( All right reerved. i improved by deigning Bayeian network that apture the ontext of an errant value to diambiguate the error. Context may be ued, for example, to detet and orret error intane in whih a valid but inorret value ha been entered for an attribute. For example, a typographial error may aue entry of intead of 87.0 a the diagnoti ICD-9 ode for a patient with relaping fever. Inpetion of other related attribute of a patient reord i the only way to identify and orret thi la of error. Cot i redued by building automated data leaning filter from Bayeian network that are partially deigned by induing model from data. Uing upervied learning to partially indue model from data redue the need for otly ative medial domain expert partiipation. Bayeian network may be ued to produe probability ditribution over orret value. Thee ditribution an apture and retain the unertainty inherent in data leaning. Furthermore, we may ue thee network to generate the mot likely value for miing attribute value. Bayeian network repreent a mature tehnology that i flexible enough to be applied to mot domain that require leaning. Our approah operate in onjuntion with model of poible error type that we have identified through initial leaning ativitie. Thee error type inlude both medial-domain-peifi error type, uh a medial ode ubtitution, and non-domain-peifi error type, uh a typographial error. Many of our model an be applied to other domain with minimal hange, thu repreenting andidate generi data leaning tool. Thi paper i trutured to preent firt the medial data in the next etion, then to diu general error detetion tehnique and error type. Our ontext-driven probabiliti approah to leaning i given next followed by it evaluation. We then onlude thi paper and outline future work. Undertanding the Medial Data A part of a long-term projet to improve emergeny enter trauma management for patient with multiple lifethreatening injurie and peifially, better undertand the effet of time delay on patient outome (Clarke et al. 2002), we obtained a large file of patient data. The data file onit of medial reord on 169,512 patient admitted diretly to trauma enter in Pennylvania between 1986 and 1999 and regitered in the Pennylvania Trauma Sy- 72 FLAIRS 2003

2 tem Foundation Regitry. Eah patient reord i ompoed of 412 attribute of information. Thee attribute an be ategorized in the following manner: Patient General Information Thee attribute pertain to the intitution number, trauma number allotted to the patient, general demographi data, bakground information on the are provided to the patient at the ene of injury, and patient inurane information. Patient Clinial Data Thee attribute relate to patient vital ign uh a pule, repiratory rate, motor repone, glagow oma ore, and revied trauma ore. Thee vital ign are multiply reorded at the ene of injury, during tranportation to the trauma enter, and at the arrival of the patient in the trauma enter. Drug reening information i alo ontained in thee field. Proe of Aute Care Thi i the larget ategory of attribute in a patient reord. They relate to patient ompliation in the emergeny department (ED), diagnoi of injury, treatment of the patient inluding non-operative and operative proedure, and final patient outome Timetamp Thi inlude the date and time of variou event that our during the proe of are of the patient. Thi inlude the date and time of injury of the patient, ene are dipath, ene are arrival, ene are leave, arrival of the patient at the ED, arrival of variou medial peronnel in the ED, and patient diharge from the ED. We obtained thi data a a ingle flat file of 69 million data point. From thi file, we ontruted a databae of relational table that houe ditint ategorie of information. We were then able to utilize data query language and other ytem tool to manipulate the data during the proe of leaning. After trimming the data bae to patient and field meeting the requirement for our planned tudie we have a databae with over 68 million data point panning 166,768 patient reord. Our firt tak wa to lean thi data, inluding etimating miing value. Data Cleaning Preliminarie Data entry and aquiition i inherently prone to error. Though muh effort i expended in implementing variou data entry hek, mot real world data et uffer from errant value (Redman 1998) neeitating the ue of leaning tehnique. The pre-proeing tep of data leaning i ritial to ahieve tandard of data quality and avoid ompromiing tudie that may be performed on the data