TY - GEN
T1 - Effective Patient Similarity Computation for Clinical Decision Support Using Time Series and Static Data
AU - Masud, Mohammad M.
AU - Hayawi, Kadhim
AU - Samuel Mathew, Sujith
AU - Dirir, Ahmed
AU - Cheratta, Muhsin
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/2/4
Y1 - 2020/2/4
N2 - This paper presents a technique for computing patient similarity using time series data effectively combined with static data. Time series data of inpatients, such as heart rate, blood pressure, Oxygen saturation, respiration are measured at regular intervals, especially for inpatients in intensive care unit (ICU). The static data are mainly patient background and demographic data, including age, weight, height and gender. The similarity computation is done in unsupervised way. It is therefore free from data labeling requirement. However, such patient similarity can be very useful in developing various clinical decision support systems including treatment, medication, hospital admission and diagnosis. Our proposed technique works in three main steps. First, patient similarity is computed for each individual time series. Second, patients are grouped by clustering the static data. Finally, similarities from individual time series are combined and effectively blended with the patient group information to create a nearest neighborhood model. This model consists of a collection of the nearest neighbors for a given patient. We encounter several challenges for this task, including dealing with multi-variate time series data, variable sampling quantities and rates, missing values, and combining time-series with static data. We evaluate the proposed technique on a real patient database on two target features, namely, 'diagnosis' and 'admission type'. Notable performance is recorded for both targets, achieving f1-score as high as 0.8. We believe this technique can effectively combine different types of clinical data and develop an efficient unsupervised framework for computing patient similarity to be utilized for clinical decision support systems.
AB - This paper presents a technique for computing patient similarity using time series data effectively combined with static data. Time series data of inpatients, such as heart rate, blood pressure, Oxygen saturation, respiration are measured at regular intervals, especially for inpatients in intensive care unit (ICU). The static data are mainly patient background and demographic data, including age, weight, height and gender. The similarity computation is done in unsupervised way. It is therefore free from data labeling requirement. However, such patient similarity can be very useful in developing various clinical decision support systems including treatment, medication, hospital admission and diagnosis. Our proposed technique works in three main steps. First, patient similarity is computed for each individual time series. Second, patients are grouped by clustering the static data. Finally, similarities from individual time series are combined and effectively blended with the patient group information to create a nearest neighborhood model. This model consists of a collection of the nearest neighbors for a given patient. We encounter several challenges for this task, including dealing with multi-variate time series data, variable sampling quantities and rates, missing values, and combining time-series with static data. We evaluate the proposed technique on a real patient database on two target features, namely, 'diagnosis' and 'admission type'. Notable performance is recorded for both targets, achieving f1-score as high as 0.8. We believe this technique can effectively combine different types of clinical data and develop an efficient unsupervised framework for computing patient similarity to be utilized for clinical decision support systems.
KW - dynamic time warping
KW - minhash
KW - patient similarity
KW - time series
KW - vital signs
UR - http://www.scopus.com/inward/record.url?scp=85079868872&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079868872&partnerID=8YFLogxK
U2 - 10.1145/3373017.3373050
DO - 10.1145/3373017.3373050
M3 - Conference contribution
AN - SCOPUS:85079868872
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the Australasian Computer Science Week Multiconference 2020, ACSW 2020
PB - Association for Computing Machinery
T2 - 2020 Australasian Computer Science Week Multiconference, ACSW 2020
Y2 - 3 February 2020 through 7 February 2020
ER -