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Analysis of free text in electronic health records for identification of cancer patient trajectories

View Article: PubMed Central - PubMed

ABSTRACT

With an aging patient population and increasing complexity in patient disease trajectories, physicians are often met with complex patient histories from which clinical decisions must be made. Due to the increasing rate of adverse events and hospitals facing financial penalties for readmission, there has never been a greater need to enforce evidence-led medical decision-making using available health care data. In the present work, we studied a cohort of 7,741 patients, of whom 4,080 were diagnosed with cancer, surgically treated at a University Hospital in the years 2004–2012. We have developed a methodology that allows disease trajectories of the cancer patients to be estimated from free text in electronic health records (EHRs). By using these disease trajectories, we predict 80% of patient events ahead in time. By control of confounders from 8326 quantified events, we identified 557 events that constitute high subsequent risks (risk > 20%), including six events for cancer and seven events for metastasis. We believe that the presented methodology and findings could be used to improve clinical decision support and personalize trajectories, thereby decreasing adverse events and optimizing cancer treatment.

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Construction of trajectories.(A) Progression of health state to multiple morbidities X and Y. (B) Variations in how patient information is registered yields a distorted information space observed by clinicians. (C) Frequent Item Set (FIS) mining identified observations that repeatedly appear together. (D) Trajectories are created using order of first appearance.
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f2: Construction of trajectories.(A) Progression of health state to multiple morbidities X and Y. (B) Variations in how patient information is registered yields a distorted information space observed by clinicians. (C) Frequent Item Set (FIS) mining identified observations that repeatedly appear together. (D) Trajectories are created using order of first appearance.

Mentions: The construction of the trajectories has been summarized in Methods and further illustrated in Fig. 2A–C. Thus, we identify two sets of sub trajectories within the cancer trajectory framework, while reserving data from a set of 1,000 random patients for later validation (referred to as our external validation set), i.e.:


Analysis of free text in electronic health records for identification of cancer patient trajectories
Construction of trajectories.(A) Progression of health state to multiple morbidities X and Y. (B) Variations in how patient information is registered yields a distorted information space observed by clinicians. (C) Frequent Item Set (FIS) mining identified observations that repeatedly appear together. (D) Trajectories are created using order of first appearance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5384191&req=5

f2: Construction of trajectories.(A) Progression of health state to multiple morbidities X and Y. (B) Variations in how patient information is registered yields a distorted information space observed by clinicians. (C) Frequent Item Set (FIS) mining identified observations that repeatedly appear together. (D) Trajectories are created using order of first appearance.
Mentions: The construction of the trajectories has been summarized in Methods and further illustrated in Fig. 2A–C. Thus, we identify two sets of sub trajectories within the cancer trajectory framework, while reserving data from a set of 1,000 random patients for later validation (referred to as our external validation set), i.e.:

View Article: PubMed Central - PubMed

ABSTRACT

With an aging patient population and increasing complexity in patient disease trajectories, physicians are often met with complex patient histories from which clinical decisions must be made. Due to the increasing rate of adverse events and hospitals facing financial penalties for readmission, there has never been a greater need to enforce evidence-led medical decision-making using available health care data. In the present work, we studied a cohort of 7,741 patients, of whom 4,080 were diagnosed with cancer, surgically treated at a University Hospital in the years 2004–2012. We have developed a methodology that allows disease trajectories of the cancer patients to be estimated from free text in electronic health records (EHRs). By using these disease trajectories, we predict 80% of patient events ahead in time. By control of confounders from 8326 quantified events, we identified 557 events that constitute high subsequent risks (risk > 20%), including six events for cancer and seven events for metastasis. We believe that the presented methodology and findings could be used to improve clinical decision support and personalize trajectories, thereby decreasing adverse events and optimizing cancer treatment.

No MeSH data available.


Related in: MedlinePlus