et. Many data leaning effort to date (e.g. (Lee et al. 1999; Hernández & Stolfo 1995)) have onentrated on the problem of deteting and removing dupliate reord during integration of data from multiple oure. Our data leaning effort onentrate on deteting and orreting data value from a ingle oure. Kimball, R. (Kimball 1996) break down the proe of leaning into the ix tep of elementizing, tandardizing, verifying, mathing, houeholding, and doumenting. Elementizing break down the reord into it atomi unit for the ubequent tep. Standardizing onvert the element into a generi form. Verifying mathe the data againt known lit uh a ode, produt lit et. Mathing map the orret value to the exiting value in the data. Bookkeeping tak are arried out during houeholding and doumenting. Our leaning effort inorporate eah of thee ix tep. Speifially, tandardizing and verifying are ombined into a ingle tep followed by mathing and doumenting. Current leaning tool (Rahm & Do 2000) are deigned to aid the manual proe of identifying and orreting error. Suh tool ue tatitial method and ubjetive analyi to both detet and orret error. Thi often entail ubtitution of the errant value with a ingle likely orret value omputed through tatitial tehnique uh a average and variane analyi. In addition to ignoring the unertainty preent in etimating the orret value, thee tool are alo inapable of diretly apturing expert domain knowledge in arriving at their deiion. Thee limitation motivate our invetigation of probabiliti model uh a Bayeian network that not only apture and model the unertainty, but alo permit aimilation of the knowledge of the domain expert. Rather than uggeting a ingle orret value, a BN generate a probability ditribution over all poible orret value. The probability ditribution repreent the unertainty impliit in the etimation of likely orret value. Either the entire ditribution or the mot likely value that ha the highet probability may then be eleted a the replaement for the errant value. In the ubetion below we outline the proe of deteting error in our trauma data et, followed by an enumeration of the different error type that were diovered during thi proe. Error Detetion Error detetion at the reord-level typially utilize tatitial outlier detetion method. Thee method are olletively referred to a data profiling. Suh method ompute for eah attribute the length, value range, variane, and direte value for that attribute, a well a the frequeny and uniquene of eah value. Ourrene of null value and miing value for attribute are alo noted. Domain-peifi aoiation rule (e.g. hopital tay = exit date - entry date) are alo mined and teted for potential errant value. Additionally, we developed partial ordering ontraint for deteting anomalie in temporal attribute. Thee partial ordering amongt the temporal attribute are detailed in (Dohi 2002). Attribute value that violate the partial ordering ontraint are iolated for potential orretion. Data leaning effort an benefit from any upplemental doumentation that aompanie the raw data, inluding the data ditionary. Suh literature, in addition to providing inight into the individual attribute emanti, erve a an invaluable referene oure for verifiation. Claifiation of Error in Trauma Data During the proe of data leaning, medial domain expert are invaluable in deiphering medial jargon that i inherent in typial medial data et, a well a eluidating relationhip between attribute. Converely, a large proportion of medial data ontain non-domain peifi entrie that do not require the otly ative partiipation of medial FLAIRS

3 expert. To ahieve thee ot aving, we have laified error mehanim into two lae: thoe that require medial knowledge and thoe that do not. Non-domain peifi error The error preented in thi group are not unique to medial data et and may our in data in other domain a well. Common data entry error: Thee inlude typographial error uh a miing deimal point in numeri and alphanumeri value, kipped harater and number, and preene of wayward peial harater. Quantitative tranformation error: Attribute requiring value in a partiular ytem of unit may frequently be error trap. For example, the body temp attribute require body temperature in o C. If the temperature i meaured in o F it mut be manually onverted to o C before entry. Thi type of tranformation proe i a oure of numerou error. Domain-peifi error The error in thi group manifet due to the medial yntax and emanti. Code ubtitution: Thee involve errant entry of invalid a well a other