Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept.
Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept.
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DOI：
10.1111/bdi.12507
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发表时间：
2017-06
影响因子：
5.4
通讯作者：
Strakowski SM
中科院分区：
文献类型：
--
作者：
Fleck DE;Ernest N;Adler CM;Cohen K;Eliassen JC;Norris M;Komoroski RA;Chu WJ;Welge JA;Blom TJ;DelBello MP;Strakowski SM
Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading Genetic Fuzzy Tree (GFT) design called the LITHium Intelligent Agent (LITHIA). Using multiple objectively defined fMRI and proton MRS (1H-MRS) inputs, we tested whether LITHIA could accurately predict lithium response in participants with first-episode bipolar mania. We identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over eight weeks and both fMRI and 1H-MRS scans at baseline pre-treatment. We trained LITHIA using 18 1H-MRS and 90 fMRI inputs over four training runs to classify treatment response and predict symptom reductions. Each training run contained a randomly selected 80% of the total sample and was followed by a 20% validation run. Over a different randomly selected distribution of the sample, we then compared LITHIA to eight common classification methods. LITHIA demonstrated nearly perfect classification accuracy and was able to predict post-treatment symptom reductions at 8 weeks with at least 88% accuracy in training and 80% accuracy in validation. Moreover, LITHIA exceeded the predictive capacity of the eight comparator methods and showed little tendency towards overfitting. Results provide proof-of-concept that a novel GFT is capable of providing control to a multidimensional bioinformatics problem – namely prediction of lithium response – in a pilot data set. Future work on this, and similar machine learning systems, could help assign psychiatric treatments more efficiently, thereby optimizing outcomes and limiting unnecessary treatment.
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