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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.

No MeSH data available.


Control of confounding factors in event trajectories and disease trajectories.(A) The adjusted risks from the event trajectories on the x-axis and those of the disease trajectories on the y-axis correlate with the outcomes in the trajectories when controlling confounders. (B) The change in intermediate events and extraneous variables on the x-axis and the change in adjusted risk on the y-axis. The outcomes in the two sets remain the same when the intermediate events and extraneous variables change.
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f5: Control of confounding factors in event trajectories and disease trajectories.(A) The adjusted risks from the event trajectories on the x-axis and those of the disease trajectories on the y-axis correlate with the outcomes in the trajectories when controlling confounders. (B) The change in intermediate events and extraneous variables on the x-axis and the change in adjusted risk on the y-axis. The outcomes in the two sets remain the same when the intermediate events and extraneous variables change.

Mentions: The risks adjusted by control of confounders for moving between events, symptoms and diseases are clearly desirable from a clinical perspective because they may disclose novel pathology. The two sets of trajectories, - disease trajectories and event trajectories - describe similar outcomes but with different extraneous variables. Figure 5A shows that the adjusted risks in the disease and event trajectories correlate (R2 = 0.89), while Fig. 5B shows that the variance between the disease trajectories and the event trajectories remains low (R2 = 0.11) with varying extraneous variables in the two trajectory sets.


Analysis of free text in electronic health records for identification of cancer patient trajectories
Control of confounding factors in event trajectories and disease trajectories.(A) The adjusted risks from the event trajectories on the x-axis and those of the disease trajectories on the y-axis correlate with the outcomes in the trajectories when controlling confounders. (B) The change in intermediate events and extraneous variables on the x-axis and the change in adjusted risk on the y-axis. The outcomes in the two sets remain the same when the intermediate events and extraneous variables change.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5: Control of confounding factors in event trajectories and disease trajectories.(A) The adjusted risks from the event trajectories on the x-axis and those of the disease trajectories on the y-axis correlate with the outcomes in the trajectories when controlling confounders. (B) The change in intermediate events and extraneous variables on the x-axis and the change in adjusted risk on the y-axis. The outcomes in the two sets remain the same when the intermediate events and extraneous variables change.
Mentions: The risks adjusted by control of confounders for moving between events, symptoms and diseases are clearly desirable from a clinical perspective because they may disclose novel pathology. The two sets of trajectories, - disease trajectories and event trajectories - describe similar outcomes but with different extraneous variables. Figure 5A shows that the adjusted risks in the disease and event trajectories correlate (R2 = 0.89), while Fig. 5B shows that the variance between the disease trajectories and the event trajectories remains low (R2 = 0.11) with varying extraneous variables in the two trajectory sets.

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.