valid but inorret ode in plae of the orret medial ode. Temporal anomalie: Timetamp value preent in temporal attribute may violate partial ordering ontraint that are peifi to the domain. Additionally, the time interval derived from the timetamp may be above or below predefined limit. Bayeian Network for Data Cleaning Our general approah to error orretion for non-temporal data i to apture the et of attribute that an be ued to diambiguate an error uing a graphial probabiliti network, alo known a a Bayeian network (Pearl 1988). A BN i a DAG in whih node repreent random variable and link between the node repreent aual or tatitial dependenie. Bayeian network provide a graphial and intuitive method to apture the relationhip between attribute in a tak or domain. Attribute are repreented with random variable, providing a repreentation for any unertainty in attribute value. Domain knowledge i aptured in the emanti truture of the network that illutrate the aual relationhip amongt the attribute of the domain. Bayeian network provide a ompat repreentation by taking advantage of onditional independene and loal truture, and permit effiient omputation of joint probability ditribution. In the ubetion below, we introdue the generi BN at the ore of our error orretion method, followed by the proedure that wa employed for the orretion. Error Corretion Model Figure 1 depit the generi BN that wa utilized during our approah. The random variable Corret Value of Attribute apture the prior probability ditribution over the range of orret value of the attribute. Error Mehanim repreent the different type of error that may have ourred. The random variable Entered Value of Attribute take Corret Value of Attribute n n Entered Value of Attribute Error Mehanim Figure 1: The Bayeian Error Corretion Model. on the potentially errant value that have been entered into the databae for the attribute. Definition 1 Let be the attribute to be leaned. Let { i i=1...n} and { i i=1...n} be the et of attribute that have a aual and a ymptomati relation repetively, with. The ontext of i the et { j i i=1...n, j= or }. The ontext repreent domain-peifi information hardoded in the network. For eah attribute, whoe errant value are to be orreted or miing value to be filled in, the ontext i identified uing expert medial knowledge. Definition 2 Let be the attribute to be leaned. Let C be the ontext identified for. A BN of the form given in Fig 1 ontaining C i alled an intantiated BN for. The proe of entering value (evidene) for the ontextual random variable in an intantiated BN i termed a ontext hek for the attribute. A ontext hek, in addition to validating our aumption of the orret value, alo at a a trong induer of the orret value. Method of Corretion Errant and miing value may be ubtituted with the mot likely value, or all poible value, or a likely range of value for that attribute. In our error orretion we replae the errant value with either the mot likely value or a probability ditribution over all poible value of the attribute. Our approah to orreting deteted errant value for ome attribute involve a two-tep proedure. The firt tep i to intantiate a BN for the attribute, followed by iolating from the databae, the hunk of errant data pertaining to the attribute that ompoe the BN. The iolated hunk of data i randomly partitioned into a training et and a tet (holdout) et of equal ardinalitie. The training et i firt leaned manually by inpeting eah patient reord, peifially eah errant value of the attribute i mapped to it likely orret value. The BN i trained uing thi et during whih the onditional probability table are populated with the appropriate probabilitie. The training algorithm utilized a Bayeian MAP approah with Dirihlet funtion a prior for learning the onditional probabilitie. Auming the prior ditribution to be Dirihlet generally doe not reult in a ignifiant lo of auray, ine preie prior are not uually available, and Dirihlet funtion an fit a wide variety of imple funtion. The trained BN may now erve a an automated data leaning filter. It i applied to the tet et during whih the errant and ontextual 74 FLAIRS 2003

