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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.


Reconstruction of patient events ahead in time.(A) The positive predictive value (PPV) (events explained, %) by trajectories compared with randomized trajectories of the same size. (B) The PPV in terms of the number of events known for a patient. (C) Positive predictions, % in terms of the event sequence number in the trajectories. (D) PPV in terms of event probability.
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f3: Reconstruction of patient events ahead in time.(A) The positive predictive value (PPV) (events explained, %) by trajectories compared with randomized trajectories of the same size. (B) The PPV in terms of the number of events known for a patient. (C) Positive predictions, % in terms of the event sequence number in the trajectories. (D) PPV in terms of event probability.

Mentions: For our patients, we have data from the first encounter and an average of 63 months (~5.3 years) ahead with an average of 142.6 events. Figure 3A demonstrates that our trajectories could predict upcoming events in our external patient data set with a positive predictive value (PPV) of 80%. In comparison, randomized trajectories, where sequence of events and length has been randomized (control group), have a PPV of 19.4%.


Analysis of free text in electronic health records for identification of cancer patient trajectories
Reconstruction of patient events ahead in time.(A) The positive predictive value (PPV) (events explained, %) by trajectories compared with randomized trajectories of the same size. (B) The PPV in terms of the number of events known for a patient. (C) Positive predictions, % in terms of the event sequence number in the trajectories. (D) PPV in terms of event probability.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: Reconstruction of patient events ahead in time.(A) The positive predictive value (PPV) (events explained, %) by trajectories compared with randomized trajectories of the same size. (B) The PPV in terms of the number of events known for a patient. (C) Positive predictions, % in terms of the event sequence number in the trajectories. (D) PPV in terms of event probability.
Mentions: For our patients, we have data from the first encounter and an average of 63 months (~5.3 years) ahead with an average of 142.6 events. Figure 3A demonstrates that our trajectories could predict upcoming events in our external patient data set with a positive predictive value (PPV) of 80%. In comparison, randomized trajectories, where sequence of events and length has been randomized (control group), have a PPV of 19.4%.

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.