4 information within eah reord i entered into the appropriate node of the BN and a probability ditribution over the orret value i inferred. Either the mot likely value or the entire probability ditribution may be retained in the leaned data. The tradeoff involved in thi deiion are explored in (Dohi, Greenwald, & Clarke 2001). In intane where the ditribution amongt the et of orret value i uniform, the errant value i left unorreted in the data. An important advantage of our BN i it dual ue in orreting errant value a well a filling in miing value. Reliane of the leaning proe on the ontextual information permit predition of the orret value for miing value a well. A imilar methodology a before i ued to fill in miing value. A peial non-trivial la of error involve ubtitution of orret value with other valid but inorret value. The abundane and omplexity of medial ode failitate frequent ourrene of uh error in medial data et. One method of deteting and orreting uh error i a tediou manual inpetion of eah patient reord. Suh a method relie on expert domain knowledge oupled with ontextual information in inferring the mot likely orret value. Our BN by virtue of poeing both thee entitie repreent an error orretion model that i apable of orreting uh error. We give example of uh error orretion elewhere. Evaluation In thi etion we demontrate the appliation of our leaning approah to the domain peifi and non-domain peifi lae of error outlined previouly. Our approah to the firt three lae of error i to produe a probabiliti error model that relate the data field in quetion with other field in the data bae. Thi provide a ontext that may be ued, in onjuntion with a model of poible error mehanim, to ompute a probability ditribution over orret value for the data field in quetion. Our approah to the fourth la of error i to develop a et of ontraint that apture the partial ordering of all time and date field in a reord, and ue thee partial ordering ontraint to detet and orret errant temporal data field (Dohi 2002). In the next ubetion we give an example intantiated BN and tet it preditive ability. We applied our trained BN model to the data et that required leaning. In the final ubetion we how the predited orret value for the orreponding errant value. Performane One of the attribute that required leaning wa patient preexiting ondition. Figure 2 how the BN intantiated for the attribute. The random variable Error Set apture two aue of error: MiDePt (miing deimal point) and CodeSubt (ode ubtitution). The ontext onit of onene pule, on-ene ytoli blood preure, on-ene repiratory rate, and on-ene intubation. The ontinuou value of eah of thee random variable have been uitably diretized uing an entropy-baed upervied diretization tehnique. The variable Entered Errant Value and Corret Value repreent the errant value and all poible orret value repetively. Figure 2: Example Intantiated BN. Performane of the network wa aeed uing a 5- fold ro-validation tehnique. We iolated and manually leaned 78 reord ontaining errant value for the attribute. Our ro-validation tehnique enompaed 5 teting phae. In eah phae, the data wa randomly partitioned into 5 fold of whih 4 fold were utilized in training and the remaining fold wa ued for teting the BN. During teting, ontextual value a well a the errant value, if any, from the tet reord were entered and the mot likely orret value and error mehanim were predited. We oberved an average error rate of 20% (ie. mipredition on 3 ae) for prediting orret value and an error rate of 3.75% (ie. mipredition on 1 ae) for prediting the error mehanim. Predited Corret Value In Table 1 we peify the errant value followed by the mot likely orret value a predited by our intantiated BN model and relevant omment pertaining to the orretion. For lak of pae we how only a mall ampling of orretion and hide the ontext of the orreted attribute value. Row 1-4 of Table 1 diplay frequently ourring error in entry of medial ode uh a miplaement of deimal point, miing deimal point and ue of numeri harater in plae of deimal point. Mathematial tranformation uh a onverion of temperature from o F to o C beome oure of error when uh tranformation are arried out manually. Row 5-8 how orretion of errant patient body temperature value in the data. Inpetion of ontextual attribute uh a anatomi injury ore, burn data, and patient pre-exiting ondition reinfored the belief in the likely orret value. Errant entry of invalid or valid medial ode our frequently while reording medial data. In addition to abene of hek at the time of entry uh error are alo aued by the preponderane of medial ode that referene the ame FLAIRS

5 No. Value Replaed with Reaon for the ation 1 A0.6 A.06 Miplaed deimal pt. Context hek Miing deimal pt. Freq(3.31-Spinal Tap,33.10-Iniion of lung)=(356,1008) Supet that deimal pt replaed with 3, Freq(34.04-Chet tube)=9780. Context hek Supet that deimal pt replaed with 0, Freq(57.94-foley)= Context hek Preumed to be in F, onverted to C. Context hek Miing deimal point and rounding. Context hek * 5/9-32 = -12 C temp. wa further onverted. Context hek (980-32)* 5/9 = 527 Deimal pt. error. Context hek Miplaed deimal pt. Frequeny(87.03-CT head) = Context hek. 10 CHF A.03 Congetive Heart F ailure, aigned ode A.03. Context hek. 11 CHOL CHOL Could imply Choleterol or Choleytetory, both are valid preexiting ondition. 12 COPD L.03 Chroni Obtrutive Pulmonary Dieae, aigned ode L.03. Context hek medial ondition. Row 9-12 how the orretion of medial ode ubtitution error in whih the orret ode i ubtituted with either an invalid or valid ode in the data. Uing our error orretion model we performed approximately 1,100 orretion. We have maintained extenive doumentation on thee hange to make them tratable. The doumentation alo erve a a oure of referene for automating future leaning ativitie. Conluion and Future Work Data leaning i now reognized a a onequential preproeing tep in data mining. Abene of formal method of leaning, inuffiient doumentation of retropetive leaning ativitie, voluminou target data, and a lak of laifiation of andidate error make thi tak tediou and error-prone in itelf. Furthermore, the unertainty impliit in thi proe and it reliane on expert domain knowledge require development of model that are able to apture both thee qualitie. BN repreent an effiient andidate tehnology that meet thee requirement and provide a pragmati unambiguou approah to takling the tak. We preented a generi BN model that utilize the ontext of an attribute to infer a probability ditribution over the orret value of the attribute a well a mehanim of the error. The BN approah operate in onjuntion with a et of poible domain-peifi and non-domain peifi error mehanim that laify typial error in medial data et. The advantage of our approah lie in it intuitivene and ability to apture unertainty a well a domain knowledge to perform error orretion. Furthermore the BN alo addree error that involve one valid ode ubtitution with another. Diadvantage involve intantiation of a BN for eah attribute that mut be leaned, limitation of BN in modelling ontinuou-valued attribute (they mut firt be diretized), and poor reult with mall ize of training et. Many of thee tehnique were developed during a year-long effort to manually lean our target databae. It i our hope that the method we have enumerated here an help other effort to better automate thi proe. The leaned data et were employed in a ueful tatitial tudy (Clarke et al. 2002) that examined the effet of delay in the ED on patient outome for a ertain la of patient. Furthermore, we are teting our BN model on Table 1: Sample error and their orretion. variou other data et a well. Aknowledgement Thi reearh i ponored in part by a Drexel/MCP-Hahnemann Reearh Synergie award. Referene Cio, K., and Moore, G. W Uniquene of medial data mining. AI in Mediine 26(1-2):1 24. Clarke, J. R.; Trookin, S. Z.; Dohi, P. J.; Greenwald, L. G.; and Mode, C. J Time to laparotomy for intra-abdominal bleeding from trauma doe affet urvival for delay up to 90 minute. The Journal of Trauma: Injury, Infetion, and Critial Care 52(3): Dohi, P. J.; Greenwald, L.; and Clarke, J. R On retaining intermediate probabiliti model in data. In AAAI Fall Sympoium on Unertainty in AI, Dohi, P. J Effetive method for building probabiliti model from large noiy data et. Mater thei, Drexel Univerity, Philadelphia, PA Fayyad, U. M.; Piatetky-Shapiro, G.; and Smyth, P From data mining to knowledge diovery: An overview. Advane in Knowledge Diovery and Data Mining, MIT Pre/AAAI Pre Hernández, M. A., and Stolfo, S. J The merge/purge problem for large databae. In Carey, M. J., and Shneider, D. A., ed., Proeeding of the 1995 ACM SIGMOD International Conferene on Management of Data, Kimball, R Dealing with dirty data. Databae and Management Sytem 9(10):55+. Lee, M.; Lu, H.; Ling, T.; and Ko, Y Cleaning data for mining and warehouing. In Proeeding of the 10th Intl Conf on Databae and Expert Sytem, Maleti, J., and Maru, A Progre rpt on automated data leaning. Teh Rpt CS-99-02, U of Memphi. Pearl, J Probabiliti Reaoning in Intelligent Sytem: Network of Plauible Inferene. Lo Alto, California: Morgan-Kaufmann. Rahm, E., and Do, H. H Data leaning: Problem and urrent approahe. IEEE Data Engg Bulletin 23(4). Redman, T The impat of poor data quality on the typial enterprie. CACM 41(2): FLAIRS 2003